%% Genetic Programming Bibliography
%%$Revision: 1.2031 $ $Date: 2012/05/15 15:28:05 $
%%Created by W.B.Langdon cs.ucl.ac.nl January 1995
%%Based on J.Koza's GP bibliography of 14 March 1994
%% To add references to your papers see
%% ftp://ftp.cs.bham.ac.uk/pub/authors/W.B.Langdon/biblio/
@Article{tagkey1997126,
title = "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",
journal = "Computers \& Mathematics with Applications",
volume = "33",
number = "5",
pages = "126--127",
year = "1997",
ISSN = "0898-1221",
doi = "doi:10.1016/S0898-1221(97)00025-4",
URL = "http://www.sciencedirect.com/science/article/B6TYJ-3SNTGM2-D/2/23afe396341b39baf74fcd29db315b46",
key = "tagkey1997126",
notes = "No author given. Contents listing of
\cite{koza:gp96}",
}
@Article{tagkey1997129,
title = "Advances in genetic programming, volume 2 : Edited by
Peter Angeline and Kenneth Kinnear, Jr. {MIT} Press,
Cambridge, {MA}. (1996). 538 pages. \$50.00",
journal = "Computers \& Mathematics with Applications",
volume = "33",
number = "5",
pages = "129",
year = "1997",
ISSN = "0898-1221",
doi = "doi:10.1016/S0898-1221(97)82933-1",
URL = "http://www.sciencedirect.com/science/article/B6TYJ-3SNTGM2-T/2/4d3bcc2dda31e9aca679eba60ff95a3a",
key = "tagkey1997129",
size = "0.5 pages",
notes = "Contents listing of \cite{book:1996:aigp2}. No author
given. To get, try other articles on page 129",
}
@Article{tagkey1999291,
title = "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",
journal = "Computers \& Mathematics with Applications",
volume = "38",
number = "11-12",
pages = "291--291",
year = "1999",
ISSN = "0898-1221",
doi = "doi:10.1016/S0898-1221(99)91267-1",
URL = "http://www.sciencedirect.com/science/article/B6TYJ-48778B1-3H/2/1d6f4728f10e14a24f4f28189d15f818",
key = "tagkey1999291",
notes = "Contents listing of \cite{spector:1999:aigp3}. No
author given.",
}
@Article{tagkey1999132,
title = "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",
journal = "Computers \& Mathematics with Applications",
volume = "37",
number = "3",
pages = "132--132",
year = "1999",
ISSN = "0898-1221",
doi = "doi:10.1016/S0898-1221(99)90375-9",
doi = "doi:10.1016/S0898-1221(99)90239-0",
URL = "http://www.sciencedirect.com/science/article/B6TYJ-489YTT5-2T/2/13179f12104abafe66b36e402ef358d9",
key = "tagkey1999132",
notes = "Contents listing of \cite{langdon:book}. No author
given.",
}
@Article{tagkey1995115,
title = "Genetic programming {II}: Automatic discovery of
reusable programs : By John {R}. Koza. {MIT} Press,
Cambridge, {MA}. (1994). 746 pages. \$45.00",
journal = "Computers \& Mathematics with Applications",
volume = "29",
number = "3",
pages = "115--115",
year = "1995",
ISSN = "0898-1221",
doi = "doi:10.1016/0898-1221(95)90099-3",
URL = "http://www.sciencedirect.com/science/article/B6TYJ-48F4PJH-H/2/bd467ac24453cb0b3f9dbbf15075bedb",
key = "tagkey1995115",
notes = "Contents listing of \cite{koza:gp2}. No author
given.",
}
@Article{tagkey1999282,
title = "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",
journal = "Computers \& Mathematics with Applications",
volume = "38",
number = "11-12",
pages = "282--282",
year = "1999",
ISSN = "0898-1221",
doi = "doi:10.1016/S0898-1221(99)91189-6",
URL = "http://www.sciencedirect.com/science/article/B6TYJ-48778B1-24/2/ee28594e33abf3bd7c4a9fc997b98492",
key = "tagkey1999282",
}
@Article{tagkey2002475,
title = "Automated generation of robust error recovery logic in
assembly systems using genetic programming : Cem {M}.
Baydar, Kazuhiro Saitou, v20, n1, 2001, pp55-68",
journal = "Journal of Manufacturing Systems",
volume = "21",
number = "6",
pages = "475--476",
year = "2002",
ISSN = "0278-6125",
doi = "doi:10.1016/S0278-6125(02)80094-2",
URL = "http://www.sciencedirect.com/science/article/B6VJD-4920DSC-1N/2/93bf79c7eb0d6ad94d169ed1b37ec77f",
key = "tagkey2002475",
notes = "Abstract of \cite{Baydar200155}",
}
@InProceedings{Abarghouei:2009:SOCPAR,
author = "Amir Atapour Abarghouei and Afshin Ghanizadeh and
Saman Sinaie and Siti Mariyam Shamsuddin",
title = "A Survey of Pattern Recognition Applications in Cancer
Diagnosis",
booktitle = "International Conference of Soft Computing and Pattern
Recognition, SOCPAR '09",
year = "2009",
month = dec,
pages = "448--453",
keywords = "genetic algorithms, genetic programming, artificial
neural networks, cancer diagnosis, image processing,
medical images, pattern recognition applications,
wavelet analysis, cancer, medical image processing,
pattern recognition",
doi = "doi:10.1109/SoCPaR.2009.93",
abstract = "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.",
notes = "Also known as \cite{5368648}",
}
@InProceedings{Abbass:2002:WCCI,
publisher_address = "Piscataway, NJ, USA",
author = "H. Abbass and N. X. Hoai and R. I. (Bob) McKay",
booktitle = "Proceedings, 2002 World Congress on Computational
Intelligence",
doi = "doi:10.1109/CEC.2002.1004490",
notes = "Refereed International Conference Papers",
pages = "1654--1666",
publisher = "IEEE Press",
title = "Ant{TAG}: {A} New Method to Compose Computer Programs
Using Colonies of Ants",
URL = "http://sc.snu.ac.kr/PAPERS/TAGACOcec02.pdf",
volume = "2",
year = "2002",
keywords = "genetic algorithms, genetic programming",
size = "6 pages",
abstract = "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.",
}
@InProceedings{abbattista:1999:SAGAACS,
author = "Fabio Abbattista and Valeria Carofiglio and Mario
Koppen",
title = "Scout Algorithms and Genetic Algorithms: {A}
Comparative Study",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "769",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems, poster
papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-803.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-803.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{aicsu-91:abbot,
author = "R. J. Abbott",
title = "Niches as a {GA} divide-and-conquer strategy",
booktitle = "Proceedings of the Second Annual AI Symposium for the
California State University",
year = "1991",
editor = "Art Chapman and Leonard Myers",
pages = "133--136",
publisher = "California State University",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{abbott:2003:OOGP,
author = "Russell J. Abbott",
title = "Object-Oriented Genetic Programming, An Initial
Implementation",
booktitle = "Procceedings of the Sixth International Conference on
Computational Intelligence and Natural Computing",
year = "2003",
address = "Embassy Suites Hotel and Conference Center, Cary,
North Carolina USA",
month = sep # " 26-30",
keywords = "genetic algorithms, genetic programming,
object-oriented, STGP",
URL = "http://abbott.calstatela.edu/PapersAndTalks/OOGP.pdf",
size = "4 pages",
abstract = "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.",
notes = "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
\cite{HPL-2001-327}.",
}
@InProceedings{abbott:2003:MLMTA,
author = "Russ Abbott and Jiang Guo and Behzad Parviz",
title = "Guided Genetic Programming",
booktitle = "The 2003 International Conference on Machine Learning;
Models, Technologies and Applications (MLMTA'03)",
year = "2003",
address = "las Vegas",
month = "23-26 " # jun,
publisher = "CSREA Press",
keywords = "genetic algorithms, genetic programming, guided
genetic programming",
URL = "http://abbott.calstatela.edu/PapersAndTalks/Guided%20Genetic%20Programming.pdf",
size = "7 pages",
abstract = "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.",
notes = "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 \cite{langdon:book} \cite{HPL-2001-327}
\cite{ppsn92:oReilly} \cite{icga93:kinnear}",
}
@InProceedings{DBLP:conf/icai/AbbottPS04,
author = "Russ Abbott and Behzad Parviz and Chengyu Sun",
title = "Genetic Programming Reconsidered",
year = "2004",
pages = "1113--1116",
bibsource = "DBLP, http://dblp.uni-trier.de",
editor = "Hamid R. Arabnia and Youngsong Mun",
publisher = "CSREA Press",
ISBN = "1-932415-32-7",
booktitle = "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",
volume = "2",
address = "Las Vegas, Nevada, USA",
month = jun # " 21-24",
keywords = "genetic algorithms, genetic programming, evolutionary
pathway, fitness function, teleological evolution,
adaptive evolution",
URL = "http://abbott.calstatela.edu/PapersAndTalks/GeneticProgrammingReconsidered.pdf",
size = "4 pages",
abstract = "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.",
notes = "sort, fitness function cheat
",
}
@InProceedings{Abdelbar:aspgp03,
author = "Ashraf M. Abdelbar and Sherif Ragab and Sara Mitri",
title = "Applying Co-Evolutionary Particle Swam Optimization to
the Egyptian Board Game Seega",
booktitle = "Proceedings of The First Asian-Pacific Workshop on
Genetic Programming",
year = "2003",
editor = "Sung-Bae Cho and Nguyen Xuan Hoai and Yin Shan",
pages = "9--15",
address = "Rydges (lakeside) Hotel, Canberra, Australia",
month = "8 " # dec,
keywords = "genetic algorithms, genetic programming",
ISBN = "0-9751724-0-9",
notes = "\cite{aspgp03}",
}
@Article{Abdelmalek:2009:JAMDS,
title = "Selecting the Best Forecasting-Implied Volatility
Model Using Genetic Programming",
author = "Wafa Abdelmalek and Sana {Ben Hamida} and Fathi Abid",
journal = "Journal of Applied Mathematics and Decision Sciences",
year = "2009",
publisher = "Hindawi Publishing Corporation",
keywords = "genetic algorithms, genetic programming",
URL = "http://downloads.hindawi.com/journals/ads/2009/179230.pdf",
URL = "http://www.hindawi.com/journals/ads/2009/179230.html",
doi = "doi:10.1155/2009/179230",
ISSN = "11739126",
bibsource = "OAI-PMH server at www.doaj.org",
language = "eng",
oai = "oai:doaj-articles:b3bc3b339d2f713819080ff9b253312a",
abstract = "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.",
notes = "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",
}
@InProceedings{4983280,
author = "I. Abd Latiff and M. O. Tokhi",
title = "Fast convergence strategy for Particle Swarm
Optimization using spread factor",
booktitle = "Evolutionary Computation, 2009. CEC '09. IEEE Congress
on",
year = "2009",
month = may,
pages = "2693--2700",
keywords = "PSO velocity equation, fast convergence strategy,
inertia weight, particle swarm optimization, spread
factor, convergence, particle swarm optimisation",
doi = "doi:10.1109/CEC.2009.4983280",
notes = "Not on GP",
}
@Article{Abdou200911402,
author = "Hussein A. Abdou",
title = "Genetic programming for credit scoring: The case of
Egyptian public sector banks",
journal = "Expert Systems with Applications",
volume = "36",
number = "9",
pages = "11402--11417",
year = "2009",
ISSN = "0957-4174",
doi = "doi:10.1016/j.eswa.2009.01.076",
URL = "http://www.sciencedirect.com/science/article/B6V03-4VJSRWK-1/2/a3b8516f289c76c474c6a1eb9d26d7ec",
keywords = "genetic algorithms, genetic programming, Credit
scoring, Weight of evidence, Egyptian public sector
banks",
abstract = "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.",
}
@InProceedings{Abdul-Rahim:2006:ccis,
author = "A. B. {Abdul rahim} and J. Teo and A. Saudi",
title = "An Empirical Comparison of Code Size Limit in
Auto-Constructive Artificial Life",
booktitle = "2006 IEEE Conference on Cybernetics and Intelligent
Systems",
year = "2006",
pages = "1--6",
address = "Bangkok",
month = jun,
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming, Push, Breve,
ALife, PushGP",
ISBN = "1-4244-0023-6",
doi = "doi:10.1109/ICCIS.2006.252308",
abstract = "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",
notes = "Sch. of Eng. & Inf. Technol., Univ. Malaysia Sabah",
}
@InCollection{abernathy:2000:UGASBCRB,
author = "Neil Abernathy",
title = "Using a Genetic Algorithm to Select Beam
Configurations for Radiosurgery of the Brain",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2000",
year = "2000",
editor = "John R. Koza",
pages = "1--7",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms",
notes = "part of \cite{koza:2000:gagp}",
}
@InProceedings{abraham:2003:CEC,
author = "Ajith Abraham and Vitorino Ramos",
title = "Web Usage Mining Using Artificial Ant Colony
Clustering and Genetic Programming",
booktitle = "Proceedings of the 2003 Congress on Evolutionary
Computation CEC2003",
editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass
and Kay Chen Tan and Bob McKay and Daryl Essam and Tom
Gedeon",
pages = "1384--1391",
year = "2003",
publisher = "IEEE Press",
address = "Canberra",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "8-12 " # dec,
organisation = "IEEE Neural Network Council (NNC), Engineers Australia
(IEAust), Evolutionary Programming Society (EPS),
Institution of Electrical Engineers (IEE)",
keywords = "genetic algorithms, genetic programming, Web Usage
Mining, Ant Systems, Stigmergy, Data-Mining, Linear
Genetic Programming.",
ISBN = "0-7803-7804-0",
URL = "http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-CEC03b.pdf",
URL = "http://arxiv.org/abs/cs/0412071",
size = "8 pages",
abstract = "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.",
notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust,
the EPS, and the IEE.",
}
@TechReport{abraham:2004:0405046,
author = "Ajith Abraham and Ravi Jain",
title = "Soft Computing Models for Network Intrusion Detection
Systems",
institution = "OSU",
year = "2004",
month = "13 " # may # " 2004",
note = "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",
keywords = "genetic algorithms, genetic programming, Cryptography
and Security",
URL = "http://www.softcomputing.net/saman2.pdf",
URL = "http://arxiv.org/abs/cs/0405046",
abstract = "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.",
notes = "ACM-class: K.6.5 cs.CR/0405046",
size = "20 pages",
}
@Article{Abraham:2003:JIKM,
author = "Ajith Abraham",
title = "Business Intelligence from Web Usage Mining",
journal = "Journal of Information \& Knowledge Management",
year = "2003",
volume = "2",
number = "4",
pages = "375--390",
keywords = "genetic algorithms, genetic programming, Web mining,
knowledge discovery, business intelligence, hybrid soft
computing, neuro-fuzzy-genetic system",
URL = "http://www.softcomputing.net/jikm.pdf",
doi = "doi:10.1142/S0219649203000565",
size = "16 pages",
abstract = "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.",
notes = "see also
\cite{oai: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",
}
@Misc{oai:arXiv.org:cs/0405030,
title = "Business Intelligence from Web Usage Mining",
author = "Ajith Abraham",
year = "2004",
month = may # "~06",
abstract = "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.",
identifier = "Journal of Information \& Knowledge Management (JIKM),
World Scientific Publishing Co., Singapore, Vol. 2, No.
4, pp. 375-390, 2003",
oai = "oai:arXiv.org:cs/0405030",
URL = "http://arXiv.org/abs/cs/0405030",
notes = "see also \cite{Abraham:2003:JIKM}",
}
@InCollection{abraham:2004:ECDM,
author = "Ajith Abraham",
title = "Evolutionary Computation in Intelligent Network
Management",
booktitle = "Evolutionary Computing in Data Mining",
publisher = "Springer",
year = "2004",
editor = "Ashish Ghosh and Lakhmi C. Jain",
volume = "163",
series = "Studies in Fuzziness and Soft Computing",
chapter = "9",
pages = "189--210",
keywords = "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",
ISBN = "3-540-22370-3",
URL = "http://www.softcomputing.net/ec_web-chapter.pdf",
URL = "http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980376-0,00.html",
abstract = "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.",
size = "22 pages",
}
@InCollection{intro:2006:GSP,
author = "Ajith Abraham and Nadia Nedjah and Luiza {de Macedo
Mourelle}",
title = "Evolutionary Computation: from Genetic Algorithms to
Genetic Programming",
year = "2006",
booktitle = "Genetic Systems Programming: Theory and Experiences",
pages = "1--20",
volume = "13",
series = "Studies in Computational Intelligence",
editor = "Nadia Nedjah and Ajith Abraham and Luiza {de Macedo
Mourelle}",
publisher = "Springer",
address = "Germany",
keywords = "genetic algorithms, genetic programming, cartesian
genetic programming",
ISBN = "3-540-29849-5",
URL = "http://www.softcomputing.net/gpsystems.pdf",
abstract = "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).",
notes = "http://www.springer.com/sgw/cda/frontpage/0,11855,5-146-22-92733168-0,00.html",
size = "21 pages",
}
@InCollection{abraham:2006:GSP,
author = "Ajith Abraham and Crina Grosan",
title = "Evolving Intrusion Detection Systems",
year = "2006",
booktitle = "Genetic Systems Programming: Theory and Experiences",
pages = "57--80",
volume = "13",
series = "Studies in Computational Intelligence",
editor = "Nadia Nedjah and Ajith Abraham and Luiza {de Macedo
Mourelle}",
publisher = "Springer",
address = "Germany",
email = "ajith.abraham@ieee.org",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-29849-5",
URL = "http://falklands.globat.com/~softcomputing.net/ids-chapter.pdf",
abstract = "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.",
notes = "http://www.springer.com/sgw/cda/frontpage/0,11855,5-146-22-92733168-0,00.html",
}
@InProceedings{abraham:2005:CEC,
author = "Ajith Abraham and Crina Grosan",
title = "Genetic Programming Approach for Fault Modeling of
Electronic Hardware",
booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary
Computation",
year = "2005",
editor = "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",
volume = "2",
pages = "1563--1569",
address = "Edinburgh, UK",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "2-5 " # sep,
organisation = "IEEE Computational Intelligence Society, Institution
of Electrical Engineers (IEE), Evolutionary Programming
Society (EPS)",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, MEP, ANN,
LGP",
ISBN = "0-7803-9363-5",
URL = "http://www.softcomputing.net/cec05.pdf",
size = "7 pages",
abstract = "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.",
notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and
the EPS.",
}
@Article{journals/jikm/AbrahamG06,
author = "Ajith Abraham and Crina Grosan",
title = "Decision Support Systems Using Ensemble Genetic
Programming",
journal = "Journal of Information \& Knowledge Management
(JIKM)",
year = "2006",
volume = "5",
number = "4",
pages = "303--313",
month = dec,
note = "Special topic: Knowledge Discovery Using Advanced
Computational Intelligence Tools",
keywords = "genetic algorithms, genetic programming, gene
expression programming, Decision support systems,
ensemble systems, evolutionary multi-objective
optimisation",
ISSN = "0219-6492",
doi = "doi:10.1142/S0219649206001566",
abstract = "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.",
bibdate = "2008-06-20",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/jikm/jikm5.html#AbrahamG06",
}
@Article{Abraham:2007:JNCS,
author = "Ajith Abraham and Ravi Jain and Johnson Thomas and
Sang Yong Han",
title = "{D}-{SCIDS}: Distributed soft computing intrusion
detection system",
journal = "Journal of Network and Computer Applications",
year = "2007",
volume = "30",
number = "1",
pages = "81--98",
month = jan,
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1016/j.jnca.2005.06.001",
abstract = "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.",
}
@InProceedings{Abraham:2008:ieeeISI,
author = "Ajith Abraham",
title = "Real time intrusion prediction, detection and
prevention programs",
booktitle = "IEEE International Conference on Intelligence and
Security Informatics, ISI 2008",
year = "2008",
month = jun,
pages = "xli--xlii",
note = "IEEE ISI 2008 Invited Talk (VI)",
keywords = "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",
doi = "doi:10.1109/ISI.2008.4565018",
size = "1.1 pages",
abstract = "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.",
notes = "Also known as \cite{4565018}",
}
@InProceedings{Abraham:2009:UKSIM,
author = "Ajith Abraham and Crina Grosan and Vaclav Snasel",
title = "Programming Risk Assessment Models for Online Security
Evaluation Systems",
booktitle = "11th International Conference on Computer Modelling
and Simulation, UKSIM '09",
year = "2009",
month = "25-27 " # mar,
pages = "41--46",
keywords = "genetic algorithms, genetic programming, genetic
programming methods, human reasoning, online security
evaluation systems, perception process, programming
risk assessment models, risk management, security of
data",
doi = "doi:10.1109/UKSIM.2009.75",
isbn13 = "978-0-7695-3593-7",
abstract = "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.",
notes = "Also known as \cite{4809735}",
}
@InProceedings{Abraham:2009:IAS,
author = "Ajith Abraham and Crina Grosan and Hongbo Liu and
Yuehui Chen",
title = "Hierarchical Takagi-Sugeno Models for Online Security
Evaluation Systems",
booktitle = "Fifth International Conference on Information
Assurance and Security, IAS '09",
year = "2009",
month = aug,
volume = "1",
pages = "687--692",
keywords = "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",
doi = "doi:10.1109/IAS.2009.348",
abstract = "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.",
notes = "Also known as \cite{5283215}",
}
@InCollection{abrams:2000:CSAMPR,
author = "Zoe Abrams",
title = "Complimentary Selection as an Alternative Method for
Population Reproduction",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2000",
year = "2000",
editor = "John R. Koza",
pages = "8--15",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "part of \cite{koza:2000:gagp}",
}
@InProceedings{abramson:1996:cccGP,
author = "Myriam Abramson and Lawrence Hunter",
title = "Classification using Cultural Co-Evolution and Genetic
Programming",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "249--254",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
broken = "ftp://lhc.nlm.nih.gov/pub/hunter/gp96.ps",
size = "6 pages",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96",
}
@InProceedings{AbuDalhoum:2005:ESM,
author = "Abdel Latif {Abu Dalhoum} and Moh'd {Al Zoubi} and
Marina {de la Cruz} and Alfonso Ortega and Manuel
Alfonseca",
title = "A Genetic Algorithm for Solving the {P}-Median
Problem",
booktitle = "European Simulation and Modeling Conference ESM'2005",
year = "2005",
editor = "J. Manuel Feliz Teixeira and A. E.{Carvalho Brito}",
pages = "141--145",
address = "Porto, Portugal",
month = oct # " 24-26",
organisation = "Eurosim, The European Multidisciplinary Society for
Modelling and Simulation Technology",
publisher = "http://www.eurosis.org",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, Christiansen grammar",
ISBN = "90-77381-22-8",
URL = "http://arantxa.ii.uam.es/~alfonsec/docs/confint/pmedian.pdf",
notes = "Title may be listed as {"}A Genetic Algorithm for
solving the P-Medium
Problem{"}
http://www.eurosis.org/cms/files/proceedings/ESM/ESM2005contents.pdf",
}
@TechReport{AcarM05tr,
author = "Aybar C. Acar and Amihai Motro",
title = "Intensional Encapsulations of Database Subsets by
Genetic Programming",
institution = "Information and Software Engineering Department, The
Volgenau School of Information Technology and
Engineering, George Mason University",
year = "2005",
number = "ISE-TR-05-01",
month = feb,
keywords = "genetic algorithms, genetic programming",
URL = "http://ise.gmu.edu/techrep/2005/05_01.pdf",
abstract = "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.",
notes = "See \cite{conf/dexa/AcarM05}",
size = "17 pages",
}
@InProceedings{conf/dexa/AcarM05,
title = "Intensional Encapsulations of Database Subsets via
Genetic Programming",
author = "Aybar C. Acar and Amihai Motro",
year = "2005",
pages = "365--374",
editor = "Kim Viborg Andersen and John K. Debenham and Roland
Wagner",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3588",
booktitle = "Database and Expert Systems Applications, 16th
International Conference, DEXA 2005, Proceedings",
address = "Copenhagen, Denmark",
month = aug # " 22-26",
bibdate = "2005-11-03",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/dexa/dexa2005.html#AcarM05",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-28566-0",
doi = "doi:10.1007/11546924_36",
size = "10 pages",
abstract = "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.",
notes = "See also \cite{AcarM05tr}",
}
@PhdThesis{Acar:thesis,
author = "Aybar C. Acar",
title = "Query Consolidation: Interpreting Queries Sent to
Independent Heterogenous Databases",
school = "The Volgenau School of Information Technology and
Engineering, George Mason University",
year = "2008",
address = "Fairfax, VA, USA",
month = "23 " # jul,
keywords = "genetic algorithms, genetic programming, Databases,
Information Integration, Query Processing, Machine
Learning",
URL = "http://hdl.handle.net/1920/3223",
URL = "http://digilib.gmu.edu:8080/dspace/bitstream/1920/3223/1/Acar_Aybar.pdf",
size = "182 pages",
abstract = "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.",
notes = "GP chapters 7, 8",
}
@InProceedings{Ackling:2011:GECCO,
author = "Thomas Ackling and Bradley Alexander and Ian Grunert",
title = "Evolving patches for software repair",
booktitle = "GECCO '11: Proceedings of the 13th annual conference
on Genetic and evolutionary computation",
year = "2011",
editor = "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",
isbn13 = "978-1-4503-0557-0",
pages = "1427--1434",
keywords = "genetic algorithms, genetic programming, SBSE,
Python",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001576.2001768",
publisher = "ACM",
publisher_address = "New York, NY, USA",
size = "8 pages",
abstract = "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.",
notes = "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 \cite{2001768} GECCO-2011 A joint meeting
of the twentieth international conference on genetic
algorithms (ICGA-2011) and the sixteenth annual genetic
programming conference (GP-2011)",
}
@InProceedings{adamopoulos:1999:FMEPUGONN,
author = "Adam V. Adamopoulos and Efstratios F. Georgopoulos and
Spiridon D. Likothanassis and Photios A. Anninos",
title = "Forecasting the MagnetoEncephaloGram ({MEG}) of
Epileptic Patients Using Genetically Optimized Neural
Networks",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1457--1462",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "real world applications",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-767.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-767.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InCollection{adams:2002:CSDPSAGP,
author = "Thomas P. Adams",
title = "Creation of Simple, Deadline, and Priority Scheduling
Algorithms using Genetic Programming",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2002",
year = "2002",
editor = "John R. Koza",
pages = "1--10",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.genetic-programming.org/sp2002/Adams.pdf",
notes = "part of \cite{koza: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",
}
@InProceedings{adorni:1998:cpapc,
author = "Giovanni Adorni and Federico Bergenti and Stefano
Cagnoni",
title = "A cellular-programming approach to pattern
classification",
booktitle = "Proceedings of the First European Workshop on Genetic
Programming",
year = "1998",
editor = "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer
and Terence C. Fogarty",
volume = "1391",
series = "LNCS",
pages = "142--150",
address = "Paris",
publisher_address = "Berlin",
month = "14-15 " # apr,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-64360-5",
abstract = "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.",
notes = "EuroGP'98",
}
@InProceedings{adorni:1999:GPgkcsrcmsc,
author = "Giovanni Adorni and Stefano Cagnoni and Monica
Mordonini",
title = "Genetic Programming of a Goal-Keeper Control Strategy
for the RoboCup Middle Size Competition",
booktitle = "Genetic Programming, Proceedings of EuroGP'99",
year = "1999",
editor = "Riccardo Poli and Peter Nordin and William B. Langdon
and Terence C. Fogarty",
volume = "1598",
series = "LNCS",
pages = "109--119",
address = "Goteborg, Sweden",
publisher_address = "Berlin",
month = "26-27 " # may,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-65899-8",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1598&spage=109",
notes = "EuroGP'99, part of \cite{poli:1999:GP}
Robot goalkeeper (.4m) controlled by twin cameras using
GP. Able to intercept football sometimes.",
}
@InProceedings{oai:CiteSeerPSU:539182,
author = "Giovanni Adorni and Stefano Cagnoni and Monica
Mordonini",
title = "Efficient low-level vision program design using
Sub-machine-code Genetic Programming",
booktitle = "AIIA 2002, Workshop sulla Percezione e Visione nelle
Macchine",
year = "2002",
editor = "Marco Gori",
address = "Siena, Italy",
month = "10-13 " # sep,
keywords = "genetic algorithms, genetic programming",
citeseer-isreferencedby = "oai:CiteSeerPSU:127480;
oai:CiteSeerPSU:236266; oai:CiteSeerPSU:154480;
oai:CiteSeerPSU:124779; oai:CiteSeerPSU:98673;
oai:CiteSeerPSU:519310; oai:CiteSeerPSU:493558;
oai:CiteSeerPSU:151056; oai:CiteSeerPSU:128026;
oai:CiteSeerPSU:195245; oai:CiteSeerPSU:154216;
oai:CiteSeerPSU:198970; oai:CiteSeerPSU:98326;
oai:CiteSeerPSU:197064; oai:CiteSeerPSU:88615;
oai:CiteSeerPSU:200149; oai:CiteSeerPSU:299112",
citeseer-references = "oai:CiteSeerPSU:70349; oai:CiteSeerPSU:329358",
annote = "The Pennsylvania State University CiteSeer Archives",
description = "Sub-machine-code Genetic Programming (SmcGP) is a
variant of GP aimed at exploiting the intrinsic
parallelism of sequential CPUs.",
language = "en",
oai = "oai:CiteSeerPSU:539182",
rights = "unrestricted",
URL = "http://www-dii.ing.unisi.it/aiia2002/paper/PERCEVISIO/adorni-aiia02.pdf",
URL = "http://citeseer.ist.psu.edu/539182.html",
size = "8 pages",
abstract = "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.",
notes = "http://www-dii.ing.unisi.it/aiia2002/paper.htm",
}
@InProceedings{adorni:2001:wsc6,
author = "Giovanni Adorni and Stefano Cagnoni",
title = "Design of Explicitly or Implicitly Parallel
Low-resolution Character Recognition Algorithms by
Means of Genetic Programming",
booktitle = "Soft Computing and Industry Recent Applications",
year = "2001",
editor = "Rajkumar Roy and Mario K{\"o}ppen and Seppo Ovaska and
Takeshi Furuhashi and Frank Hoffmann",
pages = "387--398",
month = "10--24 " # sep,
publisher = "Springer-Verlag",
note = "Published 2002",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-85233-539-4",
notes = "WSC6 Out of print?
http://www.amazon.co.uk/Soft-Computing-Industry-Recent-Applications/dp/1852335394
",
}
@Book{Affenzeller:GAGP,
author = "Michael Affenzeller and Stephan Winkler and Stefan
Wagner and Andreas Beham",
title = "Genetic Algorithms and Genetic Programming: Modern
Concepts and Practical Applications",
publisher = "CRC Press",
year = "2009",
series = "Numerical Insights",
address = "Singapore",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-58488-629-3",
URL = "http://gagp2009.heuristiclab.com/",
URL = "http://www.crcpress.com/product/isbn/9781584886297",
abstract = "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.",
notes = "Reviewed in \cite{Pappa:2009:GPEM}. My copy missing
pages i to vi.
",
size = "379 pages",
}
@InProceedings{AfzalTF08,
author = "Wasif Afzal and Richard Torkar and Robert Feldt",
title = "A Systematic Mapping Study on Non-Functional
Search-based Software Testing",
booktitle = "Proceedings of the 20th International Conference on
Software Engineering and Knowledge Engineering (SEKE
'08)",
year = "2008",
pages = "488--493",
address = "San Francisco, CA, USA",
month = jul # " 1-3",
publisher = "Knowledge Systems Institute Graduate School",
keywords = "genetic algorithms, genetic programming",
bibsource = "http://www.sebase.org/sbse/publications/repository.html",
ISBN = "1-891706-22-5",
abstract = "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.",
}
@InProceedings{Afzal08e,
author = "Wasif Afzal and Richard Torkar",
title = "Suitability of Genetic Programming for Software
Reliability Growth Modeling",
booktitle = "The 2008 International Symposium on Computer Science
and its Applications (CSA'08)",
year = "2008",
pages = "114--117",
address = "Hobart, ACT",
month = "13-15 " # oct,
publisher = "IEEE Computer Society",
keywords = "genetic algorithms, genetic programming, software
reliability data points, software reliability growth
modeling, SBSE",
doi = "doi:10.1109/CSA.2008.13",
abstract = "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.",
notes = "Also known as \cite{4654071}
",
}
@InProceedings{Afzal08d,
author = "Wasif Afzal and Richard Torkar",
title = "A comparative evaluation of using genetic programming
for predicting fault count data",
booktitle = "Proceedings of the Third International Conference on
Software Engineering Advances (ICSEA'08)",
year = "2008",
pages = "407--414",
address = "Sliema, Malta",
month = "26-31",
keywords = "genetic algorithms, genetic programming, prediction,
software reliability growth modeling, SBSE",
isbn13 = "978-1-4244-3218-9",
doi = "doi:10.1109/ICSEA.2008.9",
abstract = "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.",
notes = "Also known as \cite{4668139}
",
}
@InProceedings{Afzal08b,
author = "Wasif Afzal and Richard Torkar and Robert Feldt",
title = "Prediction of fault count data using genetic
programming",
booktitle = "Proceedings of the 12th IEEE International Multitopic
Conference (INMIC'08)",
year = "2008",
pages = "349--356",
address = "Karachi, Pakistan",
month = "23-24 " # dec,
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming, SBSE, fault
count data, prediction",
isbn13 = "978-1-4244-2823-6",
URL = "http://drfeldt.googlepages.com/afzal_submitted0805icsea_prediction_.pdf",
doi = "doi:10.1109/INMIC.2008.4777762",
abstract = "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.",
notes = "Also known as \cite{4777762}
",
}
@InProceedings{Afzal:2009:SSBSE,
author = "Wasif Afzal and Richard Torkar and Robert Feldt",
title = "Search-Based Prediction of Fault Count Data",
booktitle = "Proceedings 1st International Symposium on Search
Based Software Engineering SSBSE 2009",
year = "2009",
editor = "Massimiliano {Di Penta} and Simon Poulding",
pages = "35--38",
address = "Windsor, UK",
month = "13-15 " # may,
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming, SBSE,
search-based prediction, software fault count data,
software reliability growth model, symbolic regression,
regression analysis, software fault tolerance",
isbn13 = "978-0-7695-3675-0",
doi = "doi:10.1109/SSBSE.2009.17",
abstract = "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.",
notes = "order number P3675 http://www.ssbse.org/ Also known as
\cite{5033177}",
}
@Article{Afzal2009,
author = "Wasif Afzal and Richard Torkar and Robert Feldt",
title = "A systematic review of search-based testing for
non-functional system properties",
journal = "Information and Software Technology",
year = "2009",
volume = "51",
number = "6",
pages = "957--976",
month = jun,
keywords = "genetic algorithms, genetic programming, Systematic
review, Non-functional system properties, Search-based
software testing",
ISSN = "0950-5849",
URL = "http://drfeldt.googlepages.com/afzal_submitted0805ist_sysrev_nfr_sb.pdf",
URL = "http://www.sciencedirect.com/science/article/B6V0B-4VHXDTD-1/2/9da989f9d874eb88d1f82d9a0878114b",
doi = "doi:10.1016/j.infsof.2008.12.005",
abstract = "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.",
}
@MastersThesis{Afzal:Licentiate,
author = "Wasif Afzal",
title = "Search-Based Approaches to Software Fault Prediction
and Software Testing",
school = "School of Engineering, Dept. of Systems and Software
Engineering, Blekinge Institute of Technology",
year = "2009",
type = "Licentiate Dissertation",
address = "Sweden",
keywords = "genetic algorithms, genetic programming, SBSE,
Software Engineering, Computer Science, Artificial
Intelligence",
URL = "http://www.bth.se/fou/forskinfo.nsf/all/f0738b5fc4ca0bbac12575980043def3/$file/Afzal_lic.pdf",
URL = "http://www.bth.se/fou/forskinfo.nsf/all/f0738b5fc4ca0bbac12575980043def3?OpenDocument",
size = "212 pages",
isbn13 = "978-91-7295-163-1",
language = "eng",
oai = "oai:bth.se:forskinfoF0738B5FC4CA0BBAC12575980043DEF3",
abstract = "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.",
}
@InProceedings{Afzal:2010:SSBSE,
author = "Wasif Afzal and Richard Torkar and Robert Feldt and
Greger Wikstrand",
title = "Search-based Prediction of Fault-slip-through in Large
Software Projects",
booktitle = "Second International Symposium on Search Based
Software Engineering (SSBSE 2010)",
year = "2010",
month = "7-9 " # sep,
pages = "79--88",
address = "Benevento, Italy",
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/SSBSE.2010.19",
notes = "Also known as \cite{5635180}",
}
@InProceedings{Afzal:2010:APSEC,
author = "Wasif Afzal",
title = "Using Faults-Slip-Through Metric as a Predictor of
Fault-Proneness",
booktitle = "17th Asia Pacific Software Engineering Conference
(APSEC 2010)",
year = "2010",
month = nov # " 30-" # dec # " 3",
pages = "414--422",
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/APSEC.2010.54",
ISSN = "1530-1362",
notes = "Blekinge Inst. of Technol., Ronneby, Sweden. Also
known as \cite{5693218}",
}
@Article{Afzal201111984,
author = "Wasif Afzal and Richard Torkar",
title = "On the application of genetic programming for software
engineering predictive modeling: {A} systematic
review",
journal = "Expert Systems with Applications",
volume = "38",
number = "9",
pages = "11984--11997",
year = "2011",
ISSN = "0957-4174",
doi = "doi:10.1016/j.eswa.2011.03.041",
URL = "http://www.sciencedirect.com/science/article/B6V03-52C8FT6-5/2/668361024e4b2bcf9a4a73195271591c",
keywords = "genetic algorithms, genetic programming, Systematic
review, Symbolic regression, Modelling",
abstract = "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.",
}
@InProceedings{agapie:1999:RSCC,
author = "Alexandru Agapie",
title = "Random Systems with Complete Connections",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "770",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems, poster
papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-862.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{eurogp06:AgapitosLucas,
author = "Alexandros Agapitos and Simon M. Lucas",
title = "Learning Recursive Functions with Object Oriented
Genetic Programming",
editor = "Pierre Collet and Marco Tomassini and Marc Ebner and
Steven Gustafson and Anik\'o Ek\'art",
booktitle = "Proceedings of the 9th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3905",
year = "2006",
address = "Budapest, Hungary",
month = "10 - 12 " # apr,
organisation = "EvoNet",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-33143-3",
pages = "166--177",
URL = "http://link.springer.de/link/service/series/0558/papers/3905/39050166.pdf",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in
conjunction with EvoCOP2006 and EvoWorkshops2006
Java reflection.",
}
@InProceedings{Agapitos:2006:CEC,
author = "Alexandros Agapitos and Simon M. Lucas",
title = "Evolving Efficient Recursive Sorting Algorithms",
booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary
Computation",
year = "2006",
editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and
Simon M. Lucas",
pages = "9227--9234",
address = "Vancouver",
month = "6-21 " # jul,
publisher = "IEEE Press",
keywords = "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",
ISBN = "0-7803-9487-9",
URL = "http://privatewww.essex.ac.uk/~aagapi/papers/AgapitosLucasEvolvingSort.pdf",
doi = "doi:10.1109/CEC.2006.1688643",
size = "8 pages",
abstract = "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.",
notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
the IEE.
Best in session. IEEE Xplore gives pages as
2677--2684",
}
@InProceedings{eurogp07:agapitos1,
author = "Alexandros Agapitos and Simon M. Lucas",
title = "Evolving a Statistics Class Using Object Oriented
Evolutionary Programming",
editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and
Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar",
booktitle = "Proceedings of the 10th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "4445",
year = "2007",
address = "Valencia, Spain",
month = "11-13 " # apr,
pages = "291--300",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-540-71602-0",
ISBN = "3-540-71602-5",
doi = "doi:10.1007/978-3-540-71605-1_27",
abstract = "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.",
notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in
conjunction with EvoCOP2007, EvoBIO2007 and
EvoWorkshops2007",
}
@InProceedings{eurogp07:agapitos2,
author = "Alexandros Agapitos and Simon M. Lucas",
title = "Evolving Modular Recursive Sorting Algorithms",
editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and
Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar",
booktitle = "Proceedings of the 10th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "4445",
year = "2007",
address = "Valencia, Spain",
month = "11-13 " # apr,
pages = "301--310",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-540-71602-0",
ISBN = "3-540-71602-5",
doi = "doi:10.1007/978-3-540-71605-1_28",
abstract = "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.",
notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in
conjunction with EvoCOP2007, EvoBIO2007 and
EvoWorkshops2007",
}
@InProceedings{1277271,
author = "Alexandros Agapitos and Julian Togelius and Simon Mark
Lucas",
title = "Evolving controllers for simulated car racing using
object oriented genetic programming",
booktitle = "GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation",
year = "2007",
editor = "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",
volume = "2",
isbn13 = "978-1-59593-697-4",
pages = "1543--1550",
address = "London",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1543.pdf",
doi = "doi:10.1145/1276958.1277271",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
month = "7-11 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, evolutionary
computer games, evolutionary robotics, homologous
uniform crossover, neural networks, object oriented,
subtree macro-mutation",
abstract = "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.",
notes = "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",
}
@InProceedings{Agapitos:2007:cec,
title = "Multiobjective Techniques for the Use of State in
Genetic Programming Applied to Simulated Car Racing",
author = "Alexandros Agapitos and Julian Togelius and Simon M.
Lucas",
booktitle = "2007 IEEE Congress on Evolutionary Computation",
year = "2007",
editor = "Dipti Srinivasan and Lipo Wang",
pages = "1562--1569",
address = "Singapore",
month = "25-28 " # sep,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "1-4244-1340-0",
file = "1977.pdf",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and
the IET.
IEEE Catalog Number: 07TH8963C",
}
@InProceedings{Agapitos:2008:gecco,
author = "Alexandros Agapitos and Matthew Dyson and Simon M.
Lucas and Francisco Sepulveda",
title = "Learning to recognise mental activities: genetic
programming of stateful classifiers for brain-computer
interfacing",
booktitle = "GECCO '08: Proceedings of the 10th annual conference
on Genetic and evolutionary computation",
year = "2008",
editor = "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",
isbn13 = "978-1-60558-130-9",
pages = "1155--1162",
address = "Atlanta, GA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1155.pdf",
doi = "doi:10.1145/1389095.1389326",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "12-16 " # jul,
keywords = "genetic algorithms, genetic programming, Brain
computer interface, classification on Raw signal,
stateful representation, statistical signal
primitives",
notes = "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 \cite{1389326}",
}
@InProceedings{Agapitos2:2008:gecco,
author = "Alexandros Agapitos and Matthew Dyson and Jenya
Kovalchuk and Simon Mark Lucas",
title = "On the genetic programming of time-series predictors
for supply chain management",
booktitle = "GECCO '08: Proceedings of the 10th annual conference
on Genetic and evolutionary computation",
year = "2008",
editor = "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",
isbn13 = "978-1-60558-130-9",
pages = "1163--1170",
address = "Atlanta, GA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1163.pdf",
URL = "http://privatewww.essex.ac.uk/~yvkova/Papers/GP_GECCO08.pdf",
doi = "doi:10.1145/1389095.1389327",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "12-16 " # jul,
keywords = "genetic algorithms, genetic programming, Iterated
single-step prediction, prediction/forecasting,
single-step prediction, statistical time-series
Features",
notes = "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 \cite{1389327}",
}
@InProceedings{Agapitos:2008:CIG,
author = "Alexandros Agapitos and Julian Togelius and Simon M.
Lucas and Jurgen Schmidhuber and Andreas
Konstantinidis",
title = "Generating Diverse Opponents with Multiobjective
Evolution",
booktitle = "Proceedings of the 2008 IEEE Symposium on
Computational Intelligence and Games",
year = "2008",
pages = "135--142",
address = "Perth, Australia",
month = dec # " 15-18",
publisher = "IEEE",
keywords = "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",
URL = "http://julian.togelius.com/Agapitos2008Generating.pdf",
doi = "doi:10.1109/CIG.2008.5035632",
abstract = "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.",
notes = "Also known as \cite{5035632}",
}
@InProceedings{agapitos_etal:ppsn2010,
author = "Alexandros Agapitos and Michael O'Neill and Anthony
Brabazon",
title = "Evolutionary Learning of Technical Trading Rules
without Data-mining Bias",
booktitle = "PPSN 2010 11th International Conference on Parallel
Problem Solving From Nature",
pages = "294--303",
year = "2010",
volume = "6238",
editor = "Robert Schaefer and Carlos Cotta and Joanna Kolodziej
and Guenter Rudolph",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
isbn13 = "978-3-642-15843-8",
address = "Krakow, Poland",
month = "11-15 " # sep,
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1007/978-3-642-15844-5_30",
abstract = "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.",
}
@InProceedings{Agapitos:2010:AIAI,
author = "Alexandros Agapitos and Andreas Konstantinidis and
Haris Haralambous and Harris Papadopoulos",
title = "Evolutionary Prediction of Total Electron Content over
Cyprus",
booktitle = "6th IFIP Advances in Information and Communication
Technology AIAI 2010",
year = "2010",
editor = "Harris Papadopoulos and Andreas Andreou and Max
Bramer",
volume = "339",
series = "IFIP Advances in Information and Communication
Technology",
pages = "387--394",
address = "Larnaca, Cyprus",
month = oct # " 6-7",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, Evolutionary
Algorithms, Global Positioning System, Total Electron
Content",
doi = "doi:10.1007/978-3-642-16239-8_50",
abstract = "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.",
affiliation = "School of Computer Science and Informatics, University
College Dublin, Dublin, Ireland",
notes = "http://www.cs.ucy.ac.cy/aiai2010/",
}
@InProceedings{agapitosetal:2010:cfe,
author = "Alexandros Agapitos and Michael O'Neill and Anthony
Brabazon",
title = "Promoting the generalisation of genetically induced
trading rules",
booktitle = "Proceedings of the 4th International Conference on
Computational and Financial Econometrics CFE'10",
year = "2010",
editor = "G. Kapetanios and O. Linton and M. McAleer and E.
Ruiz",
pages = "E678",
address = "Senate House, University of London, UK",
month = "10-12 " # dec,
organisation = "CSDA, LSE, Queen Mary and Westerfield College",
publisher = "ERCIM",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cfe-csda.org/cfe10/LondonBoA.pdf",
size = "Abstracts only",
abstract = "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.",
notes = "http://www.cfe-csda.org/cfe10/",
}
@InProceedings{agapitos:2011:EuroGP,
author = "Alexandros Agapitos and Michael O'Neill and Anthony
Brabazon and Theodoros Theodoridis",
title = "Maximum Margin Decision Surfaces for Increased
Generalisation in Evolutionary Decision Tree Learning",
booktitle = "Proceedings of the 14th European Conference on Genetic
Programming, EuroGP 2011",
year = "2011",
month = "27-29 " # apr,
editor = "Sara Silva and James A. Foster and Miguel Nicolau and
Mario Giacobini and Penousal Machado",
series = "LNCS",
volume = "6621",
publisher = "Springer Verlag",
address = "Turin, Italy",
pages = "61--72",
organisation = "EvoStar",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-20406-7",
doi = "doi:10.1007/978-3-642-20407-4_6",
abstract = "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.",
notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in
conjunction with EvoCOP2011 EvoBIO2011 and
EvoApplications2011",
}
@InProceedings{Agapitos:2011:GECCOcomp,
author = "Alexandros Agapitos and Michael O'Neill and Anthony
Brabazon",
title = "Stateful program representations for evolving
technical trading rules",
booktitle = "GECCO '11: Proceedings of the 13th annual conference
companion on Genetic and evolutionary computation",
year = "2011",
editor = "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",
isbn13 = "978-1-4503-0690-4",
keywords = "genetic algorithms, genetic programming: Poster",
pages = "199--200",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001858.2001969",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{2001969} Distributed on CD-ROM at
GECCO-2011.
ACM Order Number 910112.",
}
@InProceedings{Agapitos:2011:CIG,
author = "Alexandros Agapitos and Michael O'Neill and Anthony
Brabazon and Theodoros Theodoridis",
title = "Learning Environment Models in Car Racing Using
Stateful Genetic Programming",
booktitle = "Proceedings of the 2011 IEEE Conference on
Computational Intelligence and Games",
year = "2011",
address = "Seoul, South Korea",
pages = "219--226",
month = "31 " # aug # " - 3 " # sep,
publisher = "IEEE",
keywords = "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",
isbn13 = "978-1-4577-0010-1",
URL = "http://cilab.sejong.ac.kr/cig2011/proceedings/CIG2011/papers/paper54.pdf",
doi = "doi:10.1109/CIG.2011.6032010",
size = "8 pages",
abstract = "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.",
notes = "Indexed memory.
Also known as \cite{6032010}",
}
@InCollection{Agapitos:NCFE:2011,
author = "Alexandros Agapitos and Abhinav Goyal and Cal
Muckley",
title = "An Evolutionary Algorithmic Investigation of {US}
Corporate Payout Policy",
booktitle = "Natural Computing in Computational Finance (Volume
4)",
publisher = "Springer",
year = "2012",
editor = "Anthony Brabazon and Michael O'Neill and Dietmar
Maringer",
volume = "380",
series = "Studies in Computational Intelligence",
chapter = "7",
pages = "123--139",
keywords = "genetic algorithms, genetic programming, US Corporate
Payout Policy, Symbolic Regression",
isbn13 = "978-3-642-23335-7",
URL = "http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-642-23335-7",
}
@InProceedings{agapitos:evoapps12,
author = "Alexandros Agapitos and Michael O'Neill and Anthony
Brabazon",
title = "Evolving Seasonal Forecasting Models with Genetic
Programming for Pricing Weather-derivatives",
booktitle = "Applications of Evolutionary Computing,
EvoApplications 2012: EvoCOMNET, EvoCOMPLEX, EvoFIN,
EvoGAMES, EvoHOT, EvoIASP, EvoNUM, EvoPAR, EvoRISK,
EvoSTIM, EvoSTOC",
year = "2011",
month = "11-13 " # apr,
editor = "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",
series = "LNCS",
volume = "7248",
publisher = "Springer Verlag",
address = "Malaga, Spain",
publisher_address = "Berlin",
pages = "135--144",
organisation = "EvoStar",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-29177-7",
doi = "doi:10.1007/978-3-642-29178-4_14",
abstract = "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.",
notes = "EvoFIN Part of \cite{DiChio:2012:EvoApps}
EvoApplications2012 held in conjunction with
EuroGP2012, EvoCOP2012, EvoBio'2012 and EvoMusArt2012",
}
@InCollection{Agapitos:FDMCI:2012,
author = "Alexandros Agapitos and Michael O'Neill and Anthony
Brabazon",
title = "Genetic Programming for the Induction of Seasonal
Forecasts: {A} Study on Weather Derivatives",
booktitle = "Financial Decision Making Using Computational
Intelligence",
publisher = "Springer",
year = "2012",
editor = "Doumpos Michael and Zopounidis Constantin and Pardalos
Panos",
volume = "70",
series = "Springer Optimization and Its Applications",
chapter = "6",
pages = "153--182",
note = "Due: July 31, 2012",
keywords = "genetic algorithms, genetic programming, Weather
derivatives pricing, Seasonal temperature forecasting,
Autoregressive models, Supervised ensemble learning,
Generalisation",
isbn13 = "978-1-4614-3772-7",
URL = "http://www.springer.com/mathematics/applications/book/978-1-4614-3772-7",
}
@InProceedings{agapow:1996:cbecv,
author = "Paul-Michael Agapow",
title = "Computational Brittleness and the Evolution of
Computer Viruses",
editor = "Hans-Michael Voigt and Werner Ebeling and Ingo
Rechenberg and Hans-Paul Schwefel",
booktitle = "Parallel Problem Solving From Nature IV. Proceedings
of the International Conference on Evolutionary
Computation",
year = "1996",
publisher = "Springer-Verlag",
volume = "1141",
series = "LNCS",
pages = "2--11",
address = "Berlin, Germany",
publisher_address = "Heidelberg, Germany",
month = "22-26 " # sep,
ISBN = "3-540-61723-X",
doi = "doi:10.1007/3-540-61723-X_964",
size = "10 pages",
abstract = "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.",
notes = "http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4
(UNIX Sun RISC) {"}programs are far less brittle than
expected{"}.",
affiliation = "La Trobe University Computer Science V. 3083 Melbourne
Australia V. 3083 Melbourne Australia",
}
@InCollection{agarwal:2000:GPWPPAPE,
author = "Ashish Agarwal",
title = "Genetic Programming for Wafer Property Prediction
After Plasma Enhanced",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2000",
year = "2000",
editor = "John R. Koza",
pages = "16--24",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "part of \cite{koza:2000:gagp}",
}
@Misc{Aggarwal:intern,
author = "Varun Aggarwal",
title = "Prediction of Protein Secondary Structure using
Genetic Programming",
howpublished = "Summer Internship Project Report During June-July
2003",
year = "2003",
keywords = "genetic algorithms, genetic programming",
URL = "http://web.mit.edu/varun_ag/www/psspreport.pdf",
size = "23 pages",
abstract = "Project 1:Using SOM and Genetic Programming to predict
Protein Secondary structure
Project 2: Improving PSIPRED Predictions using Genetic
Programming",
notes = "Under: Dr. Bob MacCallum, Stockholm Bioinformatics
Center, Stockholm University, Sweden",
}
@InProceedings{maccallum:2004:eurogp,
author = "Varun Aggarwal and Robert MacCallum",
title = "Evolved Matrix Operations for Post-Processing Protein
Secondary Structure Predictions",
booktitle = "Genetic Programming 7th European Conference, EuroGP
2004, Proceedings",
year = "2004",
editor = "Maarten Keijzer and Una-May O'Reilly and Simon M.
Lucas and Ernesto Costa and Terence Soule",
volume = "3003",
series = "LNCS",
pages = "220--229",
address = "Coimbra, Portugal",
publisher_address = "Berlin",
month = "5-7 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-21346-5",
URL = "http://web.mit.edu/varun_ag/www/aggarwal-eurogp2004.pdf",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=220",
abstract = "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.",
notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in
conjunction with EvoCOP2004 and EvoWorkshops2004",
}
@InCollection{Aggarwal:2006:GPTP,
author = "Varun Aggarwal and Una-May O'Reilly",
title = "Design of Posynomial Models for Mosfets: Symbolic
Regression Using Genetic Algorithms",
booktitle = "Genetic Programming Theory and Practice {IV}",
year = "2006",
editor = "Rick L. Riolo and Terence Soule and Bill Worzel",
volume = "5",
series = "Genetic and Evolutionary Computation",
chapter = "7",
pages = "-",
address = "Ann Arbor",
month = "11-13 " # may,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, circuit
sizing, symbolic regression, posynomial models,
geometric programming",
ISBN = "0-387-33375-4",
URL = "http://people.csail.mit.edu/unamay/publications-dir/gptp06.pdf",
size = "19 pages",
abstract = "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.",
notes = "part of \cite{Riolo:2006:GPTP} Published Jan 2007
after the workshop",
}
@Article{agnelli:2002:PRL,
author = "Davide Agnelli and Alessandro Bollini and Luca
Lombardi",
title = "Image classification: an evolutionary approach",
journal = "Pattern Recognition Letters",
year = "2002",
number = "1-3",
volume = "23",
pages = "303--309",
keywords = "genetic algorithms, genetic programming, Image
classification, Supervised learning",
URL = "http://www.sciencedirect.com/science/article/B6V15-443K10X-6/1/7af8206767ca79f9898fec720a84c656",
ISSN = "0167-8655",
doi = "doi:10.1016/S0167-8655(01)00128-3",
abstract = "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.",
}
@InProceedings{aguilar:1998:rcmmcfssdft,
author = "Jose L. Aguilar and Mariela Cerrada",
title = "Reliability-Centered Maintenance Methodology-Based
Fuzzy Classifier System Design for Fault Tolerance",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "621",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, classifiers",
ISBN = "1-55860-548-7",
notes = "GP-98",
}
@InProceedings{aguilar:1999:ABGAMOP,
author = "Jose Aguilar and Pablo Miranda",
title = "Approaches Based on Genetic Algorithms for
Multiobjective Optimization Problems",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "3--10",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-873.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-873.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{aguilar:1999:TGADRSLS,
author = "Jesus Aguilar and Jose Riquelme and Miguel Toro",
title = "Three Geometric Approaches for representing Decision
Rules in a Supervised Learning System",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "771",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems, poster
papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-391.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-391.ps",
notes = "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 \cite{aguilar:1999:T}",
}
@InProceedings{aguilar:1999:T,
author = "Jesus Aguilar and Jose Riquelme and Miguel Toro",
title = "Three geometric approaches for representing decision
rules in a supervised learning system",
booktitle = "Late Breaking Papers at the 1999 Genetic and
Evolutionary Computation Conference",
year = "1999",
editor = "Scott Brave and Annie S. Wu",
pages = "8--15",
address = "Orlando, Florida, USA",
month = "13 " # jul,
keywords = "Genetic Algorithms, data mining, supervised learning,
hyper rectangles, rotated hyper rectangles, hyper
ellipse",
abstract = "hyperrectangles, rotated hyperrectangles and
hyperellipses",
notes = "GECCO-99LB",
}
@InProceedings{aguilar3:2001:gecco,
title = "Fuzzy Classifier System and Genetic Programming on
System Identification Problems",
author = "Jose Aguilar and Mariela Cerrada",
pages = "1245--1251",
year = "2001",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2001)",
editor = "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",
address = "San Francisco, California, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "7-11 " # jul,
keywords = "genetic algorithms, genetic programming, real world
applications",
ISBN = "1-55860-774-9",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d24.pdf",
notes = "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 \cite{spector:2001:GECCO}",
}
@InProceedings{WSEAS_640_Aguilar,
author = "Jose Aguilar and Mariela Cerrada",
title = "Genetic Programming-Based Approach for System
Identification Applying Genetic Programming to obtain
Separation",
address = "Puerto De La Cruz, Tenerife, Spain",
year = "2001",
month = feb # "~11-15",
booktitle = "WSEAS NNA-FSFS-EC 2001",
pages = "paper ID number 640",
organisation = "The World Scientific and Engineering Academy and
Society (WSEAS)",
keywords = "genetic algorithms, genetic programming, Genetic
Programming, Evolutionary Computation, Identification
Systems",
notes = "www.wseas.com/2001.xls",
}
@Misc{Aguilar:2004:sci,
author = "J. Aguilar and J. Altamiranda",
title = "A Data Mining Algorithm Based on the Genetic
Programming",
year = "2004",
keywords = "genetic algorithms, genetic programming, Data Mining,
Clustering",
size = "10 pages",
abstract = "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.",
notes = "
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
",
}
@InProceedings{Aguilar:DEU:cec2006,
author = "Jose Aguilar and Gilberto Gonzalez",
title = "Data Extrapolation Using Genetic Programming to
Matrices Singular Values Estimation",
booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary
Computation",
year = "2006",
editor = "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",
pages = "3227--3230",
address = "Vancouver, BC, Canada",
month = "16-21 " # jul,
publisher = "IEEE Press",
ISBN = "0-7803-9487-9",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=11108",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
the IEE.
IEEE Catalog Number: 06TH8846D",
}
@InProceedings{aguirre:1999:EH,
author = "Arturo Hernandez Aguirre and Carlos A. Coello Coello
and Bill P. Buckles",
title = "A Genetic Programming Approach to Logic Function
Synthesis by Means of Multiplexers",
booktitle = "Proceedings of the The First NASA/DOD Workshop on
Evolvable Hardware",
year = "1999",
editor = "Adrian Stoica and Didier Keymeulen and Jason Lohn",
pages = "46--53",
address = "Pasadena, California",
month = "19-21 " # jul,
organisation = "Jet Propulsion Laboratory, California Institute of
Technology",
publisher = "IEEE Computer Society",
keywords = "genetic algorithms, genetic programming, evolvable
hardware",
ISBN = "0-7695-0256-3",
URL = "http://computer.org/proceedings/eh/0256/02560046abs.htm",
abstract = "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.",
notes = "EH-1999",
}
@InProceedings{aguirre:1999:CCMOGA,
author = "Hernan E. Aguirre and Kiyoshi Tanaka and Tatsuo
Sugimura",
title = "Cooperative Crossover and Mutation Operators in
Genetic Algorithms",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "772",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems, poster
papers",
ISBN = "1-55860-611-4",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{Hernandez-Aguirre:2004:MIFFfECS,
title = "Mutual Information-based Fitness Functions for
Evolutionary Circuit Synthesis",
author = "Arturo Hernandez-Aguirre and Carlos Coello-Coello",
pages = "1309--1316",
booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary
Computation",
year = "2004",
publisher = "IEEE Press",
month = "20-23 " # jun,
address = "Portland, Oregon",
volume = "2",
keywords = "genetic algorithms, genetic programming, EHW,
Evolutionary Design Automation, Evolutionary design \&
evolvable hardware",
ISBN = "0-7803-8515-2",
URL = "http://delta.cs.cinvestav.mx/~ccoello/conferences/cec04-muxmutual.pdf.gz",
doi = "doi:10.1109/CEC.2004.1331048",
size = "8 pages",
abstract = "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.",
notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and
the IEE.",
}
@Misc{nlin/0607029,
author = "Dilip P. Ahalpara and Jitendra C. Parikh",
title = "Modeling Time Series of Real Systems using Genetic
Programming",
howpublished = "ArXiv Nonlinear Sciences e-prints",
year = "2006",
month = "14 " # jul,
note = "Submitted to Physical Review E",
adsurl = "http://adsabs.harvard.edu/cgi-bin/nph-bib_query?bibcode=2006nlin......7029A&db_key=PRE",
adsnote = "Provided by the Smithsonian/NASA Astrophysics Data
System",
keywords = "genetic algorithms, genetic programming",
URL = "http://arxiv.org/PS_cache/nlin/pdf/0607/0607029v1.pdf",
size = "10 pages",
abstract = "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.",
notes = "nlin/0607029
See also \cite{Ahalpara:2008:IJMPC}",
}
@Article{Ahalpara:2008:IJMPC,
author = "Dilip P. Ahalpara and Jitendra C. Parikh",
title = "Genetic Programming based approach for Modeling Time
Series data of real systems",
journal = "International Journal of Modern Physics C,
Computational Physics and Physical Computation",
year = "2008",
volume = "19",
number = "1",
pages = "63--91",
keywords = "genetic algorithms, genetic programming, Time series
analysis, artificial neural networks",
doi = "doi:10.1142/S0129183108011942",
abstract = "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.",
notes = "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",
}
@Article{2008Prama..71..459A,
author = "Dilip P. Ahalpara and Amit Verma and Jitendra C.
Parikh and Prasanta K. Panigrahi",
title = "Characterizing and modelling cyclic behaviour in
non-stationary time series through multi-resolution
analysis",
journal = "Pramana",
year = "2008",
month = nov,
volume = "71",
pages = "459--485",
publisher = "Springer India, in co-publication with Indian Academy
of Sciences",
keywords = "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",
ISSN = "0304-4289",
doi = "doi:10.1007/s12043-008-0125-x",
adsurl = "http://adsabs.harvard.edu/abs/2008Prama..71..459A",
adsnote = "Provided by the SAO/NASA Astrophysics Data System",
abstract = "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.",
notes = "(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",
}
@InProceedings{Ahalpara:2009:eurogp,
author = "Dilip Ahalpara and Siddharth Arora and M Santhanam",
title = "Genetic Programming Based Approach for Synchronization
with Parameter Mismatches in {EEG}",
booktitle = "Proceedings of the 12th European Conference on Genetic
Programming, EuroGP 2009",
year = "2009",
editor = "Leonardo Vanneschi and Steven Gustafson and Alberto
Moraglio and Ivanoe {De Falco} and Marc Ebner",
volume = "5481",
series = "LNCS",
pages = "13--24",
address = "Tuebingen",
month = apr # " 15-17",
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-01180-1",
doi = "doi:10.1007/978-3-642-01181-8_2",
notes = "Part of \cite{conf/eurogp/2009} EuroGP'2009 held in
conjunction with EvoCOP2009, EvoBIO2009 and
EvoWorkshops2009",
}
@InProceedings{Ahalpara:2010:gecco,
author = "Dilip P. Ahalpara",
title = "Improved forecasting of time series data of real
system using genetic programming",
booktitle = "GECCO '10: Proceedings of the 12th annual conference
on Genetic and evolutionary computation",
year = "2010",
editor = "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",
isbn13 = "978-1-4503-0072-8",
pages = "977--978",
keywords = "genetic algorithms, genetic programming, Poster",
month = "7-11 " # jul,
organisation = "SIGEVO",
address = "Portland, Oregon, USA",
doi = "doi:10.1145/1830483.1830658",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{1830658} GECCO-2010 A joint
meeting of the nineteenth international conference on
genetic algorithms (ICGA-2010) and the fifteenth annual
genetic programming conference (GP-2010)",
}
@InProceedings{ahalpara:2011:EuroGP,
author = "Dilip Ahalpara and Abhijit Sen",
title = "A Sniffer Technique for an Efficient Deduction of
Model Dynamical Equations using Genetic Programming",
booktitle = "Proceedings of the 14th European Conference on Genetic
Programming, EuroGP 2011",
year = "2011",
month = "27-29 " # apr,
editor = "Sara Silva and James A. Foster and Miguel Nicolau and
Mario Giacobini and Penousal Machado",
series = "LNCS",
volume = "6621",
publisher = "Springer Verlag",
address = "Turin, Italy",
pages = "1--12",
organisation = "EvoStar",
keywords = "genetic algorithms, genetic programming, local search,
hill climbing",
isbn13 = "978-3-642-20406-7",
doi = "doi:10.1007/978-3-642-20407-4_1",
abstract = "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.",
notes = "Mathematica. Order of partial or ordinary differential
equation search in sequence starting with first order
and increasing until satisfactory match found.
Part of \cite{Silva:2011:GP} EuroGP'2011 held in
conjunction with EvoCOP2011 EvoBIO2011 and
EvoApplications2011",
}
@Unpublished{ahlschwede:2000:ugppm,
author = "John Ahlschwede",
title = "Using Genetic Programming to Play Mancala",
year = "2000",
note = "http://www.corngolem.com/john/gp/index.html",
}
@InProceedings{ahluwalia:1996:ccpGP,
author = "Manu Ahluwalia and Terence C. Fogarty",
title = "Co-Evolving Classification Programs using Genetic
Programming",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "419",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
size = "1 page",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96",
}
@InProceedings{Ahluwalia:1997:,
author = "Manu Ahluwalia and Larry Bull and Terence C. Fogarty",
title = "Co-evolving Functions in Genetic Programming: {A}
Comparison in {ADF} Selection Strategies",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "3--8",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/Ahluwalia_1997_.pdf",
size = "6 pages",
notes = "GP-97",
}
@InProceedings{ahluwalia:1997:cfGPea,
author = "Manu Ahluwalia and Larry Bull and Terence C. Fogarty",
title = "Co-evolving Functions in Genetic Programming: An
Emergent Approach using {ADF}s and {GL}i{B}",
booktitle = "Late Breaking Papers at the 1997 Genetic Programming
Conference",
year = "1997",
editor = "John R. Koza",
pages = "1--6",
address = "Stanford University, CA, USA",
publisher_address = "Stanford University, Stanford, California,
94305-3079, USA",
month = "13--16 " # jul,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-206995-8",
notes = "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",
}
@InProceedings{ahluwalia:1998:cfGP:ADF+GLiB,
author = "M. Ahluwalia and L. Bull",
title = "Co-evolving Functions in Genetic Programming: Dynamic
{ADF} Creation using {GL}i{B}",
booktitle = "Evolutionary Programming VII: Proceedings of the
Seventh Annual Conference on Evolutionary Programming",
year = "1998",
editor = "V. William Porto and N. Saravanan and D. Waagen and A.
E. Eiben",
volume = "1447",
series = "LNCS",
pages = "809--818",
address = "Mission Valley Marriott, San Diego, California, USA",
publisher_address = "Berlin",
month = "25-27 " # mar,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-64891-7",
doi = "doi:10.1007/BFb0040753",
notes = "EP-98.
University of the West of England, UK",
}
@InProceedings{ahluwalia:1999:AGPCS,
author = "Manu Ahluwalia and Larry Bull",
title = "A Genetic Programming-based Classifier System",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "11--18",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, classifier
systems",
ISBN = "1-55860-611-4",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{ahluwalia:1999:CFGPCK,
author = "Manu Ahluwalia and Larry Bull",
title = "Coevolving Functions in Genetic Programming:
Classification using {K}-nearest-neighbour",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "947--952",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-413.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-413.pdf",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@Article{Ahluwalia:2001:SA,
author = "Manu Ahluwalia and Larry Bull",
title = "Coevolving functions in genetic programming",
journal = "Journal of Systems Architecture",
volume = "47",
pages = "573--585",
year = "2001",
number = "7",
month = jul,
keywords = "genetic algorithms, genetic programming, ADF,
Classification, EDF, Feature selection/extraction,
Hierarchical programs, Knn, Speciation",
ISSN = "1383-7621",
doi = "doi:10.1016/S1383-7621(01)00016-9",
URL = "http://www.sciencedirect.com/science/article/B6V1F-43RV156-3/1/16dd3ab5502922479ef7bb1ca4f7b9c3",
abstract = "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.",
}
@Article{Ahmad:2000:CCGc,
author = "Ishfaq Ahmad",
title = "Cluster Computing: Genetic programming in clusters",
journal = "IEEE Concurrency",
volume = "8",
number = "3",
pages = "10--11, 13",
month = jul # "\slash " # sep,
year = "2000",
CODEN = "IECMFX",
ISSN = "1092-3063",
bibdate = "Tue Jan 16 11:59:57 2001",
keywords = "genetic algorithms, genetic programming",
acknowledgement = ack-nhfb,
notes = "http://csdl.computer.org/comp/mags/pd/2000/03/p3toc.htm",
}
@Article{Aho97,
author = "Hannu Ahonen and Paulo A. {de Souza Jr.} and
Vijayendra Kumar Garg",
title = "A genetic algorithm for fitting Lorentzian line shapes
in Mossbauer spectra",
journal = "Nuclear Instruments and Methods in Physics Research
B",
year = "1997",
volume = "124",
pages = "633--638",
month = "5 " # may,
email = "souza@iacgu7.chemie.uni-mainz.de",
keywords = "genetic algorithms",
ISSN = "0168583X",
abstract = "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.",
}
@InProceedings{Aichour:2007:NICSO,
author = "Malek Aichour and Evelyne Lutton",
title = "Cooperative Co-evolution Inspired Operators for
Classical {GP} Schemes",
booktitle = "Proceedings of International Workshop on Nature
Inspired Cooperative Strategies for Optimization (NICSO
'07)",
year = "2007",
pages = "169--178",
editor = "Natalio Krasnogor and Giuseppe Nicosia and Mario
Pavone and David Pelta",
volume = "129",
series = "Studies in Computational Intelligence",
address = "Acireale, Italy",
month = "8-10 " # nov,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-540-78986-4",
}
@InProceedings{aiyarak:1997:GPtootn,
author = "P. Aiyarak and A. S. Saket and M. C. Sinclair",
title = "Genetic Programming Approaches for Minimum Cost
Topology Optimisation of Optical Telecommunication
Networks",
booktitle = "Second International Conference on Genetic Algorithms
in Engineering Systems: Innovations and Applications,
GALESIA",
year = "1997",
address = "University of Strathclyde, Glasgow, UK",
publisher_address = "Savoy Place, London, WC2R 0BL, UK",
month = "1-4 " # sep,
publisher = "IEE",
email = "mcs@essex.ac.uk",
keywords = "genetic algorithms, genetic programming,
telecommunication networks, topology",
ISBN = "0-85296-693-8",
URL = "http://uk.geocities.com/markcsinclair/ps/galesia97_aiy.ps.gz",
abstract = "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.",
notes = "GALESIA'97",
size = "6 pages",
}
@InCollection{akalin:2002:DCOFSGGP,
author = "Frederick R. Akalin",
title = "Developing a Computer-Controller Opponent for a
First-Person Simulation Game using Genetic
Programming",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2002",
year = "2002",
editor = "John R. Koza",
pages = "11--20",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "part of \cite{koza:2002:gagp}",
}
@InProceedings{Akbarzadeh:2008:fuzz,
author = "Vahab Akbarzadeh and Alireza Sadeghian and Marcus V.
{dos Santos}",
title = "Derivation of Relational Fuzzy Classification Rules
Using Evolutionary Computation",
booktitle = "2008 IEEE World Congress on Computational
Intelligence",
year = "2008",
editor = "Jun Wang",
pages = "1689--1693",
address = "Hong Kong",
month = "1-6 " # jun,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-1819-0",
file = "FS0398.pdf",
doi = "doi:10.1109/FUZZY.2008.4630598",
ISSN = "1098-7584",
keywords = "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",
abstract = "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.",
notes = "Also known as \cite{4630598} WCCI 2008 - A joint
meeting of the IEEE, the INNS, the EPS and the IET.",
}
@InProceedings{Akbarzadeh:1997:jce,
author = "M.-R. Akbarzadeh-T. and E. Tunstel and M. Jamshidi",
title = "Genetic Algorithms and Genetic Programming: Combining
Strength in One Evolutionary Strategy",
booktitle = "Proceedings of the 1997 WERC/HSRC Joint Conference on
the Environment",
year = "1997",
pages = "373--377",
address = "Albuquerque, NM, USA",
month = "26-29 " # apr,
organisation = "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",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Akbarzadeh_1997_jce.pdf",
size = "5 pages",
abstract = "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.",
}
@InProceedings{Akbarzadeh:1998:wcci,
author = "M. R. Akbarzadeh-T. and E. Tunstel and K. Kumbla and
M. Jamshidi",
title = "Soft computing paradigms for hybrid fuzzy controllers:
experiments and applications",
booktitle = "Proceedings of the 1998 IEEE World Congress on
Computational Intelligence",
year = "1998",
pages = "1200--1205",
volume = "2",
address = "Anchorage, Alaska, USA",
month = "5-9 " # may,
publisher = "IEEE Press",
keywords = "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",
ISBN = "0-7803-4863-X",
URL = "http://www-robotics.jpl.nasa.gov/people/Edward_Tunstel/fieee98.pdf",
URL = "http://ieeexplore.ieee.org/iel4/5612/15018/00686289.pdf?isNumber=15018",
size = "6 pages",
abstract = "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.",
notes = "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
World Congress on Computational Intelligence",
}
@Article{Akbarzadeh-T:2000:CEE,
author = "M.-R. Akbarzadeh-T. and K. Kumbla and E. Tunstel and
M. Jamshidi",
title = "Soft computing for autonomous robotic systems",
journal = "Computers and Electrical Engineering",
volume = "26",
pages = "5--32",
year = "2000",
number = "1",
keywords = "genetic algorithms, genetic programming, Soft
computing, Neural networks, Fuzzy logic, Robotic
control, Articial intelligence",
URL = "http://www.sciencedirect.com/science/article/B6V25-3Y6GXY5-2/1/6a6f9ff946815d4e95fe3884c98e74e5",
URL = "http://citeseer.ist.psu.edu/373353.html",
size = "28 pages",
abstract = "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.",
notes = "citeseer 373353 version not identical to published
version",
}
@InProceedings{Akbarzadeh:2003:ICNAFIPS,
author = "M.-R. Akbarzadeh-T. and I. Mosavat and S. Abbasi",
title = "Friendship Modeling for Cooperative Co-Evolutionary
Fuzzy Systems: {A} Hybrid {GA}-{GP} Algorithm",
booktitle = "2003 Proceedings of the 22nd International Conference
of North American Fuzzy Information Processing
Society",
year = "2003",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{Akira:1999:AJ,
author = "Yoshida Akira",
title = "Multiple-Organisms Learning and Evolution by Genetic
Programming",
booktitle = "Proceedings of The Third Australia-Japan Joint
Workshop on Intelligent and Evolutionary Systems",
year = "1999",
editor = "Bob McKay and Yasuhiro Tsujimura and Ruhul Sarker and
Akira Namatame and Xin Yao and Mitsuo Gen",
address = "School of Computer Science Australian Defence Force
Academy, Canberra, Australia",
month = "22-25 " # nov,
email = "akira-yo@is.aist-nara.ac.jp",
keywords = "genetic algorithms, genetic programming",
notes = "http://www.cs.adfa.edu.au/archive/conference/aj99/programme.html
Nara Advanced Institute of Science and Technology",
}
@InProceedings{akira:2000:moelGP,
author = "Yoshida Akira",
title = "Intraspecific Evolution of Learning by Genetic
Programming",
booktitle = "Genetic Programming, Proceedings of EuroGP'2000",
year = "2000",
editor = "Riccardo Poli and Wolfgang Banzhaf and William B.
Langdon and Julian F. Miller and Peter Nordin and
Terence C. Fogarty",
volume = "1802",
series = "LNCS",
pages = "209--224",
address = "Edinburgh",
publisher_address = "Berlin",
month = "15-16 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-67339-3",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=209",
notes = "EuroGP'2000, part of \cite{poli:2000:GP}",
}
@InProceedings{conf/ecsqaru/AkyolYE07,
title = "A Genetic Programming Classifier Design Approach for
Cell Images",
author = "Aydin Akyol and Yusuf Yaslan and Osman Kaan Erol",
booktitle = "Proceedings of the 9th European Conference on Symbolic
and Quantitative Approaches to Reasoning with
Uncertainty, ECSQARU",
bibdate = "2007-09-17",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/ecsqaru/ecsqaru2007.html#AkyolYE07",
publisher = "Springer",
year = "2007",
volume = "4724",
editor = "Khaled Mellouli",
isbn13 = "978-3-540-75255-4",
pages = "878--888",
series = "Lecture Notes in Computer Science",
address = "Hammamet, Tunisia",
month = oct # " 31 - " # nov # " 2",
keywords = "genetic algorithms, genetic programming, cell
classification, classifier design, pollen
classification",
doi = "doi:10.1007/978-3-540-75256-1_76",
abstract = "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.",
notes = "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",
}
@MastersThesis{Al-Afeef:mastersthesis,
author = "Ala' S. Al-Afeef",
title = "Image Reconstructing in Electrical Capacitance
Tomography of Manufacturing Processes Using Genetic
Programming",
school = "Al-Balqa Applied University",
year = "2010",
address = "Al-Salt, Jordan",
month = jul,
email = "alaa.afeef@gmail.com",
keywords = "genetic algorithms, genetic programming, Image
Reconstructing, Electrical Capacitance Tomography",
URL = "https://sites.google.com/site/alaaalfeef/home/Alaa_afeef_Thesis_Final.pdf",
URL = "http://wn.com/Al-Balqa%60_Applied_University#3",
URL = "http://il.youtube.com/watch?v=ecN6JogE4hU",
size = "137",
abstract = "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.",
}
@InProceedings{Al-Afeef:2010:ISDA,
author = "Alaa Al-Afeef and Alaa F. Sheta and Adnan Al-Rabea",
title = "Image reconstruction of a metal fill industrial
process using Genetic Programming",
booktitle = "10th International Conference on Intelligent Systems
Design and Applications (ISDA), 2010",
year = "2010",
pages = "12--17",
address = "Cairo",
month = "29 " # nov # "-1 " # dec,
email = "alaa.afeef@gmail.com",
keywords = "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",
isbn13 = "978-1-4244-8134-7",
URL = "http://sites.google.com/site/alaaalfeef/home/8.pdf",
doi = "doi:10.1109/ISDA.2010.5687299",
size = "6 pages",
abstract = "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.",
notes = "Also known as \cite{5687299}",
}
@Book{AfeefBook2011,
author = "Alaa Al-Afeef and Alaa Sheta and Adnan Rabea",
title = "Image Reconstruction of a Manufacturing Process: {A}
Genetic Programming Approach",
publisher = "Lambert Academic Publishing",
year = "2011",
edition = "1",
month = apr,
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3844325690",
URL = "https://www.morebooks.de/store/gb/book/image-reconstruction-of-a-manufacturing-process/isbn/978-3-8443-2569-0",
URL = "http://www.amazon.co.uk/Image-Reconstruction-Manufacturing-Process-Programming/dp/3844325697",
abstract = "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.",
size = "100 pages",
}
@InProceedings{Alagesan:2008:AHS,
author = "Shri Vidhya Alagesan and Sruthi Kannan and G. Shanthi
and A. P. Shanthi and Ranjani Parthasarathi",
title = "Intrinsic Evolution of Large Digital Circuits Using a
Modular Approach",
booktitle = "NASA/ESA Conference on Adaptive Hardware and Systems,
AHS '08",
year = "2008",
month = jun,
pages = "19--26",
keywords = "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",
doi = "doi:10.1109/AHS.2008.52",
abstract = "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.",
notes = "Also known as \cite{4584250}",
}
@TechReport{Alander:1995:ibGP,
author = "Jarmo T. Alander",
title = "An Indexed Bibliography of Genetic Programming",
institution = "Department of Information Technology and Industrial
Management, University of Vaasa",
year = "1995",
type = "Report Series no",
number = "94-1-GP",
address = "Finland",
URL = "ftp://ftp.uwasa.fi/cs/report94-1/gaGPbib.ps.Z",
keywords = "genetic algorithms, genetic programming",
abstract = "220 references. Indexed by subject, publication type
and author",
notes = "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).",
size = "46 pages",
}
@Book{Alander:1994:bib,
author = "Jarmo T. Alander",
title = "An Indexed Bibliography of Genetic Algorithms: Years
1957--1993",
year = "1994",
publisher = "Art of CAD ltd",
address = "Vaasa, Finland",
keywords = "genetic algorithms, genetic programming",
URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.53.4481&rep=rep1&type=pdf",
notes = "All GAs some 3000+ references",
}
@InProceedings{ga96fAlander,
annote = "*on,*FIN,genetic programming,mathematics /algebra",
author = "Jarmo T. Alander and Ghodrat Moghadampour and Jari
Ylinen",
title = "2nd order equation",
pages = "215--218",
year = "1996",
editor = "Jarmo T. Alander",
booktitle = "Proceedings of the Second Nordic Workshop on Genetic
Algorithms and their Applications (2NWGA)",
series = "Proceedings of the University of Vaasa, Nro. 13",
publisher = "University of Vaasa",
address = "Vaasa (Finland)",
month = "19.-23.~" # aug,
organisation = "Finnish Artificial Intelligence Society",
keywords = "genetic algorithms, genetic programming, mathematics,
algebra",
URL = "ftp://ftp.uwasa.fi/cs/2NWGA/Ghodrat2.ps.Z",
size = "4 pages",
abstract = "In this work we have tried to use genetic programming
to solve the simple second order equation",
notes = "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",
}
@Article{Alavi:2010:EwC,
author = "Amir Hossein Alavi and Amir Hossein Gandomi and
Mohammad Ghasem Sahab and Mostafa Gandomi",
title = "Multi Expression Programming: {A} New Approach to
Formulation of Soil Classification",
journal = "Engineering with Computers",
year = "2010",
volume = "26",
number = "2",
pages = "111--118",
month = apr,
email = "ah_alavi@hotmail.com, a.h.gandomi@gmail.com",
keywords = "genetic algorithms, genetic programming, Multi
expression programming, Soil classification,
Formulation",
URL = "http://www.springerlink.com/content/q418u58024054r38/",
doi = "doi:10.1007/s00366-009-0140-7",
size = "8 pages",
abstract = "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.",
notes = "M. Gandomi School of Civil Engineering, College of
Engineering, University of Tehran, Tehran, Iran",
}
@Article{Alavi:2010:ijcamieec,
author = "Amir Hossein Alavi and Amir Hossein Gandomi",
title = "A Robust Data Mining Approach for Formulation of
Geotechnical Engineering Systems",
journal = "International Journal of Computer Aided Methods in
Engineering-Engineering Computations",
year = "2011",
volume = "28",
number = "3",
pages = "242--274",
email = "ah_alavi@hotmail.com, a.h.gandomi@gmail.com",
keywords = "genetic algorithms, genetic programming, gene
expression programming, multi expression programming,
Data mining, Geotechnical engineering, Linear-based
genetic programming, Formulation",
ISSN = "0264-4401",
doi = "doi:10.1108/02644401111118132",
size = "33 pages",
abstract = "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.",
}
@Article{Alavi:2010:CBM,
author = "Amir Hossein Alavi and Mahmoud Ameri and Amir Hossein
Gandomi and Mohammad Reza Mirzahosseini",
title = "Formulation of Flow Number of Asphalt Mixes Using a
Hybrid Computational Method",
journal = "Construction and Building Materials",
year = "2011",
volume = "25",
number = "3",
pages = "1338--1355",
month = mar,
keywords = "genetic algorithms, genetic programming, Asphalt
concrete mixture, Flow number, Simulated annealing,
Marshall mix design, Regression analysis",
ISSN = "0950-0618",
doi = "doi:10.1016/j.conbuildmat.2010.09.010",
size = "18 pages",
abstract = "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.",
notes = "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",
}
@Article{Alavi20101239,
author = "A. H. Alavi and A. H. Gandomi and A. A. R. Heshmati",
title = "Discussion on {"}Soft computing approach for real-time
estimation of missing wave heights{"} by {S}.{N}.
Londhe [Ocean Engineering 35 (2008) 1080-1089]",
journal = "Ocean Engineering",
volume = "37",
number = "13",
pages = "1239--1240",
year = "2010",
ISSN = "0029-8018",
doi = "doi:10.1016/j.oceaneng.2010.06.003",
URL = "http://www.sciencedirect.com/science/article/B6V4F-50DXD90-1/2/b2489a1aebf49e771abca1b27d3b24b4",
keywords = "genetic algorithms, genetic programming, Tree
structure, Wave forecasts",
abstract = "The paper studied by Londhe (2008)
\cite{Londhe20081080} 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.",
}
@Article{Alavi2011,
author = "Amir Hossein Alavi and Pejman Aminian and Amir Hossein
Gandomi and Milad Arab Esmaeili",
title = "Genetic-based modeling of uplift capacity of suction
caissons",
journal = "Expert Systems with Applications",
volume = "In Press, Uncorrected Proof",
year = "2011",
ISSN = "0957-4174",
doi = "doi:10.1016/j.eswa.2011.04.049",
URL = "http://www.sciencedirect.com/science/article/B6V03-52P1KNK-4/2/f33267200d0fc51ad7a086befe3a361c",
keywords = "genetic algorithms, genetic programming, Gene
expression programming, Suction caissons, Uplift
capacity, Formulation",
abstract = "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.",
}
@InProceedings{alba:1996:tGPrdflc,
author = "Enrique Alba and Carlos Cotta and Jose M. Troya",
title = "Type-Constrained Genetic Programming for Rule-Base
Definition in Fuzzy Logic Controllers",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "255--260",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
size = "6 pages",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96",
}
@InProceedings{alba:1999:ERASPSPDGA,
author = "Enrique Alba and Carlos Cotta and Jose M. Troya",
title = "Entropic and Real-Time Analysis of the Search with
Panmictic, Structured, and Parallel Distributed Genetic
Algorithms",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "773",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems, poster
papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/Ga-808.pdf",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{alba:1999:T,
author = "Enrique Alba and Jose M. Troya",
title = "Tackling epistasis with panmictic and structured
genetic algorithms",
booktitle = "Late Breaking Papers at the 1999 Genetic and
Evolutionary Computation Conference",
year = "1999",
editor = "Scott Brave and Annie S. Wu",
pages = "1--7",
address = "Orlando, Florida, USA",
month = "13 " # jul,
keywords = "Genetic Algorithms, NK",
notes = "GECCO-99LB",
}
@Article{alba:1999:edflcSGP,
author = "Enrique Alba and Carlos Cotta and Jose M. Troya",
title = "Evolutionary Design of Fuzzy Logic Controllers Using
Strongly-Typed {GP}",
journal = "Mathware \& Soft Computing",
year = "1999",
volume = "6",
number = "1",
pages = "109--124",
keywords = "genetic algorithms, genetic programming, Type System,
Fuzzy Logic Controller, Cart-Centering Problem",
URL = "http://docto-si.ugr.es/Mathware/v6n1/PS/7-alba.ps.gz",
abstract = "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.",
notes = "Mathware and softcomputing
http://docto-si.ugr.es/Mathware/ENG/mathware.html",
}
@Book{Alba05,
author = "Enrique Alba",
title = "Parallel Metaheuristics: {A} New Class of Algorithms",
publisher = "John Wiley \& Sons",
month = aug,
year = "2005",
address = "NJ, USA",
ISBN = "0-471-67806-6",
keywords = "genetic algorithms, genetic programming, book, text,
general computer engineering",
URL = "http://www.ebookmall.com/ebooks/parallel-metaheuristics-a-new-class-of-algorithms-alba-ebooks.htm",
abstract = "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.",
notes = "US 95.",
size = "00584 pages",
}
@InCollection{Albuquerque:2004:EMTP,
author = "Ana Claudia M. L. Albuquerque and Jorge D. Melo and
Adriao D. {Doria Neto}",
title = "Evolutionary Computation and Parallel Processing
Applied to the Design of Multilayer Perceptrons",
year = "2004",
booktitle = "Evolvable Machines: Theory \& Practice",
pages = "181--203",
volume = "161",
series = "Studies in Fuzziness and Soft Computing",
chapter = "8",
editor = "Nadia Nedjah and Luiza {de Macedo Mourelle}",
publisher = "Springer",
address = "Berlin",
keywords = "genetic algorithms",
ISBN = "3-540-22905-1",
URL = "http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980449-0,00.html",
notes = "Springer says published in 2005 but available Nov
2004",
}
@InProceedings{albuquerque:2000:irfl,
author = "Paul Albuquerque and Bastien Chopard and Christian
Mazza and Marco Tomassini",
title = "On the Impact of the Representation on Fitness
Landscapes",
booktitle = "Genetic Programming, Proceedings of EuroGP'2000",
year = "2000",
editor = "Riccardo Poli and Wolfgang Banzhaf and William B.
Langdon and Julian F. Miller and Peter Nordin and
Terence C. Fogarty",
volume = "1802",
series = "LNCS",
pages = "1--15",
address = "Edinburgh",
publisher_address = "Berlin",
month = "15-16 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-67339-3",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=1",
abstract = "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.",
notes = "EuroGP'2000, part of \cite{poli:2000:GP}",
}
@InCollection{alderson:1999:TTCNDUEM,
author = "David Alderson",
title = "Toward a Technique for Cooperative Network Design
Using Evolutionary Methods",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1999",
year = "1999",
editor = "John R. Koza",
pages = "1--10",
address = "Stanford, California, 94305-3079 USA",
month = "15 " # mar,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms",
notes = "part of \cite{koza:1999:GAGPs}",
}
@InProceedings{aler:1998:5parity,
author = "Ricardo Aler",
title = "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",
booktitle = "Proceedings of the First European Workshop on Genetic
Programming",
year = "1998",
editor = "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer
and Terence C. Fogarty",
volume = "1391",
series = "LNCS",
pages = "60--70",
address = "Paris",
publisher_address = "Berlin",
month = "14-15 " # apr,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-64360-5",
abstract = "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.",
notes = "EuroGP'98",
}
@InProceedings{aler:1998:ehp,
author = "Ricardo Aler and Daniel Borrajo and Pedro Isasi",
title = "Evolved Heuristics for Planning",
booktitle = "Evolutionary Programming VII: Proceedings of the
Seventh Annual Conference on Evolutionary Programming",
year = "1998",
editor = "V. William Porto and N. Saravanan and D. Waagen and A.
E. Eiben",
volume = "1447",
series = "LNCS",
pages = "745--754",
address = "Mission Valley Marriott, San Diego, California, USA",
publisher_address = "Berlin",
month = "25-27 " # mar,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-64891-7",
doi = "doi:10.1007/BFb0040753",
notes = "EP-98
http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-64891-7
EvoCK compared with PRODIGY. HAMLET. Blocksworld
domain.",
}
@InProceedings{icml98-ricardo,
author = "Ricardo Aler and Daniel Borrajo and Pedro Isasi",
title = "Genetic Programming and Deductive-Inductive Learning:
{A} Multistrategy Approach",
booktitle = "Proceedings of the Fifteenth International Conference
on Machine Learning, ICML'98",
year = "1998",
editor = "Jude Shavlik",
pages = "10--18",
address = "Madison, Wisconsin, USA",
month = jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, Learning in
Planning, Multistrategy learning",
ISBN = "1-55860-556-8",
URL = "http://scalab.uc3m.es/~dborrajo/papers/icml98.ps.gz",
size = "9 pages",
abstract = "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.",
notes = "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",
}
@PhdThesis{aler:thesis,
author = "Ricardo Aler Mur",
title = "Programacion Genetica de Heuristicas para
Planificacion",
school = "Facultad de Informatica de la Universidad Politecnica
de Madrid",
year = "1999",
address = "Spain",
month = jul,
keywords = "genetic algorithms, genetic programming, Planning,
Problem Solving, Rule Based System",
size = "200 pages",
abstract = "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.",
notes = "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
",
}
@InProceedings{aler:2000:G,
author = "Ricardo Aler and Daniel Borrajo and Pedro Isasi",
title = "{GP} fitness functions to evolve heuristics for
planning",
booktitle = "Evolutionary Methods for AI Planning",
year = "2000",
editor = "Martin Middendorf",
pages = "189--195",
address = "Las Vegas, Nevada, USA",
month = "8 " # jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://scalab.uc3m.es/~dborrajo/papers/gecco00.ps.gz",
abstract = "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",
size = "5 pages",
notes = "GECCO-2000WKS Part of \cite{wu:2000:GECCOWKS}",
}
@InProceedings{oai:CiteSeerPSU:341634,
title = "Knowledge Representation Issues in Control Knowledge
Learning",
author = "Ricardo Aler and Daniel Borrajo and Pedro Isasi",
booktitle = "Proceedings of the Seventeenth International
Conference on Machine Learning (ICML 2000)",
year = "2000",
editor = "Pat Langley",
pages = "1--8",
address = "Stanford University, Standord, CA, USA",
month = jun # " 29 - " # jul # " 2",
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, EBL, HAMLET,
EVOCK",
ISBN = "1-55860-707-2",
bibsource = "DBLP, http://dblp.uni-trier.de",
URL = "http://scalab.uc3m.es/~dborrajo/papers/icml00.ps.gz",
URL = "http://citeseer.ist.psu.edu/341634.html",
citeseer-isreferencedby = "oai:CiteSeerPSU:42967",
citeseer-references = "oai:CiteSeerPSU:104987; oai:CiteSeerPSU:15322;
oai:CiteSeerPSU:554819",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:341634",
rights = "unrestricted",
size = "8 pages",
abstract = "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).",
notes = "http://www.informatik.uni-trier.de/~ley/db/conf/icml/icml2000.html",
}
@InProceedings{aler:2001:glckg,
author = "Ricardo Aler and Daniel Borrajo and Pedro Isasi",
title = "Grammars for Learning Control Knowledge with {GP}",
booktitle = "Proceedings of the 2001 Congress on Evolutionary
Computation CEC2001",
year = "2001",
pages = "1220--1227",
address = "COEX, World Trade Center, 159 Samseong-dong,
Gangnam-gu, Seoul, Korea",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "27-30 " # may,
organisation = "IEEE Neural Network Council (NNC), Evolutionary
Programming Society (EPS), Institution of Electrical
Engineers (IEE)",
publisher = "IEEE Press",
keywords = "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",
ISBN = "0-7803-6658-1",
URL = "http://scalab.uc3m.es/~dborrajo/papers/cec01.ps.gz",
size = "8 pages",
abstract = "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",
notes = "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",
}
@Article{aler:2001:ECJ,
author = "Ricardo Aler and Daniel Borrajo and Pedro Isasi",
title = "Learning to Solve Planning Problems Efficiently by
Means of Genetic Programming",
journal = "Evolutionary Computation",
year = "2001",
volume = "9",
number = "4",
pages = "387--420",
month = "Winter",
keywords = "genetic algorithms, genetic programming, genetic
planning, evolving heuristics, planning, search. EvoCK,
STGP, blocks world, logistics, Prodigy4.0, STRIPS,
PDL40.",
ISSN = "1063-6560",
URL = "http://www.mitpressjournals.org/doi/pdf/10.1162/10636560152642841",
doi = "doi:10.1162/10636560152642841",
size = "34 pages",
abstract = "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.",
}
@Article{aler:2002:AI,
author = "Ricardo Aler and Daniel Borrajo and Pedro Isasi",
title = "Using genetic programming to learn and improve control
knowledge",
journal = "Artificial Intelligence",
year = "2002",
volume = "141",
number = "1-2",
pages = "29--56",
month = oct,
keywords = "genetic algorithms, genetic programming, Speedup
learning, Multi-strategy learning, Planning",
URL = "http://scalab.uc3m.es/~dborrajo/papers/aij-evock.ps.gz",
URL = "http://citeseer.ist.psu.edu/511810.html",
doi = "doi:10.1016/S0004-3702(02)00246-1",
abstract = "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).",
notes = "Hamlet, EvoCK, PRODIGY 4.0",
}
@InProceedings{Aleshunas:2011:CAoUHiA,
title = "Cost-benefit Analysis of Using Heuristics in {ACGP}",
author = "John Aleshunas and Cezary Janikow",
pages = "1177--1183",
booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary
Computation",
year = "2011",
editor = "Alice E. Smith",
month = "5-8 " # jun,
address = "New Orleans, USA",
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "CEC2011 sponsored by the IEEE Computational
Intelligence Society, and previously sponsored by the
EPS and the IET.",
}
@InProceedings{Alexander:2009:cec,
author = "B. J. Alexander and M. J. Gratton",
title = "Constructing an Optimisation Phase Using Grammatical
Evolution",
booktitle = "2009 IEEE Congress on Evolutionary Computation",
year = "2009",
editor = "Andy Tyrrell",
pages = "1209--1216",
address = "Trondheim, Norway",
month = "18-21 " # may,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-2959-2",
file = "P395.pdf",
doi = "doi:10.1109/CEC.2009.4983083",
size = "8 pages",
abstract = "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.",
keywords = "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",
notes = "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
\cite{4983083}",
}
@PhdThesis{Alfaro-Cid:thesis,
author = "Maria Eva {Alfaro Cid}",
title = "Optimisation of Time Domain Controllers for Supply
Ships Using Genetic Algorithms and Genetic
Programming",
school = "The University of Glasgow",
year = "2003",
address = "Glasgow, UK",
month = oct,
keywords = "genetic algorithms, genetic programming",
URL = "http://casnew.iti.es/papers/ThesisEva.pdf",
size = "348 pages",
abstract = "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.",
}
@InProceedings{alfespshar05,
title = "Clasificaci\'{o}n de Senales de Electroencefalograma
Usando Programaci\'{o}n Gen\'{e}tica",
author = "Eva Alfaro-Cid and Anna Esparcia-Alc\'{a}zar and Ken
Sharman",
booktitle = "Actas del IV Congreso Espanol sobre
Metaheur\'{i}sticas, Algoritmos Evolutivos y
Bioinspirados ({MAEB}'05)",
month = sep,
address = "Granada, Spain",
year = "2005",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.iti.upv.es/cas/nade/data/maeb05vfinal.pdf",
notes = "in Spanish",
}
@InProceedings{eurogp:Alfaro-CidMM05,
author = "Eva Alfaro-Cid and Euan William McGookin and David
James Murray-Smith",
editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre
Collet and Jano I. {van Hemert} and Marco Tomassini",
title = "Evolution of a Strategy for Ship Guidance Using Two
Implementations of Genetic Programming",
booktitle = "Proceedings of the 8th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3447",
year = "2005",
address = "Lausanne, Switzerland",
month = "30 " # mar # " - 1 " # apr,
organisation = "EvoNet",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-25436-6",
pages = "250--260",
URL = "http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=250",
doi = "doi:10.1007/b107383",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in
conjunction with EvoCOP2005 and EvoWorkshops2005",
}
@InProceedings{conf/esann/Alfaro-CidES06,
title = "Using distributed genetic programming to evolve
classifiers for a brain computer interface",
author = "Eva Alfaro-Cid and Anna Esparcia-Alc{\'a}zar and Ken
Sharman",
year = "2006",
booktitle = "ESANN'2006 proceedings - European Symposium on
Artificial Neural Networks",
editor = "Michel Verleysen",
pages = "59--66",
address = "Bruges, Belgium",
month = "26-28 " # apr,
bibdate = "2006-08-30",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/esann/esann2006.html#Alfaro-CidES06",
keywords = "genetic algorithms, genetic programming",
ISBN = "2-930307-06-4",
URL = "http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2006-44.pdf",
abstract = "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",
notes = "http://www.dice.ucl.ac.be/Proceedings/esann/",
}
@InProceedings{Alfaro-Cid:2006:CEC,
author = "Eva Alfaro-Cid and Ken Sharman and Anna I.
Esparcia-Alcazar",
title = "Evolving a Learning Machine by Genetic Programming",
booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary
Computation",
year = "2006",
editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and
Simon M. Lucas",
pages = "958--962",
address = "Vancouver",
month = "6-21 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, simulated
annealing, function set, learning machine, learning
node, optimization algorithm, simulated annealing",
ISBN = "0-7803-9487-9",
doi = "doi:10.1109/CEC.2006.1688316",
size = "5 pages",
abstract = "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.",
notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
the IEE.
IEEE Catalog Number: 06TH8846D IEEE Xplore gives pages
as 254--258",
}
@InProceedings{alshaes2007a,
title = "Predicci\'{o}n de quiebra empresarial usando
programaci\'{o}n gen\'{e}tica",
author = "Eva {Alfaro Cid} and Ken Sharman and Anna I. {Esparcia
Alc\'{a}zar}",
booktitle = "Actas del V Congreso Espa{\~n}ol sobre
Metaheur\'{i}sticas, Algoritmos Evolutivos y
Bioinspirados ({MAEB}'07)",
month = "Febrero",
year = "2007",
pages = "703--710",
address = "Tenerife, Spain",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-84-690-3470-5",
notes = "in Spanish",
}
@InProceedings{alshaescu2007a,
title = "Aprendizaje autom\'{a}tico con programaci\'{o}n
gen\'{e}tica",
author = "Eva {Alfaro Cid} and Ken Sharman and Anna I. {Esparcia
Alc\'{a}zar} and Alberto {Cuesta Ca{\~n}ada}",
booktitle = "Actas del V Congreso Espa{\~n}ol sobre
Metaheur\'{i}sticas, Algoritmos Evolutivos y
Bioinspirados ({MAEB}'07)",
month = "Febrero",
year = "2007",
pages = "819--826",
address = "Tenerife, Spain",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-84-690-3470-5",
notes = "in Spanish",
}
@InProceedings{alfaro-cid:evows07,
author = "Eva Alfaro-Cid and Ken Sharman and Anna I.
Esparcia-Alc\`azar",
title = "A genetic programming approach for bankruptcy
prediction using a highly unbalanced database",
booktitle = "Applications of Evolutionary Computing,
EvoWorkshops2007: {EvoCOMNET}, {EvoFIN}, {EvoIASP},
{EvoInteraction}, {EvoMUSART}, {EvoSTOC},
{EvoTransLog}",
year = "2007",
month = "11-13 " # apr,
editor = "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",
series = "LNCS",
volume = "4448",
publisher = "Springer Verlag",
address = "Valencia, Spain",
pages = "169--178",
keywords = "genetic algorithms, genetic programming, SVM",
isbn13 = "978-3-540-71804-8",
doi = "doi:10.1007/978-3-540-71805-5_19",
abstract = "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.",
notes = "EvoWorkshops2007",
}
@InProceedings{conf/evoW/Alfaro-CidMGES08,
title = "A {SOM} and {GP} Tool for Reducing the Dimensionality
of a Financial Distress Prediction Problem",
author = "Eva Alfaro-Cid and Antonio Miguel Mora and Juan
Juli{\'a}n Merelo Guerv{\'o}s and Anna
Esparcia-Alc{\'a}zar and Ken Sharman",
bibdate = "2008-04-15",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/evoW/evoW2008.html#Alfaro-CidMGES08",
booktitle = "Proceedings of Evo{COMNET}, Evo{FIN}, Evo{HOT},
Evo{IASP}, Evo{MUSART}, Evo{NUM}, Evo{STOC}, and
EvoTransLog, Applications of Evolutionary Computing,
EvoWorkshops",
publisher = "Springer",
year = "2008",
volume = "4974",
editor = "Mario Giacobini and Anthony Brabazon and Stefano
Cagnoni and Gianni {Di Caro} and Rolf Drechsler and
Anik{\'o} Ek{\'a}rt and Anna Esparcia-Alc{\'a}zar 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",
isbn13 = "978-3-540-78760-0",
pages = "123--132",
series = "Lecture Notes in Computer Science",
doi = "doi:10.1007/978-3-540-78761-7_13",
address = "Naples",
month = "26-28 " # mar,
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{Alfaro-Cid:2008:cec,
author = "E. Alfaro-Cid and P. A. Castillo and A. Esparcia and
K. Sharman and J. J. Merelo and A. Prieto and J. L. J.
Laredo",
title = "Comparing Multiobjective Evolutionary Ensembles for
Minimizing Type {I} and {II} Errors for Bankruptcy
Prediction",
booktitle = "2008 IEEE World Congress on Computational
Intelligence",
year = "2008",
editor = "Jun Wang",
pages = "2902--2908",
address = "Hong Kong",
month = "1-6 " # jun,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-1823-7",
file = "EC0649.pdf",
doi = "doi:10.1109/CEC.2008.4631188",
abstract = "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.",
keywords = "genetic algorithms, genetic programming",
notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
EPS and the IET.",
}
@Article{Alfaro-Cid:2008:ieeeITS,
author = "Eva Alfaro-Cid and Euan W. McGookin and David J.
Murray-Smith and Thor I. Fossen",
title = "Genetic Programming for the Automatic Design of
Controllers for a Surface Ship",
journal = "IEEE Transactions on Intelligent Transportation
Systems",
year = "2008",
month = jun,
volume = "9",
number = "2",
pages = "311--321",
keywords = "genetic algorithms, genetic programming, control
system synthesis, navigation, propulsion, ships
CyberShip II, automatic design, controller structure,
navigation controllers, propulsion controllers, supply
ship, surface ship",
ISSN = "1524-9050",
doi = "doi:10.1109/TITS.2008.922932",
abstract = "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.",
notes = "Also known as \cite{4517335}",
}
@InProceedings{Alfaro-Cid:2008:HIS,
author = "Eva Alfaro-Cid and Anna Esparcia-Alcazar and Ken
Sharman and Francisco {Fernandez de Vega} and J. J.
Merelo",
title = "Prune and Plant: {A} New Bloat Control Method for
Genetic Programming",
booktitle = "Eighth International Conference on Hybrid Intelligent
Systems, HIS '08",
year = "2008",
month = sep,
pages = "31--35",
keywords = "genetic algorithms, genetic programming, bloat control
method, genetic operator, prune and plant, time
consumption, tree size reduction, mathematical
operators, trees (mathematics)",
doi = "doi:10.1109/HIS.2008.127",
abstract = "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.",
notes = "Also known as \cite{4626601}",
}
@InCollection{series/sci/Alfaro-CidCSE08,
title = "Strong Typing, Variable Reduction and Bloat Control
for Solving the Bankruptcy Prediction Problem Using
Genetic Programming",
author = "Eva Alfaro-Cid and Alberto Cuesta-Canada and Ken
Sharman and Anna Esparcia-Alcazar",
bibdate = "2008-08-26",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/series/sci/sci100.html#Alfaro-CidCSE08",
booktitle = "Natural Computing in Computational Finance",
publisher = "Springer",
year = "2008",
volume = "100",
editor = "Anthony Brabazon and Michael O'Neill",
isbn13 = "978-3-540-77476-1",
pages = "161--185",
series = "Studies in Computational Intelligence",
doi = "doi:10.1007/978-3-540-77477-8_9",
chapter = "9",
keywords = "genetic algorithms, genetic programming, STGP, SVM",
size = "29 pages",
abstract = "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.",
}
@InProceedings{Alfaro-Cid:2009:evonum,
title = "Modeling Pheromone Dispensers Using Genetic
Programming",
author = "Eva Alfaro-Cid and Anna I. Esparcia-Alc\'{a}zar and
Pilar Moya and Beatriu Femenia-Ferrer and Ken Sharman
and J. J. Merelo",
booktitle = "Applications of Evolutionary Computing, EvoWorkshops
2009: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES,
EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM,
EvoSTOC, EvoTRANSLOG",
editor = "Mario Giacobini and Anthony Brabazon and Stefano
Cagnoni and Gianni A. Di Caro and Anik{\'o} Ek{\'a}rt
and Anna Esparcia-Alc{\'a}zar 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",
volume = "5484",
series = "Lecture Notes in Computer Science",
address = "Tubingen, Germany",
year = "2009",
pages = "635--644",
month = apr # " 15-17",
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-01128-3",
bibsource = "DBLP, http://dblp.uni-trier.de",
doi = "doi:10.1007/978-3-642-01129-0_73",
size = "10 pages",
abstract = "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.",
notes = "ECJ.
EvoWorkshops2009 held in conjunction with EuroGP2009,
EvoCOP2009, EvoBIO2009",
}
@InProceedings{DBLP:conf/gecco/Alfaro-CidEMMFSP09,
author = "Eva Alfaro-Cid and Anna Esparcia-Alcazar and Pilar
Moya and J. J. Merelo and Beatriu Femenia-Ferrer and
Ken Sharman and Jaime Primo",
title = "Multiobjective genetic programming approach for a
smooth modeling of the release kinetics of a pheromone
dispenser",
booktitle = "GECCO-2009 Symbolic regression and modeling workshop
(SRM)",
year = "2009",
editor = "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",
pages = "2225--2230",
address = "Montreal",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "8-12 " # jul,
organisation = "SigEvo",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-60558-325-9",
bibsource = "DBLP, http://dblp.uni-trier.de",
doi = "doi:10.1145/1570256.1570309",
abstract = "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.",
notes = "Distributed on CD-ROM at GECCO-2009.
ACM Order Number 910092.",
}
@Article{Alfaro-Cid:2010:EC,
author = "Eva Alfaro-Cid and J. J. Merelo and Francisco
{Fernandez de Vega} and Anna I. Esparcia-Alcazar and
and Ken Sharman",
title = "Bloat Control Operators and Diversity in Genetic
Programming: {A} Comparative Study",
journal = "Evolutionary Computation",
year = "2010",
volume = "18",
number = "2",
pages = "305--332",
month = "Summer",
keywords = "genetic algorithms, genetic programming",
ISSN = "1063-6560",
doi = "doi:10.1162/evco.2010.18.2.18206",
abstract = "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.",
}
@Article{alfonseca:2004:GPEM,
author = "Manuel Alfonseca and Alfonso Ortega",
title = "Book Review: {Grammatical Evolution}: {Evolutionary}
Automatic Programming in an Arbitrary Language",
journal = "Genetic Programming and Evolvable Machines",
year = "2004",
volume = "5",
number = "4",
pages = "393",
month = dec,
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
ISSN = "1389-2576",
doi = "doi:10.1023/B:GENP.0000036057.27304.5b",
size = "1 page",
notes = "review of \cite{oneill:book}. Article ID: 5272973",
}
@InProceedings{Alhejali:2010:UKCI,
author = "Atif M. Alhejali and Simon M. Lucas",
title = "Evolving diverse Ms. Pac-Man playing agents using
genetic programming",
booktitle = "UK Workshop on Computational Intelligence (UKCI
2010)",
year = "2010",
month = "8-10 " # sep,
pages = "1--6",
abstract = "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.",
keywords = "genetic algorithms, genetic programming, Ms PacMan
game, reactive agents, computer games, learning
(artificial intelligence), software agents",
doi = "doi:10.1109/UKCI.2010.5625586",
notes = "Also known as \cite{5625586}",
}
@InProceedings{Alhejali:2011:CIG,
author = "Atif M. Alhejali and Simon M. Lucas",
title = "Using a Training Camp with Genetic Programming to
Evolve {Ms Pac-Man} Agents",
booktitle = "Proceedings of the 2011 IEEE Conference on
Computational Intelligence and Games",
year = "2011",
pages = "118--125",
address = "Seoul, South Korea",
month = "31 " # aug # " - 3 " # sep,
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming, Pac-Man,
Evolving Controllers, Decomposition learning, Training
camp",
URL = "http://cilab.sejong.ac.kr/cig2011/proceedings/CIG2011/papers/paper31.pdf",
size = "8 pages",
abstract = "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.",
}
@Article{ali:2004:GPEM,
author = "B. Ali and A. E. A. Almaini and T. Kalganova",
title = "Evolutionary Algorithms and Theirs Use in the Design
of Sequential Logic Circuits",
journal = "Genetic Programming and Evolvable Machines",
year = "2004",
volume = "5",
number = "1",
month = mar,
keywords = "genetic algorithms, evolvable hardware, sequential
circuits, state assignment",
ISSN = "1389-2576",
doi = "doi:10.1023/B:GENP.0000017009.11392.e2",
abstract = "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.",
notes = "Article ID: 5264733",
}
@InCollection{Ali:2008:GPTP,
author = "Mostafa Z. Ali and Robert G. Reynolds and Xiangdong
Che",
title = "Genetic Programming for Incentive-Based Design within
a Cultural Algorithms Framework",
booktitle = "Genetic Programming Theory and Practice {VI}",
year = "2008",
editor = "Rick L. Riolo and Terence Soule and Bill Worzel",
series = "Genetic and Evolutionary Computation",
chapter = "16",
pages = "249--269",
address = "Ann Arbor",
month = "15-17" # may,
publisher = "Springer",
size = "20 pages",
isbn13 = "978-0-387-87622-1",
notes = "part of \cite{Riolo:2008:GPTP} To be published late
2008",
keywords = "genetic algorithms, genetic programming",
}
@Article{Ali:2010:ieeeTSE,
author = "Shaukat Ali and Lionel C. Briand and Hadi Hemmati and
Rajwinder K. Panesar-Walawege",
title = "A Systematic Review of the Application and Empirical
Investigation of Search-Based Test-Case Generation",
journal = "IEEE Transactions on Software Engineering",
year = "2010",
volume = "36",
number = "6",
pages = "742--762",
month = nov # "-" # dec,
keywords = "genetic algorithms, genetic programming, SBSE",
ISSN = "0098-5589",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5210118&isnumber=4359463",
doi = "doi:10.1109/TSE.2009.52",
size = "22 pages",
abstract = "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.",
notes = "cites one GP paper:
\cite{Wappler:2007:ASE}.
TSESI-2008-09-0283",
}
@Article{AliGhorbani2010620,
author = "Mohammad Ali Ghorbani and Rahman Khatibi and Ali Aytek
and Oleg Makarynskyy and Jalal Shiri",
title = "Sea water level forecasting using genetic programming
and comparing the performance with Artificial Neural
Networks",
journal = "Computer \& Geosciences",
volume = "36",
number = "5",
pages = "620--627",
year = "2010",
ISSN = "0098-3004",
doi = "doi:10.1016/j.cageo.2009.09.014",
URL = "http://www.sciencedirect.com/science/article/B6V7D-4YCS020-1/2/514d629e145e62f37dbf599a1a7608a9",
keywords = "genetic algorithms, genetic programming, Sea-level
variations, Forecasting, Artificial Neural Networks,
Comparative studies",
abstract = "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.",
}
@InProceedings{Aljahdali:2010:AICCSA,
author = "Sultan Aljahdali and Alaa F. Sheta",
title = "Software effort estimation by tuning {COOCMO} model
parameters using differential evolution",
booktitle = "2010 IEEE/ACS International Conference on Computer
Systems and Applications (AICCSA)",
year = "2010",
month = "16-19 " # may,
address = "Hammamet, Tunisia",
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/AICCSA.2010.5586985",
notes = "'We suggest the use of Genetic Programming (GP)
technique to build suitable model structure for the
software effort estimation.' Also known as
\cite{5586985}",
}
@Article{Allen:2003:NB,
author = "Jess Allen and Hazel M. Davey and David Broadhurst and
Jim K. Heald and Jem J. Rowland and Stephen G. Oliver
and Douglas B. Kell",
title = "High-throughput classification of yeast mutants for
functional genomics using metabolic footprinting",
journal = "Nature Biotechnology",
year = "2003",
volume = "21",
number = "6",
pages = "692--696",
month = jun,
email = "dbk@umist.ac.uk",
keywords = "genetic algorithms, genetic programming",
URL = "http://dbkgroup.org/Papers/NatureBiotechnology21(692-696).pdf",
doi = "doi:10.1038/nbt823",
abstract = "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.",
}
@Article{Allen:2004:AEM,
author = "Jess Allen and Hazel M. Davey and David Broadhurst and
Jem J. Rowland and Stephen G. Oliver and Douglas B.
Kell",
title = "Discrimination of Modes of Action of Antifungal
Substances by Use of Metabolic Footprinting",
journal = "Applied and Environmental Microbiology",
year = "2004",
volume = "70",
number = "10",
pages = "6157--6165",
month = oct,
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1128/AEM.70.10.6157-6165.2004",
abstract = "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.",
notes = "PMID:",
}
@InProceedings{DBLP:conf/gecco/AllenBHK09,
author = "Sam Allen and Edmund K. Burke and Matthew R. Hyde and
Graham Kendall",
title = "Evolving reusable 3{D} packing heuristics with genetic
programming",
booktitle = "GECCO '09: Proceedings of the 11th Annual conference
on Genetic and evolutionary computation",
year = "2009",
editor = "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",
pages = "931--938",
address = "Montreal",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "8-12 " # jul,
organisation = "SigEvo",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-60558-325-9",
bibsource = "DBLP, http://dblp.uni-trier.de",
doi = "doi:10.1145/1569901.1570029",
abstract = "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.",
notes = "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.",
}
@InCollection{Almal:2005:GPTP,
author = "A. Almal and W. P. Worzel and E. A. Wollesen and C. D.
MacLean",
title = "Content Diversity in Genetic Programming and its
Correlation with Fitness",
booktitle = "Genetic Programming Theory and Practice {III}",
year = "2005",
editor = "Tina Yu and Rick L. Riolo and Bill Worzel",
volume = "9",
series = "Genetic Programming",
chapter = "12",
pages = "177--190",
address = "Ann Arbor",
month = "12-14 " # may,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, diversity,
chaos game, fitness correlation, visualisation",
ISBN = "0-387-28110-X",
size = "14 pages",
notes = "part of \cite{yu:2005:GPTP} Published Jan 2006 after
the workshop",
}
@InProceedings{1144040,
author = "Arpit A. Almal and Anirban P. Mitra and Ram H. Datar
and Peter F. Lenehan and David W. Fry and Richard J.
Cote and William P. Worzel",
title = "Using genetic programming to classify node positive
patients in bladder cancer",
booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference
on Genetic and evolutionary computation",
year = "2006",
editor = "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",
volume = "1",
ISBN = "1-59593-186-4",
pages = "239--246",
address = "Seattle, Washington, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p239.pdf",
doi = "doi:10.1145/1143997.1144040",
publisher = "ACM Press",
publisher_address = "New York, NY, 10286-1405, USA",
month = "8-12 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "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",
notes = "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",
}
@InCollection{Almal:2007:GPTP,
author = "A. A. Almal and C. D. MacLean and W. P. Worzel",
title = "Program Structure-Fitness Disconnect and Its Impact On
Evolution In {GP}",
booktitle = "Genetic Programming Theory and Practice {V}",
year = "2007",
editor = "Rick L. Riolo and Terence Soule and Bill Worzel",
series = "Genetic and Evolutionary Computation",
chapter = "9",
pages = "143--158",
address = "Ann Arbor",
month = "17-19" # may,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, phenotype,
genotype, evolutionary dynamics, GP structure, GP
content, speciation, population, fitness",
isbn13 = "978-0-387-76308-8",
doi = "doi:10.1007/978-0-387-76308-8_9",
size = "15 pages",
abstract = "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.",
notes = "part of \cite{Riolo:2007:GPTP} Published 2008",
}
@InCollection{Almal:2008:GPTP,
author = "A. A. Almal and C. D. MacLean and W. P. Worzel",
title = "A Population Based Study of Evolutionary Dynamics in
Genetic Programming",
booktitle = "Genetic Programming Theory and Practice {VI}",
year = "2008",
editor = "Rick L. Riolo and Terence Soule and Bill Worzel",
series = "Genetic and Evolutionary Computation",
chapter = "2",
pages = "19--29",
address = "Ann Arbor",
month = "15-17" # may,
publisher = "Springer",
size = "10 pages",
isbn13 = "978-0-387-87622-1",
notes = "part of \cite{Riolo:2008:GPTP} To be published late
2008",
keywords = "genetic algorithms, genetic programming",
}
@InCollection{almgren:2000:CADGP,
author = "Magnus Almgren",
title = "Communicating Agents Developed with Genetic
Programming",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2000",
year = "2000",
editor = "John R. Koza",
pages = "25--32",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "part of \cite{koza:2000:gagp}",
}
@InProceedings{Al-Mulla:2009:EMBC,
author = "M. R. Al-Mulla and F. Sepulveda and M. Colley and A.
Kattan",
title = "Classification of localized muscle fatigue with
genetic programming on s{EMG} during isometric
contraction",
booktitle = "Annual International Conference of the IEEE
Engineering in Medicine and Biology Society, EMBC
2009",
year = "2009",
month = "2-6 " # sep,
address = "Minneapolis, Minnesota, USA",
pages = "2633--2638",
keywords = "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",
doi = "doi:10.1109/IEMBS.2009.5335368",
ISSN = "1557-170X",
abstract = "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.",
notes = "Also known as \cite{5335368}",
}
@Article{Al-Mulla:2011:MEP,
author = "Mohamed R. Al-Mulla and Francisco Sepulveda and M.
Colley",
title = "Evolved pseudo-wavelet function to optimally decompose
s{EMG} for automated classification of localized muscle
fatigue",
journal = "Medical Engineering and Physics",
year = "2011",
volume = "33",
number = "4",
pages = "411--417",
month = may,
keywords = "genetic algorithms, Localized muscle fatigue, sEMG,
Wavelet analysis, matlab",
doi = "doi:10.1016/j.medengphy.2010.11.008",
abstract = "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",
}
@InProceedings{Alonso:2008:ieeeICTAI,
author = "Cesar L. Alonso and Jorge Puente and Jose Luis
Montana",
title = "Straight Line Programs: {A} New Linear Genetic
Programming Approach",
booktitle = "20th IEEE International Conference on Tools with
Artificial Intelligence, ICTAI '08",
year = "2008",
month = nov,
volume = "2",
pages = "517--524",
keywords = "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",
doi = "doi:10.1109/ICTAI.2008.14",
ISSN = "1082-3409",
abstract = "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.",
notes = "Also known as \cite{4669818}",
}
@Article{Alonso:2009:IJAIT,
author = "Cesar L. Alonso and Jose Luis Montana and Jorge Puente
and Cruz Enrique Borges",
title = "A new Linear Genetic Programming approach based on
straight line programs: some Theoretical and
Experimental Aspects",
journal = "International Journal on Artificial Intelligence
Tools",
year = "2009",
volume = "18",
number = "5",
pages = "757--781",
keywords = "genetic algorithms, genetic programming, slp,
Vapnik-Chervonenkis dimension, VC",
doi = "doi:10.1142/S0218213009000391",
abstract = "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.",
notes = "IJAIT",
}
@InProceedings{Alonso:2009:ICTAI,
author = "Cesar L. Alonso and Jose Luis Montana and Cruz Enrique
Borges",
title = "Evolution Strategies for Constants Optimization in
Genetic Programming",
booktitle = "21st International Conference on Tools with Artificial
Intelligence, ICTAI '09",
year = "2009",
month = nov,
pages = "703--707",
keywords = "genetic algorithms, genetic programming, computer
program, constants optimization, evolutionary
computation methods, learning problems, linear genetic
programming approach, symbolic regression problem,
regression analysis",
doi = "doi:10.1109/ICTAI.2009.35",
ISSN = "1082-3409",
abstract = "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.",
notes = "Also known as \cite{5366517}",
}
@InProceedings{conf/incdm/AlonsoMPSV08,
title = "Modelling Medical Time Series Using Grammar-Guided
Genetic Programming",
author = "Fernando Alonso and Loic Martinez and Aurora
Perez-Perez and Agustin Santamaria and Juan Pedro
Valente",
bibdate = "2010-02-01",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/incdm/incdm2008.html#AlonsoMPSV08",
booktitle = "8th Industrial Conference in Data Mining, Medical
Applications, E-Commerce, Marketing and Theoretical
Aspects, ICDM 2008",
publisher = "Springer",
year = "2008",
volume = "5077",
editor = "Petra Perner",
isbn13 = "978-3-540-70717-2",
pages = "32--46",
series = "Lecture Notes in Computer Science",
doi = "doi:10.1007/978-3-540-70720-2_3",
address = "Leipzig, Germany",
month = jul # " 16-18",
keywords = "genetic algorithms, genetic programming, Time series
characterization, isokinetics, symbolic distance,
information extraction, reference model, text mining",
size = "15 pages",
abstract = "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.",
notes = "Context Free Grammar",
}
@InProceedings{Alonso:2010:gecco,
author = "Fernando Alonso and Loic Martinez and Agustin
Santamaria and Aurora Perez and Juan Pedro Valente",
title = "{GGGP}-based method for modeling time series: operator
selection, parameter optimization and expert
evaluation",
booktitle = "GECCO '10: Proceedings of the 12th annual conference
on Genetic and evolutionary computation",
year = "2010",
editor = "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",
isbn13 = "978-1-4503-0072-8",
pages = "989--990",
keywords = "genetic algorithms, genetic programming,
grammar-guided genetic programming, Poster",
month = "7-11 " # jul,
organisation = "SIGEVO",
address = "Portland, Oregon, USA",
doi = "doi:10.1145/1830483.1830664",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{1830664} GECCO-2010 A joint
meeting of the nineteenth international conference on
genetic algorithms (ICGA-2010) and the fifteenth annual
genetic programming conference (GP-2010)",
}
@Article{Al-Rabadi:2006:EPB,
author = "Anas N. Al-Rabadi",
title = "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.}",
journal = "The Computer Journal",
volume = "49",
number = "1",
pages = "129--130",
month = jan,
year = "2006",
CODEN = "CMPJA6",
ISSN = "0010-4620",
bibdate = "Wed Dec 21 17:38:55 MST 2005",
bibsource = "http://comjnl.oxfordjournals.org/content/vol49/issue1/index.dtl",
URL = "http://comjnl.oxfordjournals.org/cgi/content/full/49/1/129;
http://comjnl.oxfordjournals.org/cgi/reprint/49/1/129",
acknowledgement = "ack-nhfb",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1093/comjnl/bxh134",
notes = "review of \cite{spector:book}",
}
@InProceedings{eurogp:Al-SakranKJ05,
author = "Sameer H. Al-Sakran and John R. Koza and Lee W.
Jones",
editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre
Collet and Jano I. {van Hemert} and Marco Tomassini",
title = "Automated Re-invention of a Previously Patented
Optical Lens System Using Genetic Programming",
booktitle = "Proceedings of the 8th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3447",
year = "2005",
address = "Lausanne, Switzerland",
month = "30 " # mar # " - 1 " # apr,
organisation = "EvoNet",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-25436-6",
pages = "25--37",
URL = "http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=25",
doi = "doi:10.1007/b107383",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in
conjunction with EvoCOP2005 and EvoWorkshops2005",
}
@Article{Alsberg:2000:CILS,
author = "Bjorn K. Alsberg and Nathalie Marchand-Geneste and
Ross D. King",
title = "A new 3{D} molecular structure representation using
quantum topology with application to structure-property
relationships",
journal = "Chemometrics and Intelligent Laboratory Systems",
year = "2000",
volume = "54",
pages = "75--91",
number = "2",
keywords = "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",
ISSN = "0169-7439",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6TFP-426XTF7-1/2/36265a259de8f80d4918ee6612612218",
doi = "doi:10.1016/S0169-7439(00)00101-5",
abstract = "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.",
}
@InProceedings{DBLP:conf/gecco/AlshammariLHZ09,
author = "Riyad Alshammari and Peter Lichodzijewski and Malcolm
I. Heywood and A. Nur Zincir-Heywood",
title = "Classifying {SSH} encrypted traffic with minimum
packet header features using genetic programming",
booktitle = "GECCO-2009 Defense applications of computational
intelligence workshop",
year = "2009",
editor = "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",
pages = "2539--2546",
address = "Montreal",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "8-12 " # jul,
organisation = "SigEvo",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-60558-325-9",
bibsource = "DBLP, http://dblp.uni-trier.de",
doi = "doi:10.1145/1570256.1570358",
abstract = "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.
",
notes = "Distributed on CD-ROM at GECCO-2009.
ACM Order Number 910092.",
}
@InProceedings{Alshammari:2010:cec,
author = "Riyad Alshammari and A. Nur Zincir-Heywood",
title = "Unveiling Skype encrypted tunnels using {GP}",
booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-6910-9",
abstract = "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.",
doi = "doi:10.1109/CEC.2010.5586288",
notes = "WCCI 2010. Also known as \cite{5586288}",
}
@InProceedings{Alshammari:2010:CNSM,
author = "Riyad Alshammari and A. Nur Zincir-Heywood",
title = "An investigation on the identification of {VoIP}
traffic: Case study on Gtalk and Skype",
booktitle = "2010 International Conference on Network and Service
Management (CNSM)",
year = "2010",
month = "25-29 " # oct,
pages = "310--313",
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/CNSM.2010.5691210",
notes = "Also known as \cite{5691210}",
}
@InProceedings{Alshammari:2011:IMLltbtptsaVt,
title = "Is Machine Learning losing the battle to produce
transportable signatures against Vo{IP} traffic?",
author = "Riyad Alshammari and A. Nur Zincir-Heywood",
pages = "1542--1549",
booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary
Computation",
year = "2011",
editor = "Alice E. Smith",
month = "5-8 " # jun,
address = "New Orleans, USA",
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming, Related Areas
and Applications:Testing Evolutionary Algorithms on
Real-world Numerical Optimisation Problems",
abstract = "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.",
notes = "CEC2011 sponsored by the IEEE Computational
Intelligence Society, and previously sponsored by the
EPS and the IET.",
}
@InProceedings{Alsulaiman:2009:ieeeCISDA,
author = "Fawaz A. Alsulaiman and Nizar Sakr and Julio J. Valdes
and Abdulmotaleb {El Saddik} and Nicolas D. Georganas",
title = "Feature selection and classification in genetic
programming: Application to haptic-based biometric
data",
booktitle = "IEEE Symposium on Computational Intelligence for
Security and Defense Applications, CISDA 2009",
year = "2009",
month = jul,
pages = "1--7",
keywords = "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",
doi = "doi:10.1109/CISDA.2009.5356540",
abstract = "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.",
notes = "Also known as \cite{5356540}",
}
@InCollection{kinnear:altenberg,
author = "Lee Altenberg",
title = "The Evolution of Evolvability in Genetic Programming",
booktitle = "Advances in Genetic Programming",
publisher = "MIT Press",
year = "1994",
editor = "Kenneth E. {Kinnear, Jr.}",
pages = "47--74",
chapter = "3",
keywords = "genetic algorithms, genetic programming",
URL = "http://dynamics.org/~altenber/PAPERS/EEGP/",
URL = "http://dynamics.org/Altenberg/FILES/LeeEEGP.pdf",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262111888",
abstract = "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",
notes = "
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 \cite{Altenberg:1994EEGP}",
size = "29 pages",
}
@InProceedings{Altenberg:1994EBR,
author = "Lee Altenberg",
year = "1994",
pages = "182--187",
title = "Evolving better representations through selective
genome growth",
booktitle = "Proceedings of the 1st IEEE Conference on Evolutionary
Computation",
publisher = "IEEE",
address = "Orlando, Florida, USA",
month = "27-29 " # jun,
publisher_address = "Piscataway, NJ, USA",
volume = "1",
keywords = "genetic algorithms, genetic programming",
URL = "http://dynamics.org/~altenber/PAPERS/EBR/",
URL = "http://dynamics.org/Altenberg/FILES/LeeEBR.pdf",
abstract = "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",
notes = "
",
}
@InProceedings{Altenberg:1994EPIGP,
author = "Lee Altenberg",
year = "1994",
title = "Emergent phenomena in genetic programming",
booktitle = "Evolutionary Programming --- Proceedings of the Third
Annual Conference",
editor = "Anthony V. Sebald and Lawrence J. Fogel",
publisher = "World Scientific Publishing",
pages = "233--241",
address = "San Diego, CA, USA",
month = "24-26 " # feb,
keywords = "genetic algorithms, genetic programming",
ISBN = "981-02-1810-9",
URL = "http://dynamics.org/~altenber/PAPERS/EPIGP/",
URL = "http://dynamics.org/Altenberg/FILES/LeeEPIGP.pdf",
URL = "http://dynamics.org/~altenber/FTP/LeeEPIGP.ps",
URL = "http://citeseer.ist.psu.edu/398393.html",
abstract = "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",
notes = "
EP-94 http://www.wspc.com.sg/books/compsci/2401.html
http://www.natural-selection.com/eps/EP94.html",
}
@InProceedings{Altenberg:1995STPT,
author = "Lee Altenberg",
year = "1994",
title = "The {Schema} {Theorem} and {Price}'s {Theorem}",
booktitle = "Foundations of Genetic Algorithms 3",
editor = "L. Darrell Whitley and Michael D. Vose",
publisher = "Morgan Kaufmann",
publisher_address = "San Francisco, CA, USA",
address = "Estes Park, Colorado, USA",
pages = "23--49",
month = "31 " # jul # "--2 " # aug,
organisation = "International Society for Genetic Algorithms",
note = "Published 1995",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-356-5",
URL = "http://dynamics.org/~altenber/PAPERS/STPT/",
URL = "http://dynamics.org/Altenberg/FILES/LeeSTPT.pdf",
abstract = "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",
notes = "FOGA-3
Deals with GAs as a whole, not specifically GP.",
}
@InCollection{Altenberg:1995GGEGPM,
author = "Lee Altenberg",
year = "1995",
title = "Genome growth and the evolution of the
genotype-phenotype map",
booktitle = "Evolution as a Computational Process",
editor = "Wolfgang Banzhaf and Frank H. Eeckman",
publisher = "Springer-Verlag",
address = "Berlin, Germany",
pages = "205--259",
keywords = "genetic algorithms, genetic programming",
URL = "http://dynamics.org/~altenber/PAPERS/GGEGPM/",
URL = "http://dynamics.org/Altenberg/FILES/LeeGGEGPM.pdf",
size = "55 pages",
abstract = "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",
notes = "
",
}
@Unpublished{Altenberg:and:Feldman:1995SGTEMG2,
author = "Lee Altenberg and Marcus W. Feldman",
year = "1995",
title = "Selection, generalized transmission, and the evolution
of modifier genes. {II}. {M}odifier polymorphisms",
note = "In preparation",
URL = "ftp://ftp.mhpcc.edu/pub/incoming/altenberg/LeeSGTEMG2MP.ps.Z",
notes = "
",
}
@InCollection{Altenberg:2004:MESLLQ,
title = "Modularity in Evolution: Some Low-Level Questions",
author = "Lee Altenberg",
booktitle = "Modularity: Understanding the Development and
Evolution of Complex Natural Systems",
editor = "Diego Rasskin-Gutman and Werner Callebaut",
publisher = "MIT Press",
address = "Cambridge, MA, USA",
year = "2005",
chapter = "5",
pages = "99--128",
month = jun,
keywords = "genetic algorithms, genetic programming",
ISBN = "0-262-03326-7",
URL = "http://dynamics.org/Altenberg/FILES/LeeMESLLQ.pdf",
abstract = "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.",
notes = "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",
size = "32 pages",
}
@InCollection{Altenberg:2004:OPSAED,
title = "Open Problems in the Spectral Analysis of Evolutionary
Dynamics",
author = "Lee Altenberg",
booktitle = "Frontiers of Evolutionary Computation",
editor = "Anil Menon",
series = "Genetic Algorithms And Evolutionary Computation
Series",
volume = "11",
chapter = "4",
publisher = "Kluwer Academic Publishers",
address = "Boston, MA, USA",
year = "2004",
pages = "73--99",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-4020-7524-3",
URL = "http://dynamics.org/Altenberg/FILES/LeeOPSAED.pdf",
abstract = "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?",
notes = "Revised 2010",
size = "26 pages",
}
@Article{altenberg:2004:ESSFSA,
author = "Lee Altenberg",
year = "2005",
title = "Evolvability Suppression to Stabilize Far-Sighted
Adaptations",
journal = "Artificial Life",
volume = "11",
number = "3",
pages = "427--443",
month = "Fall",
keywords = "genetic algorithms",
ISSN = "1064-5462",
doi = "doi:10.1162/106454605774270633",
size = "18 pages",
abstract = "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.",
}
@Article{Alvarez:2007:JMS,
author = "A. Alvarez and Alejandro Orfila and G. Basterretxea
and J. Tintore and G. Vizoso and A. Fornes",
title = "Forecasting front displacements with a satellite based
ocean forecasting ({SOFT}) system",
journal = "Journal of Marine Systems",
year = "2007",
volume = "65",
number = "1-4",
pages = "299--313",
month = mar,
note = "Marine Environmental Monitoring and Prediction -
Selected papers from the 36th International Liege
Colloquium on Ocean Dynamics",
keywords = "genetic algorithms, genetic programming, Satellite
data, Ocean prediction, Front evolution",
doi = "doi:10.1016/j.jmarsys.2005.11.017",
abstract = "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.",
}
@InCollection{alvarez:2003:SVMASTI,
author = "Gabriel Alvarez",
title = "Standard Versus Micro-Genetic Algorithms for Seismic
Trace Inversion",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2003",
year = "2003",
editor = "John R. Koza",
pages = "1--10",
address = "Stanford, California, 94305-3079 USA",
month = "4 " # dec,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms",
notes = "part of \cite{koza:2003:gagp}",
}
@InProceedings{alvarez:1998:,
author = "Luis F. Alvarez and Vassili V. Toropov",
title = "Application of Genetic Programming to the Choice of a
Structure of Global Approximations",
booktitle = "Late Breaking Papers at the Genetic Programming 1998
Conference",
year = "1998",
editor = "John R. Koza",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "Stanford, CA, USA",
month = "22-25 " # jul,
publisher = "Stanford University Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "GP-98LB",
}
@InProceedings{oai:CiteSeerPSU:512359,
author = "Luis F. Alvarez and Vassili V. Toropov and David C.
Hughes and Ashraf F. Ashour",
title = "Approximation model building using genetic programming
methodology: applications",
booktitle = "Second ISSMO/AIAA Internet Conference on
Approximations and Fast Reanalysis in Engineering
Optimization",
year = "2000",
editor = "Thouraya Baranger and Fred van Keulen",
month = "25 " # may # "-2 " # jun,
keywords = "genetic algorithms, genetic programming",
URL = "http://www-tm.wbmt.tudelft.nl/~wbtmavk/2aro_conf/Toropov/Fred4.pdf",
URL = "http://www-tm.wbmt.tudelft.nl/~wbtmavk/2aro_conf/Toropov/fred.html",
URL = "http://www-tm.wbmt.tudelft.nl/~wbtmavk/2aro_conf/Toropov/FRED4.PS",
URL = "http://citeseer.ist.psu.edu/512359.html",
citeseer-isreferencedby = "oai:CiteSeerPSU:81525",
citeseer-references = "oai:CiteSeerPSU:60878",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:512359",
rights = "unrestricted",
abstract = "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.",
notes = "Multicriteria Optimization of the Manufacturing
Process for Roman Cement",
}
@Article{Alvarez-Diaz:2003:ael,
author = "Marcos Alvarez-Diaz and Alberto Alvarez",
title = "Forecasting exchange rates using genetic algorithms",
journal = "Applied Economics Letters",
year = "2003",
volume = "10",
number = "6",
pages = "319--322",
month = apr,
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1080/13504850210158250",
abstract = "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.",
}
@Article{Alvarez-Diaz:2005:EE,
author = "Marcos Alvarez-Diaz and Alberto Alvarez",
title = "Genetic multi-model composite forecast for non-linear
prediction of exchange rates",
journal = "Empirical Economics",
year = "2005",
volume = "30",
number = "3",
pages = "643--663",
month = oct,
keywords = "genetic algorithms, genetic programming,
Composite-forecast or data-fusion, neural networks,
exchange-rate forecasting",
ISSN = "0377-7332",
doi = "doi:10.1007/s00181-005-0249-5",
abstract = "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.",
}
@Article{Alvarez-Diaz:2006:jbe,
author = "Marcos Alvarez-Diaz and Marcos Dominquez-Torreiro",
title = "Using Genetic Algorithms to Estimate and Validate
Bioeconomic Models: The Case of the Ibero-atlantic
Sardine Fishery",
journal = "Journal of Bioeconomics",
year = "2006",
volume = "8",
number = "1",
pages = "55--65",
month = apr,
keywords = "genetic algorithms, genetic programming, bioeconomic
modeling, linear and non-linear forecasting",
ISSN = "1387-6996",
doi = "doi:10.1007/s10818-005-0494-x",
abstract = "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.",
notes = "
p 64 {"}Unlike a uni-variant analysis, DARWIN now
allows us to look for functional relationships between
two or more time-series.{"}",
}
@Article{AlvarezDiaz2008161,
author = "Marcos Alvarez-Diaz and Gonzalo {Caballero Miguez}",
title = "The quality of institutions: {A} genetic programming
approach",
journal = "Economic Modelling",
volume = "25",
number = "1",
pages = "161--169",
year = "2008",
ISSN = "0264-9993",
doi = "doi:10.1016/j.econmod.2007.05.001",
URL = "http://www.sciencedirect.com/science/article/B6VB1-4P0VD80-1/2/c0bb8da3af64aa1ea6b0a4f90e4790b0",
keywords = "genetic algorithms, genetic programming, Quality of
institutions, Institutional determinants,
Non-parametric perspective",
abstract = "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.",
}
@TechReport{Alvarez-Diaz:funcas401,
author = "Marcos Alvarez-Diaz and Gonzalo {Caballero Miguez} and
Mario Solino",
title = "The institutional determinants of {CO2} emissions: {A}
computational modelling approach using Artificial
Neural Networks and Genetic Programming",
institution = "Fundacion de las Cajas de Ahorros",
year = "2008",
type = "FUNCAS Working Paper",
number = "401",
address = "Madrid",
month = jul,
keywords = "genetic algorithms, genetic programming, ANN",
URL = "http://www.funcas.es/Publicaciones/InformacionArticulos/Publicaciones.asp?ID=1411",
}
@Article{Alvarez-Diaz:2009:IJCEE,
title = "Forecasting tourist arrivals to {Balearic} {Islands}
using genetic programming",
author = "Marcos Alvarez-Diaz and Josep Mateu-Sbert and Jaume
Rossello-Nadal",
year = "2009",
volume = "1",
journal = "International Journal of Computational Economics and
Econometrics",
number = "1",
pages = "64--75",
month = nov # "~06",
keywords = "genetic algorithms, genetic programming, tourism
forecasting, Diebold-Mariano test, tourist arrivals,
Balearic Islands, UK, United Kingdom, Germany, Spain",
URL = "http://www.inderscience.com/link.php?id=29153",
doi = "doi:10.1504/IJCEE.2009.029153",
publisher = "Inderscience Publishers",
ISSN = "1757-1189",
bibsource = "OAI-PMH server at www.inderscience.com",
abstract = "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.",
}
@Article{AlvarezDiaz2009,
author = "Marcos {Alvarez Diaz} and Manuel Gonzalez Gomez and
Angeles Saavedra Gonzalez and Jacobo {De Una Alvarez}",
title = "On dichotomous choice contingent valuation data
analysis: Semiparametric methods and Genetic
Programming",
journal = "Journal of Forest Economics",
year = "2010",
volume = "16",
number = "2",
pages = "145--156",
month = apr,
ISSN = "1104-6899",
doi = "doi:10.1016/j.jfe.2009.02.002",
URL = "http://www.sciencedirect.com/science/article/B7GJ5-4XY3F46-1/2/d98566d6ee97a4f7f2c2f1b9deb29bc1",
keywords = "genetic algorithms, genetic programming, Dichotomous
choice contingent valuation, Genetic program,
Parametric techniques, Proportional hazard model",
size = "12 pages",
abstract = "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.",
}
@Article{Alvarez-Diaz:2010:AEL,
title = "Forecasting exchange rates using local regression",
author = "Marcos Alvarez-Diaz and Alberto Alvarez",
journal = "Applied Economics Letters",
year = "2010",
volume = "17",
number = "5",
pages = "509--514",
month = mar,
keywords = "genetic algorithms, genetic programming, local
search",
doi = "doi:0.1080/13504850801987217",
oai = "oai:RePEc:taf:apeclt:v:17:y:2010:i:5:p:509-514",
abstract = "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.",
notes = "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.",
}
@Article{Alvarez-Diaz:2010:AFE,
author = "Marcos {Alvarez Diaz}",
title = "Speculative strategies in the foreign exchange market
based on genetic programming predictions",
journal = "Applied Financial Economics",
year = "2010",
volume = "20",
number = "6",
pages = "465--476",
month = mar,
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1080/09603100903459782",
oai = "oai:RePEc:taf:apfiec:v:20:y:2010:i:6:p:465-476",
abstract = "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.",
notes = "Department of Economics, University of Vigo, Galicia,
Spain",
}
@InProceedings{amarteifio:2004:AL,
author = "Saoirse Amarteifio and Michael O'Neill",
title = "An Evolutionary Approach to Complex System Regulation
Using Grammatical Evolution",
booktitle = "Artificial Life {XI} Ninth International Conference on
the Simulation and Synthesis of Living Systems",
year = "2004",
editor = "Jordan Pollack and Mark Bedau and Phil Husbands and
Takashi Ikegami and Richard A. Watson",
pages = "551--556",
address = "Boston, Massachusetts",
month = "12-15 " # sep,
publisher = "The MIT Press",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
ISBN = "0-262-66183-7",
notes = "http://www.alife9.org/ ALIFE9",
}
@InProceedings{amarteifio:2005:CEC,
author = "Saoirse Amarteifio and Michael O'Neill",
title = "Coevolving Antibodies with a Rich Representation of
Grammatical Evolution",
booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary
Computation",
year = "2005",
editor = "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",
volume = "1",
pages = "904--911",
address = "Edinburgh, UK",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "2-5 " # sep,
organisation = "IEEE Computational Intelligence Society, Institution
of Electrical Engineers (IEE), Evolutionary Programming
Society (EPS)",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
ISBN = "0-7803-9363-5",
abstract = "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.",
notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and
the EPS.",
}
@MastersThesis{amarteifio:2005:IAGPMWRRIX,
title = "Interpreting a Genotype-Phenotype Map with Rich
Representations in {XMLGE}",
author = "Saoirse Amarteifio",
school = "University of Limerick",
year = "2005",
type = "Master of Science in Computer Science",
address = "University of Limerick, Ireland",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, xml",
URL = "http://ncra.ucd.ie/downloads/pub/SaoirseMScThesis.pdf",
size = "177 pages",
abstract = "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.",
language = "en",
}
@Article{nc:Amit+Geman:1997,
author = "Yali Amit and Donald Geman",
title = "Shape Quantization and Recognition with Randomized
Trees",
journal = "Neural Computation",
year = "1997",
volume = "9",
number = "7",
pages = "1545--1588",
month = oct,
abstract = "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.",
notes = "MIT Press
Cited by \cite{MatthewGSmith:2005:GPEM}.",
}
@InProceedings{amos:1998:DNAsbc,
author = "Martyn Amos and Paul E. Dunne and Alan Gibbons",
title = "{DNA} Simulation of {Boolean} Circuits",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "679--683",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "DNA Computing",
ISBN = "1-55860-548-7",
notes = "GP-98",
}
@InProceedings{Anand:2010:ICCSIT,
author = "Deepa Anand and K. K. Bharadwaj",
title = "Adaptive user similarity measures for recommender
systems: {A} genetic programming approach",
booktitle = "3rd IEEE International Conference on Computer Science
and Information Technology (ICCSIT 2010)",
year = "2010",
month = "9-11 " # jul,
volume = "8",
pages = "121--125",
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/ICCSIT.2010.5563737",
notes = "Sch. of Comput. & Syst. Sci., Jawaharlal Nehru Univ.,
Delhi, India Also known as \cite{5563737}",
}
@TechReport{anderson:1994:profile,
author = "Kenneth R. Anderson",
title = "Courage in Profiling",
institution = "BBN",
year = "1994",
month = "28 " # jul,
keywords = "genetic algorithms, genetic programming, CASCOR1",
URL = "http://openmap.bbn.com/~kanderso/performance/postscript/courage-in-profiles.ps",
notes = "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",
}
@InProceedings{anderson:ppsn2002:pp689,
author = "Eike Falk Anderson",
title = "Off-Line Evolution of Behaviour for Autonomous Agents
in Real-Time Computer Games",
booktitle = "Parallel Problem Solving from Nature - PPSN VII",
address = "Granada, Spain",
month = "7-11 " # sep,
pages = "689--699",
year = "2002",
editor = "Juan J. Merelo-Guervos and Panagiotis Adamidis and
Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and
Hans-Paul Schwefel",
number = "2439",
series = "Lecture Notes in Computer Science, LNCS",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, Games,
Machine Learning, Fitness Evaluation",
ISBN = "3-540-44139-5",
annote = "Available from
http://link.springer.de/link/service/series/0558/papers/2439/243900689.pdf",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2439&spage=689",
}
@InProceedings{andersson:1999:rmbGPrc,
author = "Bjorn Andersson and Per Svensson and Peter Nordin and
Mats Nordahl",
title = "Reactive and Memory-Based Genetic Programming for
Robot Control",
booktitle = "Genetic Programming, Proceedings of EuroGP'99",
year = "1999",
editor = "Riccardo Poli and Peter Nordin and William B. Langdon
and Terence C. Fogarty",
volume = "1598",
series = "LNCS",
pages = "161--172",
address = "Goteborg, Sweden",
publisher_address = "Berlin",
month = "26-27 " # may,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-65899-8",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1598&spage=161",
notes = "EuroGP'99, part of \cite{poli:1999:GP}
AIMGP machine code GP, memory, simulated robot",
}
@InProceedings{andersson:2000:4lrGP,
author = "Bjorn Andersson and Per Svensson and Peter Nordin and
Mats Nordahl",
title = "On-line Evolution of Control for a Four-Legged Robot
Using Genetic Programming",
booktitle = "Real-World Applications of Evolutionary Computing",
year = "2000",
editor = "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",
volume = "1803",
series = "LNCS",
pages = "319--326",
address = "Edinburgh",
publisher_address = "Berlin",
month = "17 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, linear GP",
ISBN = "3-540-67353-9",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1803&spage=319",
notes = "{"}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",
}
@InProceedings{Andersson:1998:ecmlc,
author = "Claes Andersson and Mats G. Nordahl",
title = "Evolving Coupled Map Lattices for Computation",
booktitle = "Proceedings of the First European Workshop on Genetic
Programming",
year = "1998",
editor = "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer
and Terence C. Fogarty",
volume = "1391",
series = "LNCS",
pages = "151--162",
address = "Paris",
publisher_address = "Berlin",
month = "14-15 " # apr,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-64360-5",
abstract = "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.",
notes = "EuroGP'98",
}
@Misc{oai:CiteSeerPSU:491253,
title = "The Rolling Stones - Genetic Programming in {AIP}",
author = "Thord Andersson and Per-Erik Forssen",
year = "2000",
month = mar # "~06",
abstract = "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",
citeseer-isreferencedby = "oai:CiteSeerPSU:76065",
citeseer-references = "oai:CiteSeerPSU:107311;
oai:CiteSeerPSU:125377",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:491253",
rights = "unrestricted",
URL = "http://www.ida.liu.se/~silco/AIP/Rolling-Stones.ps",
URL = "http://citeseer.ist.psu.edu/491253.html",
note = "student project",
keywords = "genetic algorithms, genetic programming",
size = "13 pages",
}
@InProceedings{ando:evows07,
author = "Daichi Ando and Palle Dahlsted and Mats Nordahl and
Hitoshi Iba",
title = "Interactive {GP} with Tree Representation of Classical
Music Pieces",
booktitle = "Applications of Evolutionary Computing,
EvoWorkshops2007: {EvoCOMNET}, {EvoFIN}, {EvoIASP},
{EvoInteraction}, {EvoMUSART}, {EvoSTOC},
{EvoTransLog}",
year = "2007",
month = "11-13 " # apr,
editor = "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",
series = "LNCS",
volume = "4448",
publisher = "Springer Verlag",
address = "Valencia, Spain",
pages = "577--584",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-540-71804-8",
doi = "doi:10.1007/978-3-540-71805-5_63",
abstract = "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.",
notes = "EvoWorkshops2007",
}
@InProceedings{Ando:2007:cec,
author = "Daichi Ando and Hitoshi Iba",
title = "Interactive Composition Aid System by Means of Tree
Representation of Musical Phrase",
booktitle = "2007 IEEE Congress on Evolutionary Computation",
year = "2007",
editor = "Dipti Srinivasan and Lipo Wang",
pages = "4258--4265",
address = "Singapore",
month = "25-28 " # sep,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "1-4244-1340-0",
file = "1814.pdf",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and
the IET.
IEEE Catalog Number: 07TH8963C",
}
@InProceedings{Ando:2009:ieeeSMC,
author = "Jun Ando and Tomoharu Nagao",
title = "Image classification and processing using modified
parallel-{ACTIT}",
booktitle = "IEEE International Conference on Systems, Man and
Cybernetics, SMC 2009",
year = "2009",
month = oct,
pages = "1787--1791",
keywords = "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",
doi = "doi:10.1109/ICSMC.2009.5346894",
ISSN = "1062-922X",
abstract = "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.",
notes = "Also known as \cite{5346894}",
}
@InProceedings{ando:2002:mgnbhg,
author = "Shin Ando and Hitoshi Iba and Erina Sakamoto",
title = "Modeling Genetic Network by Hybrid {GP}",
booktitle = "Proceedings of the 2002 Congress on Evolutionary
Computation CEC2002",
editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
and Garry Greenwood and Hitoshi Iba and Paul Marrow and
Mark Shackleton",
pages = "291--296",
year = "2002",
publisher = "IEEE Press",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
organisation = "IEEE Neural Network Council (NNC), Institution of
Electrical Engineers (IEE), Evolutionary Programming
Society (EPS)",
ISBN = "0-7803-7278-6",
month = "12-17 " # may,
notes = "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)",
keywords = "genetic algorithms, genetic programming",
URL = "http://citeseer.ist.psu.edu/520794.html",
URL = "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",
abstract = "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.",
notes = "oai:CiteSeerPSU:520794",
size = "6 pages",
}
@Article{ando:emi,
author = "Shin Ando and Erina Sakamoto and Hitoshi Iba",
title = "Evolutionary modeling and inference of gene network",
journal = "Information Sciences",
volume = "145",
number = "3-4",
month = sep,
year = "2002",
pages = "237--259",
keywords = "genetic algorithms, genetic programming, Gene network,
Evolutionary modeling, Time series prediction",
URL = "http://www.sciencedirect.com/science/article/B6V0C-46WWB37-3/2/963172f8c0faa12d700376b07bfc96a5",
ISSN = "0020-0255",
doi = "doi:10.1016/S0020-0255(02)00235-9",
abstract = "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.",
}
@Article{ando:2004:GPEM,
author = "Shin Ando and Hitoshi Iba",
title = "Classification of Gene Expression Profile Using
Combinatory Method of Evolutionary Computation and
Machine Learning",
journal = "Genetic Programming and Evolvable Machines",
year = "2004",
volume = "5",
number = "2",
pages = "145--156",
month = jun,
keywords = "genetic algorithms, genetic programming, evolutionary
computation, artificial immune system, wrapper
approach, gene expression classification, cancer
diagnosis",
ISSN = "1389-2576",
doi = "doi:10.1023/B:GENP.0000023685.83861.69",
abstract = "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.",
notes = "Part of \cite{banzhaf: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",
}
@Misc{andre:UGthesis,
author = "David Andre",
title = "Artificial Evolution of Intelligence: Lessons from
natural evolution: An illustrative approach using
Genetic Programming",
school = "Stanford University, Symbolic Systems Program",
year = "1994",
type = "BS Honors Thesis",
keywords = "genetic algorithms, genetic programming",
URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.66.1367&rep=rep1&type=pdf",
}
@InCollection{kinnear:andre,
title = "Automatically Defined Features: The Simultaneous
Evolution of 2-Dimensional Feature Detectors and an
Algorithm for Using Them",
author = "David Andre",
booktitle = "Advances in Genetic Programming",
publisher = "MIT Press",
editor = "Kenneth E. {Kinnear, Jr.}",
year = "1994",
pages = "477--494",
keywords = "genetic algorithms, genetic programming",
chapter = "23",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262111888",
size = "18 pages",
notes = "
Mixture of GP and two dee GA",
}
@InProceedings{andre:maps,
author = "David Andre",
title = "Evolution of Mapmaking Ability: Strategies for the
evolution of learning, planning, and memory using
genetic programming",
booktitle = "Proceedings of the 1994 IEEE World Congress on
Computational Intelligence",
year = "1994",
volume = "1",
pages = "250--255",
address = "Orlando, Florida, USA",
month = "27-29 " # jun,
publisher = "IEEE Press",
doi = "doi:10.1109/ICEC.1994.350007",
keywords = "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)",
abstract = "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",
}
@InProceedings{ieee94:andre,
author = "David Andre",
title = "Learning and Upgrading Rules for an {OCR} System Using
Genetic Programming",
year = "1994",
booktitle = "Proceedings of the 1994 IEEE World Congress on
Computational Intelligence",
address = "Orlando, Florida, USA",
month = "27-29 " # jun,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
URL = "http://citeseer.ist.psu.edu/31976.html",
URL = "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",
doi = "doi:10.1109/ICEC.1994.349906",
size = "6 pages",
notes = "Uses GP both to recognise C in various fonts and to
maintain manually produced extremely high level code
when a new font is added",
}
@InProceedings{Andre:1995:ammsp,
author = "David Andre",
title = "The Evolution of Agents that Build Mental Models and
Create Simple Plans Using Genetic Programming",
booktitle = "Genetic Algorithms: Proceedings of the Sixth
International Conference (ICGA95)",
year = "1995",
editor = "Larry J. Eshelman",
pages = "248--255",
address = "Pittsburgh, PA, USA",
publisher_address = "San Francisco, CA, USA",
month = "15-19 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, memory",
ISBN = "1-55860-370-0",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Andre_1995_ammsp.pdf",
size = "8 pages",
notes = "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.",
}
@InProceedings{andre:1995:parallel,
author = "David Andre and John R. Koza",
title = "Parallel Genetic Programming on a Network of
Transputers",
booktitle = "Proceedings of the Workshop on Genetic Programming:
From Theory to Real-World Applications",
year = "1995",
editor = "Justinian P. Rosca",
pages = "111--120",
address = "Tahoe City, California, USA",
month = "9 " # jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/andre_1995_parallel.pdf",
size = "10 pages",
notes = "like \cite{Koza:1995:pGPnt} part of
\cite{rosca:1995:ml}",
}
@InProceedings{andre:1995:apalmm,
author = "David Andre",
title = "The Automatic Programming of Agents that Learn Mental
Models and Create Simple Plans of Action",
booktitle = "IJCAI-95 Proceedings of the Fourteenth International
Joint Conference on Artificial Intelligence",
year = "1995",
volume = "1",
pages = "741--747",
address = "Montreal, Quebec, Canada",
publisher_address = "San Francisco, CA, USA",
month = "20-25 " # aug,
organisation = "IJCAII,AAAI,CSCSI",
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, memory",
ISBN = "1-55860-363-8",
URL = "http://ijcai.org/Past%20Proceedings/IJCAI-95-VOL%201/pdf/097.pdf",
size = "7 pages",
abstract = "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.",
notes = "MAPMAKER searches for gold",
}
@InProceedings{andre:1996:GKL,
author = "David Andre and Forrest H {Bennett III} and John R.
Koza",
title = "Evolution of Intricate Long-Distance Communication
Signals in Cellular Automata using Genetic
Programming",
booktitle = "Artificial Life V: Proceedings of the Fifth
International Workshop on the Synthesis and Simulation
of Living Systems",
year = "1996",
volume = "1",
address = "Nara, Japan",
publisher_address = "Cambridge, MA, USA",
month = "16--18 " # may,
publisher = "MIT Press",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.genetic-programming.com/jkpdf/alife1996gkl.pdf",
size = "10 pages",
abstract = "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.",
notes = "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.{"}",
}
@InCollection{andre:1996:aigp2,
author = "David Andre and John R. Koza",
title = "Parallel Genetic Programming: {A} Scalable
Implementation Using The Transputer Network
Architecture",
booktitle = "Advances in Genetic Programming 2",
publisher = "MIT Press",
year = "1996",
editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}",
pages = "317--338",
chapter = "16",
address = "Cambridge, MA, USA",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-262-01158-1",
URL = "http://cisnet.mit.edu/Advances-in-Genetic-Programming/334",
abstract = "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.",
}
@InProceedings{andre:1996:camc,
author = "David Andre and Forrest H {Bennett III} and John R.
Koza",
title = "Discovery by Genetic Programming of a Cellular
Automata Rule that is Better than any Known Rule for
the Majority Classification Problem",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "3--11",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
URL = "http://www.genetic-programming.com/jkpdf/gp1996gkl.pdf",
size = "9 pages",
abstract = "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.",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96",
}
@InProceedings{andre:1996:introns,
author = "David Andre and Astro Teller",
title = "A Study in Program Response and the Negative Effects
of Introns in Genetic Programming",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "12--20",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
URL = "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/AndreTeller.ps",
URL = "http://www.cs.cmu.edu/afs/cs/usr/astro/mosaic/TellerGP96/TellerGP96.html",
size = "9 pages",
abstract = "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.",
notes = "GP-96 html version available from
http://www.cs.cmu.edu/~astro/",
}
@InProceedings{andre:1996:parGP,
author = "David Andre and John R. Koza",
title = "A parallel implementation of genetic programming that
achieves super-linear performance",
booktitle = "Proceedings of the International Conference on
Parallel and Distributed Processing Techniques and
Applications",
year = "1996",
editor = "Hamid R. Arabnia",
volume = "III",
pages = "1163--1174",
address = "Sunnyvale",
month = "9-11 " # aug,
publisher = "CSREA",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.genetic-programming.com/jkpdf/pdpta1996.pdf",
size = "13 pages",
abstract = "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.",
notes = "Awarded Best Paper Award PDPTA'96",
}
@InCollection{andre:1997:HEC,
author = "David Andre",
title = "Learning and Upgrading Rules for an Optical Character
Recognition System Using Genetic Programming",
booktitle = "Handbook of Evolutionary Computation",
publisher = "Oxford University Press",
publisher_2 = "Institute of Physics Publishing",
year = "1997",
editor = "Thomas Baeck and David B. Fogel and Zbigniew
Michalewicz",
chapter = "section G8.1",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7503-0392-1",
doi = "doi:10.1201/9781420050387.ptg",
notes = "invited chapter",
}
@Misc{andre:cs267,
author = "David Andre",
title = "Multi-level parallelism in automatically synthesizing
soccer-playing programs for Robocup using genetic
programming",
year = "1998",
keywords = "genetic algorithms, genetic programming, memory",
URL = "http://citeseer.ist.psu.edu/245675.html",
broken = "http://www.cs.berkeley.edu/~dandre/cs267/final/cs267_final.ps",
broken = "http://www.cs.berkeley.edu/~dandre/cs267/final/project_final.htm",
size = "18 pages",
abstract = "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.",
notes = "my ghostview (Jan 2002) barfs at cs267_final.ps but it
prints",
}
@Article{AK97,
author = "David Andre and John R. Koza",
title = "A parallel implementation of genetic programming that
achieves super-linear performance",
journal = "Information Sciences",
year = "1998",
volume = "106",
number = "3-4",
pages = "201--218",
keywords = "genetic algorithms, genetic programming",
ISSN = "0020-0255",
URL = "http://www.sciencedirect.com/science/article/B6V0C-3TKS65B-21/2/22b9842f820b08883990bbae1d889c03",
URL = "http://www.davidandre.com/papers/isj97.ps",
doi = "doi:10.1016/S0020-0255(97)10011-1",
size = "18 pages",
abstract = "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.",
notes = "Information Sciences
http://www.elsevier.com/inca/publications/store/5/0/5/7/3/0/505730.pub.htt",
}
@InProceedings{andre:1998:tdcGPmdk,
author = "David Andre and Forrest H {Bennett III} and John Koza
and Martin A. Keane",
title = "On the Theory of Designing Circuits using Genetic
Programming and a Minimum of Domain Knowledge",
booktitle = "Proceedings of the 1998 IEEE World Congress on
Computational Intelligence",
year = "1998",
pages = "130--135",
address = "Anchorage, Alaska, USA",
month = "5-9 " # may,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-4869-9",
file = "c023.pdf",
size = "6 pages",
abstract = "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.",
notes = "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
World Congress on Computational Intelligence",
}
@InProceedings{Andre:1999:ETD,
author = "D. Andre and A. Teller",
title = "Evolving {Team Darwin United}",
booktitle = "RoboCup-98: Robot Soccer World Cup II",
year = "1999",
editor = "M. Asada and H. Kitano",
volume = "1604",
series = "LNCS",
pages = "346--351",
address = "Paris, France",
month = jul # " 1998",
publisher = "Springer Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-66320-7",
ISSN = "0302-9743",
bibdate = "Mon Sep 13 16:57:02 MDT 1999",
acknowledgement = ack-nhfb,
URL = "http://www.cs.cmu.edu/afs/cs/usr/astro/public/papers/Teller_Astro.ps",
URL = "http://link.springer.de/link/service/series/0558/bibs/1604/16040346.htm",
URL = "http://206.210.94.135/work/pdfs/Teller_Astro.pdf",
size = "7 pages",
abstract = "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.",
notes = "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",
}
@Article{Andreae:2008:IJKBIES,
author = "Peter Andreae and Huayang Xie and Mengjie Zhang",
title = "Genetic Programming for detecting rhythmic stress in
spoken English",
journal = "International Journal of Knowledge-Based and
Intelligent Engineering Systems",
year = "2008",
volume = "12",
number = "1",
pages = "15--28",
keywords = "genetic algorithms, genetic programming",
ISSN = "1327-2314",
publisher = "IOS Press",
URL = "http://iospress.metapress.com/content/k017m554023m5732/",
abstract = "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.",
notes = "KES, see also \cite{xie:evows06}",
}
@InCollection{kinnear:andrews,
author = "Martin Andrews and Richard Prager",
title = "Genetic Programming for the Acquisition of Double
Auction Market Strategies",
booktitle = "Advances in Genetic Programming",
publisher = "MIT Press",
editor = "Kenneth E. {Kinnear, Jr.}",
year = "1994",
chapter = "16",
size = "14 pages",
keywords = "genetic algorithms, genetic programming",
pages = "355--368",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262111888",
notes = "{"} 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.
",
}
@InProceedings{Ang:2008:cec,
author = "J. H. Ang and E. J. Teoh and C. H. Tan and K. C. Goh
and K. C. Tan",
title = "Dimension Reduction Using Evolutionary Support Vector
Machines",
booktitle = "2008 IEEE World Congress on Computational
Intelligence",
year = "2008",
editor = "Jun Wang",
address = "Hong Kong",
month = "1-6 " # jun,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-1823-7",
file = "EC0777.pdf",
abstract = "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",
keywords = "genetic algorithms, genetic programming",
notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
EPS and the IET.",
}
@PhdThesis{angeline:dissertation,
author = "Peter John Angeline",
title = "Evolutionary Algorithms and Emergent Intelligence",
school = "Ohio State University",
year = "1993",
size = "180 pages",
keywords = "genetic algorithms, genetic programming",
broken = "ftp://nervous.cis.ohio-state.edu/pub/papers/DISS/pja",
URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/chapter0.ps.Z",
URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/chapter1.ps.Z",
URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/chapter2.ps.Z",
URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/chapter3.ps.Z",
URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/chapter4.ps.Z",
URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/chapter5.ps.Z",
URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/chapter6.ps.Z",
URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/chapter7.ps.Z",
URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/chapter8.ps.Z",
URL = "http://www.ai.uga.edu/ftplib/misc/ga/papers/ToPrint/Dissertation/dissrefs.ps.Z",
notes = "
http://citeseer.ist.psu.edu/114089.html has
introduction",
}
@InCollection{kinnear:angeline,
title = "Genetic Programming and Emergent Intelligence",
author = "Peter John Angeline",
booktitle = "Advances in Genetic Programming",
publisher = "MIT Press",
editor = "Kenneth E. {Kinnear, Jr.}",
year = "1994",
pages = "75--98",
chapter = "4",
size = "23 pages",
keywords = "genetic algorithms, genetic programming",
URL = "http://citeseer.ist.psu.edu/187189.html",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/1870/http:zSzzSzwww.natural-selection.comzSzpeoplezSzpjazSzdocszSzaigp.pdf/angeline94genetic.pdf",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262111888",
notes = "{"}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.",
}
@InProceedings{icga93:angeline,
author = "Peter J. Angeline and Jordan B. Pollack",
title = "Competitive Environments Evolve Better Solutions for
Complex Tasks",
year = "1993",
booktitle = "Proceedings of the 5th International Conference on
Genetic Algorithms, ICGA-93",
editor = "Stephanie Forrest",
publisher = "Morgan Kaufmann",
pages = "264--270",
month = "17-21 " # jul,
address = "University of Illinois at Urbana-Champaign",
publisher_address = "2929 Campus Drive, Suite 260, San Mateo, CA
94403, USA",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.demo.cs.brandeis.edu/papers/icga5.pdf",
URL = "http://www.demo.cs.brandeis.edu/papers/icga5.ps.gz",
URL = "http://www.natural-selection.com/Library/1993/icga93.ps.Z",
size = "7 pages",
abstract = "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.",
ISBN = "1-55860-299-2",
notes = "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.",
}
@InProceedings{Angeline:1994:GPCS,
author = "P. J. Angeline",
title = "Genetic programming: {A} current snapshot",
booktitle = "Proceedings of the Third Annual Conference on
Evolutionary Programming",
year = "1994",
editor = "D. B. Fogel and W. Atmar",
publisher = "Evolutionary Programming Society",
keywords = "genetic algorithms, genetic programming",
broken = "http://www.natural-selection.com/Library/1994/ep94-gp.ps.Z",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/1870/http:zSzzSzwww.natural-selection.comzSzpeoplezSzpjazSzdocszSzep94-gp.pdf/angeline94genetic.pdf",
URL = "http://citeseer.ist.psu.edu/147407.html",
abstract = "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.",
}
@InProceedings{Angeline:1992:EIS,
author = "Peter J. Angeline and Jordan B. Pollack",
title = "The evolutionary induction of subroutines",
booktitle = "Proceedings of the Fourteenth Annual Conference of the
Cognitive Science Society",
year = "1992",
pages = "236--241",
address = "Bloomington, Indiana, USA",
publisher = "Lawrence Erlbaum",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.demo.cs.brandeis.edu/papers/glib92.pdf",
URL = "http://www.demo.cs.brandeis.edu/papers/glib92.ps.gz",
URL = "http://www.natural-selection.com/Library/1992/cogsci92.ps.Z",
abstract = "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.",
notes = "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.",
}
@TechReport{Angeline:1993:CHLR,
author = "P. J. Angeline and J. B. Pollack",
title = "Coevolving High-Level Representations",
institution = "Laboratory for Artificial Intelligence. The Ohio State
University",
year = "1993",
type = "July",
number = "Technical report 92-PA-COEVOLVE",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.demo.cs.brandeis.edu/papers/alife3.pdf",
abstract = "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.",
size = "15 pages",
}
@InProceedings{angeline:1993:ema,
author = "Peter J. Angeline and Jordan Pollack",
title = "Evolutionary Module Acquisition",
booktitle = "Proceedings of the Second Annual Conference on
Evolutionary Programming",
year = "1993",
editor = "D. Fogel and W. Atmar",
pages = "154--163",
address = "La Jolla, CA, USA",
month = "25-26 " # feb,
organisation = "The Evolutionary Programming Society",
keywords = "genetic algorithms, genetic programming, FSM, GLiB",
URL = "http://www.demo.cs.brandeis.edu/papers/ep93.pdf",
URL = "http://www.demo.cs.brandeis.edu/papers/ep93.ps.gz",
URL = "http://www.natural-selection.com/Library/1993/ep93.ps.Z",
size = "9 pages",
abstract = "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.",
notes = "Artificial Ant (John Muir). Finite State Machines.
Genetic Library Builder
",
}
@InProceedings{Angeline:1991:CHLR,
author = "P. J. Angeline and J. B. Pollack",
title = "Coevolving high-level representations",
booktitle = "Artificial Life III",
year = "1994",
editor = "Christopher G. Langton",
volume = "XVII",
series = "SFI Studies in the Sciences of Complexity",
pages = "55--71",
address = "Santa Fe, New Mexico",
month = "15-19 " # jun # " 1992",
publisher = "Addison-Wesley",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.demo.cs.brandeis.edu/papers/alife3.pdf",
URL = "http://www.demo.cs.brandeis.edu/papers/alife3.ps.gz",
abstract = "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.",
notes = "ALife3 Held June 1992 in Santa Fe, New Mexico, USA
GLiB, Tower of Hanoi, Tic Tac Toe. Also in thesis.",
}
@Article{angeline:1994:BS,
author = "Peter J. Angeline",
title = "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",
journal = "Biosystems",
year = "1994",
volume = "33",
number = "1",
pages = "69--73",
note = "Book review",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.sciencedirect.com/science/article/B6T2K-49N8PP4-23/2/021e3e016b39a87da29046c37f423f73",
doi = "doi:10.1016/0303-2647(94)90062-0",
notes = "Review of \cite{koza:book}",
}
@Article{angeline:1995:er,
author = "Peter J. Angeline",
title = "Evolution Revolution: An Introduction to the Special
Track on Genetic and Evolutionary Programming",
journal = "IEEE Expert",
year = "1995",
volume = "10",
number = "3",
pages = "6--10",
month = jun,
note = "Guest editor's introduction",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1109/MIS.1995.10027",
size = "4 pages",
notes = "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 (\cite{Tackett:1995:mGP} and
\cite{wong:1995:glp}) and 2 use hybrids ((GA and GP
\cite{howard:1995:GA-P}) and (Riziki and Zmuda, August
1995 GA and EP morphological pattern recognition))",
}
@InProceedings{angeline:1995:mcc,
author = "P. J. Angeline",
title = "Morphogenic Evolutionary Computations: Introduction,
Issues and Examples",
booktitle = "Evolutionary Programming IV: The Fourth Annual
Conference on Evolutionary Programming",
year = "1995",
editor = "John Robert McDonnell and Robert G. Reynolds and David
B. Fogel",
pages = "387--401",
publisher = "MIT Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-262-13317-2",
broken = "http://www.natural-selection.com/Library/1995/ep95-morph.ps.Z",
URL = "http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=4397",
size = "16 pages",
notes = "EP-95",
}
@InCollection{angeline:1995:asa,
author = "Peter J. Angeline",
title = "Adaptive and Self-Adaptive Evolutionary Computations",
booktitle = "Computational Intelligence: A Dynamic Systems
Perspective",
publisher = "IEEE Press",
year = "1995",
editor = "Marimuthu Palaniswami and Yianni Attikiouzel",
pages = "152--163",
keywords = "genetic algorithms, genetic programming",
broken = "http://www.natural-selection.com/Library/1995/icec95.ps.Z",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/1007/http:zSzzSzwww.natural-selection.comzSzpeoplezSzpjazSzdocszSzicec95.pdf/angeline95adaptive.pdf",
URL = "http://citeseer.ist.psu.edu/angeline95adaptive.html",
abstract = "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",
size = "13 pages",
}
@Book{book:1996:aigp2,
editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}",
title = "Advances in Genetic Programming 2",
publisher = "MIT Press",
year = "1996",
address = "Cambridge, MA, USA",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-262-01158-1",
URL = "http://www.cs.bham.ac.uk/~wbl/aigp2.html",
URL = "http://mitpress.mit.edu/book-home.tcl?isbn=0262011581",
URL = "http://cisnet.mit.edu/umrjb/toc",
size = "538 pages",
}
@InCollection{intro:1996:aigp2,
author = "Peter J. Angeline",
title = "Genetic Programming's Continued Evolution",
booktitle = "Advances in Genetic Programming 2",
publisher = "MIT Press",
year = "1996",
editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}",
pages = "1--20",
chapter = "1",
address = "Cambridge, MA, USA",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-262-01158-1",
URL = "http://cisnet.mit.edu/Advances-in-Genetic-Programming/18",
}
@InCollection{angeline:1996:aigp2,
author = "Peter J. Angeline",
title = "Two Self-Adaptive Crossover Operators for Genetic
Programming",
booktitle = "Advances in Genetic Programming 2",
publisher = "MIT Press",
year = "1996",
editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}",
pages = "89--110",
chapter = "5",
address = "Cambridge, MA, USA",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-262-01158-1",
URL = "http://www.natural-selection.com/Library/1996/aigp2.ps.Z",
URL = "http://cisnet.mit.edu/Advances-in-Genetic-Programming/106",
notes = "THese were called Selective Self-Adaptive Crossover
and Self-adaptive Multi-Crossover.",
}
@InProceedings{angeline:1996:leaf,
author = "Peter J. Angeline",
title = "An Investigation into the Sensitivity of Genetic
Programming to the Frequency of Leaf Selection During
Subtree Crossover",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "21--29",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
URL = "http://www.natural-selection.com/Library/1996/gp96.zip",
size = "9 pages",
notes = "GP-96 multiple types of mutation
Sunspot Numbers data from
http://www.ngdc.noaa.gov/stp/SOLAR/SSN/ssn.html",
}
@InProceedings{angeline:1996:efm,
author = "Peter J. Angeline",
title = "Evolving Fractal Movies",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "Evolutionary Programming",
pages = "503--511",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96 EP paper",
}
@InProceedings{angeline:1997:tcbbe,
author = "Peter J. Angeline",
title = "Subtree Crossover: Building Block Engine or
Macromutation?",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "9--17",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
URL = "http://ncra.ucd.ie/COMP41190/SubtreeXoverBuildingBlockorMacromutation_angeline_gp97.ps",
size = "9 pages",
notes = "GP-97",
}
@InProceedings{Angeline:1997:aIMepesr,
author = "Peter J. Angeline",
title = "An Alternative to Indexed Memory for Evolving Programs
with Explicit State Representations",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "evolutionary programming and evolution strategies",
pages = "423--430",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
notes = "GP-97",
}
@InProceedings{angeline:1997:txde,
author = "Peter J. Angeline",
title = "Tracking Extrema in Dynamic Environments",
booktitle = "Proceedings of the 6th International Conference on
Evolutionary Programming",
year = "1997",
editor = "P. J. Angeline and R. G. Reynolds and J. R. McDonnell
and R. Eberhart",
volume = "1213",
series = "Lecture Notes in Computer Science",
address = "Indianapolis, Indiana, USA",
month = apr # " 13-16",
publisher = "Springer Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-62788-X",
URL = "http://www.natural-selection.com/Library/1997/ep97b.pdf",
URL = "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-62788-X",
size = "11 pages",
abstract = "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.",
notes = "EP-97",
}
@InProceedings{angeline:1997:spie,
author = "Peter J. Angeline and David B. Fogel",
title = "An evolutionary program for the identification of
dynamical systems",
booktitle = "Application and Science of Artificial Neural Networks
III",
year = "1997",
editor = "S. Rogers",
volume = "3077",
pages = "409--417",
publisher_address = "Bellingham, WA, USA",
organisation = "SPIE-The International Society for Optical
Engineering",
keywords = "genetic algorithms, genetic programming, evolutionary
computation, evolutionary programming, system
identification, dynamical systems, optimization",
URL = "http://www.natural-selection.com/Library/1997/spie97.pdf",
size = "9 pages",
abstract = "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",
}
@InCollection{Angeline:1997:HEC,
author = "Peter J. Angeline",
title = "Parse Trees",
booktitle = "Handbook of Evolutionary Computation",
publisher = "Oxford University Press",
publisher_2 = "Institute of Physics Publishing",
year = "1997",
editor = "Thomas Baeck and David B. Fogel and Zbigniew
Michalewicz",
chapter = "section C1.6",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7503-0392-1",
doi = "doi:10.1201/9781420050387.ptc",
size = "3 pages",
}
@InCollection{Angeline:1997:HECa,
author = "Peter J. Angeline",
title = "Mutation: Parse Trees",
booktitle = "Handbook of Evolutionary Computation",
publisher = "Oxford University Press",
publisher_2 = "Institute of Physics Publishing",
year = "1997",
editor = "Thomas Baeck and David B. Fogel and Zbigniew
Michalewicz",
chapter = "section C3.2.5",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7503-0392-1",
doi = "doi:10.1201/9781420050387.ptc",
notes = "grow, shrink,switch, cycle=point",
size = "2 pages",
}
@InCollection{Angeline:1997:HECb,
author = "Peter J. Angeline",
title = "Crossover: parse trees",
booktitle = "Handbook of Evolutionary Computation",
publisher = "Oxford University Press",
publisher_2 = "Institute of Physics Publishing",
year = "1997",
editor = "Thomas Baeck and David B. Fogel and Zbigniew
Michalewicz",
chapter = "section C3.3.5",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7503-0392-1",
doi = "doi:10.1201/9781420050387.ptc",
size = "2 pages",
}
@InProceedings{angeline:1998:sccb,
author = "Peter J. Angeline",
title = "Subtree Crossover Causes Bloat",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "745--752",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, evolutionary
programming",
ISBN = "1-55860-548-7",
size = "9 pages",
notes = "GP-98, Even-5 parity, intertwined spirals, sunspot
prediction.
",
}
@Article{angeline:1998:hpees,
author = "Peter J. Angeline",
title = "A Historical Perspective on the Evolution of
Executable Structures",
journal = "Fundamenta Informaticae",
year = "1998",
volume = "35",
number = "1--4",
pages = "179--195",
month = aug,
email = "angeline@natural-selection.com",
keywords = "genetic algorithms, genetic programming",
ISSN = "0169-2968",
URL = "http://www.natural-selection.com/Library/1998/gphist.pdf",
size = "16 pages",
abstract = "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.",
notes = "Special volume: Evolutionary Computation
Also published in book form, see
\cite{angeline:1999:hpees}",
}
@Article{angeline:1998:mips3,
author = "Peter J. Angeline",
title = "Multiple Interacting Programs: {A} Representation for
Evolving Complex Behaviors",
journal = "Cybernetics and Systems",
year = "1998",
volume = "29",
number = "8",
pages = "779--806",
month = nov,
keywords = "genetic algorithms, genetic programming, mips",
ISSN = "0196-9722",
URL = "http://www.natural-selection.com/Library/1998/mips3.pdf",
URL = "http://www.tandf.co.uk/journals/frameloader.html?http://www.tandf.co.uk/journals/tf/01969722.html",
size = "31 pages",
abstract = "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.",
notes = "Sun spots, Santa Fe trail Artifical Ant, 5-bit
reverser, Tree, ANN",
}
@InProceedings{angeline:1998:spie,
author = "Peter J. Angeline",
title = "Evolving Predictors for Chaotic Time Series",
booktitle = "Proceedings of SPIE: Application and Science of
Computational Intelligence",
year = "1998",
editor = "S. Rogers and D. Fogel and J. Bezdek and B. Bosacchi",
volume = "3390",
pages = "170--80",
publisher_address = "Bellingham, WA, USA",
organisation = "SPIE",
keywords = "genetic algorithms, genetic programming, evolutionary
computation, evolutionary programming, neural networks,
chaotic time series prediction",
URL = "http://www.natural-selection.com/Library/1998/spie98.pdf",
size = "11 pages",
abstract = "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.",
}
@InCollection{angeline:1999:hpees,
author = "Peter J. Angeline",
title = "A Historical Perspective on the Evolution of
Executable Structures",
booktitle = "Evolutionary Computation",
publisher = "Ohmsha",
year = "1999",
editor = "A. E. Eiben and A. Michalewicz",
address = "Tokyo",
keywords = "genetic algorithms, genetic programming",
ISBN = "4-274-90269-2",
URL = "http://www.ohmsha.co.jp/data/books/e_contents/4-274-90269-2.htm",
notes = "This is the book edition of the journal, Fundamenta
Informaticae, Volume 35, Nos. 1-4, 1998. See also
\cite{angeline:1998:hpees}
",
size = "pages",
}
@InCollection{angeline:2000:EC1,
author = "Peter J. Angeline",
title = "Parse trees",
booktitle = "Evolutionary Computation 1 Basic Algorithms and
Operators",
publisher = "Institute of Physics Publishing",
year = "2000",
editor = "Thomas Baeck and David B. Fogel and Zbigniew
Michalewicz",
chapter = "19",
pages = "155--159",
address = "Bristol",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7503-0664-5",
notes = "http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=IP274",
size = "5 pages",
}
@InProceedings{Angelov:2008:GEFS,
author = "Plamen Angelov and Arthur Kordon and Xiaowei Zhou",
title = "Evolving fuzzy inferential sensors for process
industry",
booktitle = "3rd International Workshop on Genetic and Evolving
Fuzzy Systems, GEFS 2008",
year = "2008",
month = "4-7 " # mar,
address = "Witten-Boommerholz, Germany",
pages = "41--46",
keywords = "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",
doi = "doi:10.1109/GEFS.2008.4484565",
abstract = "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.",
notes = "Also known as \cite{4484565}",
}
@Article{AnnunziatoL2003:ICAE,
author = "Mauro Annunziato and Carlo Bruni and Matteo Lucchetti
and Stefano Pizzuti",
title = "Artificial Life Approach for Continuous Optimisation
of Non Stationary Dynamical Systems",
journal = "Integrated Computer-Aided Engineering",
year = "2003",
volume = "10",
number = "2",
pages = "111--125",
email = "lucchetti@dis.uniroma1.it",
keywords = "genetic algorithms, genetic programming, artificial
life",
ISSN = "1069-2509",
URL = "http://iospress.metapress.com/openurl.asp?genre=article&issn=1069-2509&volume=10&issue=2&spage=111",
abstract = "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.",
}
@InProceedings{Ansel:2011:GECCO,
author = "Jason Ansel and Maciej Pacula and Saman Amarasinghe
and Una-May O'Reilly",
title = "An efficient evolutionary algorithm for solving
incrementally structured problems",
booktitle = "GECCO '11: Proceedings of the 13th annual conference
on Genetic and evolutionary computation",
year = "2011",
editor = "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",
isbn13 = "978-1-4503-0557-0",
pages = "1699--1706",
keywords = "genetic algorithms, genetic programming, SBSE, Real
world applications",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001576.2001805",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "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 \cite{2001805} GECCO-2011 A joint meeting
of the twentieth international conference on genetic
algorithms (ICGA-2011) and the sixteenth annual genetic
programming conference (GP-2011)",
}
@Article{Anthes:2009:ACM,
author = "Gary Anthes",
title = "Deep Data Dives Discover Natural Laws",
journal = "Communications of the ACM",
year = "2009",
volume = "52",
number = "11",
pages = "13--14",
month = nov,
note = "News",
keywords = "genetic algorithms, genetic programming",
URL = "http://cacm.acm.org/magazines/2009/11/48443-deep-data-dives-discover-natural-laws/pdf",
doi = "doi:10.1145/1592761.1592768",
size = "2 pages",
abstract = "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.",
notes = "Report on \cite{Science09:Schmidt}",
}
@MastersThesis{hdl:1860/18,
title = "Evolving board evaluation fuctions for a complex
strategy game",
author = "Lisa Patricia Anthony",
year = "2002",
month = dec # "~30",
language = "en_US",
school = "Drexel University",
keywords = "genetic algorithms, genetic programming",
URL = "http://dspace.library.drexel.edu/handle/1721.1/18",
URL = "http://dspace.library.drexel.edu/bitstream/1860/18/1/anthony_thesis.pdf",
size = "73 pages",
abstract = "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.",
notes = "format = 318461",
}
@MastersThesis{antolik:mastersthesis,
author = "Jan Antolik",
title = "Evolutionary Tree Genetic Programming",
school = "Department of Computing and Information Sciences,
College of Arts and Sciences, Kansan State University",
year = "2004",
type = "Master of Science",
address = "Manhattan, Kansas, USA",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.ms.mff.cuni.cz/~antoj9am/thesis.pdf",
size = "49 pages",
abstract = "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.",
notes = "Approved by: Major Professor William Hsu",
}
@InProceedings{1068312,
author = "Jan Antolik and William H. Hsu",
title = "Evolutionary tree genetic programming",
booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on
Genetic and evolutionary computation",
year = "2005",
editor = "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",
volume = "2",
ISBN = "1-59593-010-8",
pages = "1789--1790",
address = "Washington DC, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1789.pdf",
doi = "doi:10.1145/1068009.1068312",
publisher = "ACM Press",
publisher_address = "New York, NY, 10286-1405, USA",
month = "25-29 " # jun,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, Poster",
notes = "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",
}
@InProceedings{conf/setn/AntoniouGTVL10,
title = "A Gene Expression Programming Environment for Fatigue
Modeling of Composite Materials",
author = "Maria A. Antoniou and Efstratios F. Georgopoulos and
Konstantinos A. Theofilatos and Anastasios P.
Vassilopoulos and Spiridon D. Likothanassis",
booktitle = "6th Hellenic Conference on Artificial Intelligence:
Theories, Models and Applications (SETN 2010)",
year = "2010",
volume = "6040",
editor = "Stasinos Konstantopoulos and Stavros J. Perantonis and
Vangelis Karkaletsis and Constantine D. Spyropoulos and
George A. Vouros",
pages = "297--302",
series = "Lecture Notes in Computer Science",
address = "Athens, Greece",
month = may # " 4-7",
publisher = "Springer",
isbn13 = "978-3-642-12841-7",
keywords = "genetic algorithms, genetic programming",
bibdate = "2010-05-11",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/setn/setn2010.html#AntoniouGTVL10",
doi = "doi:10.1007/978-3-642-12842-4",
abstract = "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.",
}
@InProceedings{Antoniou:2010:AIAI,
author = "Maria Antoniou and Efstratios Georgopoulos and
Konstantinos Theofilatos and Spiridon Likothanassis",
title = "Forecasting Euro - United States Dollar Exchange Rate
with Gene Expression Programming",
booktitle = "6th IFIP Advances in Information and Communication
Technology AIAI 2010",
year = "2010",
editor = "Harris Papadopoulos and Andreas Andreou and Max
Bramer",
volume = "339",
series = "IFIP Advances in Information and Communication
Technology",
pages = "78--85",
address = "Larnaca, Cyprus",
month = oct # " 6-7",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, Gene
Expression Programming",
doi = "doi:10.1007/978-3-642-16239-8_13",
abstract = "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.",
affiliation = "Pattern Recognition Laboratory, Dept. of Computer
Engineering & Informatics, University of Patras, 26500
Patras, Greece",
notes = "http://www.cs.ucy.ac.cy/aiai2010/",
}
@InProceedings{foga90*193,
author = "Hendrik James Antonisse",
title = "A Grammar-Based Genetic Algorithm",
pages = "193--204",
ISBN = "1-55860-170-8",
editor = "Gregory J. E. Rawlins",
booktitle = "Foundations of Genetic Algorithms",
month = "15--18 " # jul # " 1990",
address = "Indiana University, Bloomington, USA",
publisher = "Morgan Kaufmann",
publisher_address = "San Mateo",
keywords = "genetic algorithms, genetic programming, inductive
bias, high-level representations, crossover",
year = "1991",
notes = "FOGA-90 Published in 1991. cited by
\cite{bruhn:2002:ECJ} grammar-based crossover, parity.
K-armed bandit",
}
@InProceedings{ICIP99_Vol1*529,
author = "Shinya Aoki and Tomoharu Nagao",
title = "Automatic construction of tree-structural image
transformation using genetic programming",
pages = "529--533",
booktitle = "Proceedings of the 1999 International Conference on
Image Processing ({ICIP}-99)",
month = oct # " ~24--28",
publisher = "IEEE",
year = "1999",
volume = "1",
address = "Kobe",
publisher_address = "Los Alamitos, CA, USA",
keywords = "genetic algorithms, genetic programming",
abstract = "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",
}
@InProceedings{aparicio:1999:PM,
author = "Joaquim N. Aparicio and Luis Correia and Fernando
Moura-Pires",
title = "Populations are Multisets-{PLATO}",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1845--1850",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "methodology, pedagogy and philosophy",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/MP-603.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/MP-603.pdf",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@Article{Arakawa:2006:CILS,
author = "Masamoto Arakawa and Kiyoshi Hasegawa and Kimito
Funatsu",
title = "{QSAR} study of anti-{HIV} {HEPT} analogues based on
multi-objective genetic programming and
counter-propagation neural network",
journal = "Chemometrics and Intelligent Laboratory Systems",
year = "2006",
volume = "83",
number = "2",
pages = "91--98",
month = "15 " # sep,
keywords = "genetic algorithms, genetic programming,
Multi-objective optimisation, Variable selection, HEPT,
quantitative structure activity relationship",
doi = "doi:10.1016/j.chemolab.2006.01.009",
abstract = "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.",
}
@InProceedings{Aranha:2006:ASPGP,
title = "The effect of using evolutionary algorithms on ant
clustering techniques",
author = "Claus Aranha and Hitoshi Iba",
booktitle = "Proceedings of the Third Asian-Pacific workshop on
Genetic Programming",
year = "2006",
editor = "The Long Pham and Hai Khoi Le and Xuan Hoai Nguyen",
pages = "24--34",
ISSN = "18590209",
address = "Military Technical Academy, Hanoi, VietNam",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/aspgp06/Aranha_2006_ASPGP.pdf",
size = "11 pages",
abstract = "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.",
notes = "http://www.aspgp.org",
}
@InProceedings{Die99,
author = "Dieferson L. A. Araujo and Heitor S. Lopes and Alex A.
Freitas",
title = "A parallel genetic algorithm for rule discovery in
large databases",
booktitle = "Proceedings of IEEE Systems, Man and Cybernetics
Conference",
year = "1999",
volume = "III",
pages = "940--945",
note = "Tokyo, Japan, 12-15/october/1999",
keywords = "genetic algorithms, data mining, parallel",
URL = "http://www.cpgei.cefetpr.br/publicacoes/1999/ieeesmc99.zip",
notes = "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",
}
@InProceedings{araujo:2000:R,
author = "Dieferson L. A. Araujo and Heitor S. Lopes and Alex A.
Freitas",
title = "Rule discovery with a parallel genetic algorithm",
booktitle = "Data Mining with Evolutionary Algorithms",
year = "2000",
editor = "Alex A. Freitas and William Hart and Natalio Krasnogor
and Jim Smith",
pages = "89--94",
address = "Las Vegas, Nevada, USA",
month = "8 " # jul,
keywords = "genetic algorithms, data mining, parallel",
URL = "http://www.cpgei.cefetpr.br/~hslopes/publicacoes/2000/gecco2000b.zip",
notes = "GECCO-2000WKS Part of \cite{wu:2000:GECCOWKS}",
}
@InProceedings{araujo:2003:ICES,
author = "Sergio G. Araujo and A. Mesquita and Aloysio C. P.
Pedroza",
title = "Using Genetic Programming and High Level Synthesis to
Design Optimized Datapath",
booktitle = "Evolvable Systems: From Biology to Hardware, Fifth
International Conference, ICES 2003",
year = "2003",
editor = "Andy M. Tyrrell and Pauline C. Haddow and Jim
Torresen",
volume = "2606",
series = "LNCS",
pages = "434--445",
address = "Trondheim, Norway",
month = "17-20 " # mar,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-00730-X",
doi = "doi:10.1007/3-540-36553-2_39",
abstract = "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.",
notes = "ICES-2003",
}
@InProceedings{semish2003meta007,
title = "{S}{\'i}ntese de Circuitos Digitais Otimizados via
Programa{\c c}{\~a}o Gen{\'e}tica",
author = "Sergio Granato {de Araujo} and Antonio C. Mesquita and
Aloysio C. P. Pedroza",
year = "2003",
abstract = "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.",
identifier = "semish2003article007",
language = "por; eng",
rights = "Sociedade Brasileira de Computa{\c c}{\~a}o",
source = "semish2003",
URL = "http://www.sbc.org.br/sbc2003_cd/pdf/arq0088.pdf
broken",
booktitle = "XXX Semin{\'a}rio Integrado de Software e Hardware",
address = "Unicamp, Campinas, SP, Brasil",
month = "2-8 " # aug,
keywords = "genetic algorithms, genetic programming",
notes = "XXIII Brazilian Symposium on Computation (SBC'03)
http://www.ic.unicamp.br/sbc2003/enia.html
url broken 27 Sep 2004
In Portuguese
",
}
@InProceedings{araujo:2004:eurogp,
author = "Lourdes Araujo",
title = "Genetic Programming for Natural Language Parsing",
booktitle = "Genetic Programming 7th European Conference, EuroGP
2004, Proceedings",
year = "2004",
editor = "Maarten Keijzer and Una-May O'Reilly and Simon M.
Lucas and Ernesto Costa and Terence Soule",
volume = "3003",
series = "LNCS",
pages = "230--239",
address = "Coimbra, Portugal",
publisher_address = "Berlin",
month = "5-7 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-21346-5",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=230",
abstract = "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.",
notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in
conjunction with EvoCOP2004 and EvoWorkshops2004",
}
@InProceedings{Araujo:PPSN:2006,
author = "L. Araujo",
title = "Multiobjective Genetic Programming for Natural
Language Parsing and Tagging",
booktitle = "Parallel Problem Solving from Nature - PPSN IX",
year = "2006",
editor = "Thomas Philip Runarsson and Hans-Georg Beyer and
Edmund Burke and Juan J. Merelo-Guervos and L. Darrell
Whitley and Xin Yao",
volume = "4193",
pages = "433--442",
series = "LNCS",
address = "Reykjavik, Iceland",
publisher_address = "Berlin",
month = "9-13 " # sep,
publisher = "Springer-Verlag",
ISBN = "3-540-38990-3",
keywords = "genetic algorithms, genetic programming",
URL = "http://ppsn2006.raunvis.hi.is/proceedings/055.pdf",
doi = "doi:10.1007/11844297_44",
size = "10 pages",
abstract = "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.",
notes = "PPSN-IX",
}
@InProceedings{Araujo:2010:cec,
author = "Lourdes Araujo and Jesus Santamaria",
title = "Evolving natural language grammars without
supervision",
booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-6910-9",
abstract = "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.",
doi = "doi:10.1109/CEC.2010.5586291",
notes = "WCCI 2010. Also known as \cite{5586291}",
}
@InProceedings{Arcanjo:2011:GECCO,
author = "Filipe {de Lima Arcanjo} and Gisele Lobo Pappa and
Paulo Viana Bicalho and {Wagner Meira, Jr.} and
Altigran Soares {da Silva}",
title = "Semi-supervised genetic programming for
classification",
booktitle = "GECCO '11: Proceedings of the 13th annual conference
on Genetic and evolutionary computation",
year = "2011",
editor = "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",
isbn13 = "978-1-4503-0557-0",
pages = "1259--1266",
keywords = "genetic algorithms, genetic programming, Genetics
based machine learning",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001576.2001746",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{2001746} GECCO-2011 A joint
meeting of the twentieth international conference on
genetic algorithms (ICGA-2011) and the sixteenth annual
genetic programming conference (GP-2011)",
}
@InProceedings{1144042,
author = "Francesco Archetti and Stefano Lanzeni and Enza
Messina and Leonardo Vanneschi",
title = "Genetic programming for human oral bioavailability of
drugs",
booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference
on Genetic and evolutionary computation",
year = "2006",
editor = "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",
volume = "1",
ISBN = "1-59593-186-4",
pages = "255--262",
address = "Seattle, Washington, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p255.pdf",
doi = "doi:10.1145/1143997.1144042",
publisher = "ACM Press",
publisher_address = "New York, NY, 10286-1405, USA",
month = "8-12 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, Biological
Applications, bioavailability, bioinformatics,
complexity measures, molecular descriptors, performance
measures, SVM, ANN, LLSR, CFS, PCA, AIC, feature
selection, SMILES",
size = "8 pages",
abstract = "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",
notes = "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.",
}
@InProceedings{Archetti:2007:evobio,
author = "Francesco Archetti and Stefano Lanzeni and Enza
Messina and Leonardo Vanneschi",
title = "Genetic Programming and Other Machine Learning
Approaches to Predict Median Oral Lethal Dose ({LD50})
and Plasma Protein Binding Levels (%{PPB}) of Drugs",
booktitle = "EvoBIO 2007, Proceedings of the 5th European
Conference on Evolutionary Computation, Machine
Learning and Data Mining in Bioinformatics",
year = "2007",
editor = "Elena Marchiori and Jason H. Moore and Jagath C.
Rajapakse",
volume = "4447",
series = "Lecture Notes in Computer Science",
pages = "11--23",
address = "Valencia, Spain",
publisher_address = "Berlin Heidelberg NewYork",
month = apr # " 11-13",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
ISSN = "0302-9743",
ISBN = "3-540-71782-X",
isbn-13 = "978-3-540-71782-9",
doi = "doi:10.1007/978-3-540-71783-6_2",
abstract = "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.",
notes = "EvoBIO2007",
}
@Article{Archetti:2007:GPEM,
author = "Francesco Archetti and Stefano Lanzeni and Enza
Messina and Leonardo Vanneschi",
title = "Genetic programming for computational pharmacokinetics
in drug discovery and development",
journal = "Genetic Programming and Evolvable Machines",
year = "2007",
volume = "8",
number = "4",
pages = "413--432",
month = dec,
note = "special issue on medical applications of Genetic and
Evolutionary Computation",
keywords = "genetic algorithms, genetic programming, Computational
pharmacokinetics, Drug discovery, QSAR",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-007-9040-z",
abstract = "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.",
notes = "GP, LS2-GP, LS2-C-GP, DF-GP, AIC, Weka ANN, SVM,
Linear regression",
}
@Article{Archetti2010170,
title = "Genetic programming for {QSAR} investigation of
docking energy",
author = "Francesco Archetti and Ilaria Giordani and Leonardo
Vanneschi",
journal = "Applied Soft Computing",
volume = "10",
number = "1",
pages = "170--182",
year = "2010",
month = jan,
keywords = "genetic algorithms, genetic programming, Machine
learning, Regression, Docking energy, Computational
biology, Drug design, QSAR",
ISSN = "1568-4946",
doi = "doi:10.1016/j.asoc.2009.06.013",
URL = "http://www.sciencedirect.com/science/article/B6W86-4WP47KG-3/2/20419bfc47761543f509e96265d88e5d",
abstract = "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
\cite{Archetti: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.",
}
@Article{Archetti20101395,
author = "Francesco Archetti and Ilaria Giordani and Leonardo
Vanneschi",
title = "Genetic programming for anticancer therapeutic
response prediction using the {NCI}-60 dataset",
journal = "Computers \& Operations Research",
volume = "37",
number = "8",
pages = "1395--1405",
year = "2010",
note = "Operations Research and Data Mining in Biological
Systems",
ISSN = "0305-0548",
doi = "doi:10.1016/j.cor.2009.02.015",
URL = "http://www.sciencedirect.com/science/article/B6VC5-4VS40CF-4/2/a55e5b35bc3d30ac9057d5fb8cdcd2d0",
keywords = "genetic algorithms, genetic programming, Machine
learning, Regression, Microarray data, Anticancer
therapy, NCI-60",
abstract = "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.",
}
@InProceedings{Arcuri:2007:ASE,
author = "Andrea Arcuri and Xin Yao",
title = "Coevolving Programs and Unit Tests from their
Specification",
booktitle = "IEEE International Conference on Automated Software
Engineering (ASE)",
year = "2007",
address = "Atlanta, Georgia, USA",
month = nov # " 5-9",
organisation = "IEEE",
keywords = "genetic algorithms, genetic programming, Automatic
Programming, Coevolution, Software Testing, Formal
Specification, Sorting, SBSE",
doi = "doi:10.1145/1321631.1321693",
abstract = "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.",
notes = "http://www.cse.msu.edu/ase2007/",
}
@InProceedings{Arcuri:2008:ICSEphd,
author = "Andrea Arcuri",
title = "On the automation of fixing software bugs",
booktitle = "ICSE Companion '08: Companion of the 30th
international conference on Software engineering",
year = "2008",
pages = "1003--1006",
address = "Leipzig, Germany",
publisher_address = "New York, NY, USA",
publisher = "ACM",
note = "Doctoral symposium session",
keywords = "genetic algorithms, genetic programming, co-evolution,
SuA, SBSE",
isbn13 = "978-1-60558-079-1",
URL = "http://delivery.acm.org/10.1145/1380000/1370223/p1003-arcuri.pdf",
doi = "doi:10.1145/1370175.1370223",
size = "4 pages",
abstract = "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.",
notes = "p1006 {"}We are building our prototype on top of our
previous system for AP{"} \cite{Arcuri:2007:ASE}
also known as \cite{1370223}
Doctoral Symposium of the IEEE International Conference
in Software Engineering",
}
@InProceedings{Arcuri:2008:cec,
author = "Andrea Arcuri and Xin Yao",
title = "A Novel Co-Evolutionary Approach to Automatic Software
Bug Fixing",
booktitle = "2008 IEEE World Congress on Computational
Intelligence",
year = "2008",
editor = "Jun Wang",
address = "Hong Kong",
month = "1-6 " # jun,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-1823-7",
file = "EC0063.pdf",
abstract = "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.",
keywords = "genetic algorithms, genetic programming",
notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
EPS and the IET.",
}
@InProceedings{ArcuriWCY08,
author = "Andrea Arcuri and David Robert White and John Clark
and Xin Yao",
title = "Multi-Objective Improvement of Software using
Co-evolution and Smart Seeding",
booktitle = "Proceedings of the 7th International Conference on
Simulated Evolution And Learning (SEAL '08)",
year = "2008",
editor = "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{\"u}rgen Branke
and Yuhui Shi",
volume = "5361",
series = "Lecture Notes in Computer Science",
pages = "61--70",
address = "Melbourne, Australia",
month = dec # " 7-10",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, SBSE",
bibsource = "http://www.sebase.org/sbse/publications/repository.html",
isbn13 = "978-3-540-89693-7",
doi = "doi:10.1007/978-3-540-89694-4_7",
abstract = "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.",
notes = "Also known as \cite{DBLP:conf/seal/ArcuriWCY08}",
bibsource = "DBLP, http://dblp.uni-trier.de",
}
@TechReport{Arcuri09,
author = "Andrea Arcuri",
title = "Evolutionary Repair of Faulty Software",
institution = "University of Birmingham, School of Computer Science",
year = "2009",
type = "Technical Report",
number = "CSR-09-02",
address = "B15 2TT, UK",
month = apr,
keywords = "genetic algorithms, genetic programming, SBSE",
URL = "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/2009/CSR-09-02.pdf",
size = "34 pages",
abstract = "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.",
notes = "cited by \cite{Ackling:2011:GECCO}",
}
@InProceedings{Arcuri:2009:SSBSE,
author = "Andrea Arcuri",
title = "On Search Based Software Evolution",
booktitle = "Proceedings 1st International Symposium on Search
Based Software Engineering SSBSE 2009",
year = "2009",
editor = "Massimiliano {Di Penta} and Simon Poulding",
pages = "39--42",
address = "Windsor, UK",
month = "13-15 " # may,
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming, SBSE, program
coevolution, program test case, search algorithm,
software engineering problem, software evolution,
program testing, search problems, software
engineering",
isbn13 = "978-0-7695-3675-0",
doi = "doi:10.1109/SSBSE.2009.12",
abstract = "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.",
notes = "order number P3675 http://www.ssbse.org/ Also known as
\cite{5033178}",
}
@PhdThesis{Arcuri:thesis,
author = "Andrea Arcuri",
title = "Automatic software generation and improvement through
search based techniques",
school = "School of Computer Science, University of Birmingham",
year = "2009",
address = "UK",
month = aug,
keywords = "genetic algorithms, genetic programming, SBSE",
URL = "http://etheses.bham.ac.uk/400/1/Arcuri09PhD.pdf",
URL = "http://etheses.bham.ac.uk/400/",
size = "234 pages",
abstract = "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.",
}
@Article{Arcuri2010,
author = "Andrea Arcuri and Xin Yao",
title = "Co-evolutionary automatic programming for software
development",
journal = "Information Sciences",
year = "2010",
note = "In Press, Corrected Proof",
ISSN = "0020-0255",
doi = "doi:10.1016/j.ins.2009.12.019",
URL = "http://www.sciencedirect.com/science/article/B6V0C-4Y34WFM-2/2/6700572128cf209a061759f28c5b7020",
keywords = "genetic algorithms, genetic programming, SBSE, STGP,
Automatic programming, Automatic refinement,
Co-evolution, Software testing",
abstract = "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.",
notes = "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.",
}
@Article{Arcuri20113494,
author = "Andrea Arcuri",
title = "Evolutionary repair of faulty software",
journal = "Applied Soft Computing",
volume = "11",
number = "4",
pages = "3494--3514",
year = "2011",
ISSN = "1568-4946",
doi = "doi:10.1016/j.asoc.2011.01.023",
URL = "http://www.sciencedirect.com/science/article/B6W86-5223XWX-1/2/5d81be4fc12644887723df167e134516",
keywords = "genetic algorithms, genetic programming, Repair, Fault
localisation, Automated debugging, Search Based
Software Engineering, Coevolution",
abstract = "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.",
}
@InCollection{ardell:1994:TOPE,
author = "David H. Ardell",
title = "{TOPE} and Magic Squares: {A} Simple {GA} Approach to
Combinatorial Optimization",
booktitle = "Genetic Algorithms at Stanford 1994",
year = "1994",
editor = "John R. Koza",
pages = "1--6",
address = "Stanford, California, 94305-3079 USA",
month = dec,
organisation = "Stanford University",
publisher = "Stanford Bookstore",
keywords = "genetic algorithms",
ISBN = "0-18-187263-3",
notes = "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",
}
@Article{Arganis:2009:AiCE,
title = "Genetic Programming and Standardization in Water
Temperature Modelling",
author = "Maritza Arganis and Rafael Val and Jordi Prats and
Katya Rodriguez and Ramon Dominguez and Josep Dolz",
journal = "Advances in Civil Engineering",
year = "2009",
volume = "2009",
publisher = "Hindawi Publishing Corporation",
keywords = "genetic algorithms, genetic programming",
ISSN = "16878086",
URL = "http://downloads.hindawi.com/journals/ace/2009/353960.pdf",
doi = "doi:10.1155/2009/353960",
bibsource = "OAI-PMH server at www.doaj.org",
language = "eng",
oai = "oai:doaj-articles:c20c6fc231b4ba552b81c9f42d58f35f",
abstract = "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.",
notes = "Article ID 353960",
}
@InProceedings{Arita:1997:hamilton,
author = "Masanori Arita and Akira Suyama and Masami Hagiya",
title = "A Heuristic Approach for Hamiltonian Path Problem with
Molecules",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "DNA Computing",
pages = "457--462",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
notes = "GP-97",
}
@InProceedings{Arkoudas:2008:AAAIf,
author = "Konstantine Arkoudas",
title = "Automatically Discovering Euler's Identity via Genetic
Programming",
booktitle = "AAAI Fall Symposium",
year = "2008",
editor = "Selmer Bringsjord and Andrew Shilliday",
pages = "1--7",
address = "Arlington, Virginia, USA",
publisher_address = "Menlo Park, California, USA",
month = nov # " 7-9",
publisher = "AAAI",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-57735-395-9",
URL = "http://www.aaai.org/Papers/Symposia/Fall/2008/FS-08-03/FS08-03-001.pdf",
size = "7 pages",
abstract = "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.",
notes = "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/",
}
@Article{Arkov:2000:ARC,
author = "V. Arkov and C. Evans and P. J. Fleming and D. C. Hill
and J. P. Norton and I. Pratt and D. Rees and K.
Rodriguez-Vazquez",
title = "System Identification Strategies Applied to Aircraft
Gas Turbine Engines",
journal = "Annual Reviews in Control",
volume = "24",
pages = "67--81",
year = "2000",
number = "1",
keywords = "genetic algorithms, genetic programming, gas turbines,
system identification, frequency domain, multisine
signals least-squares estimation, time-varying systems,
structure selection",
ISSN = "1367-5788",
URL = "http://www.sciencedirect.com/science/article/B6V0H-482MDPD-8/2/dd470648e2228c84efe7e14ca3841b7e",
doi = "doi:10.1016/S1367-5788(00)90015-4",
size = "15 pages",
abstract = "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.",
notes = "Also known as \cite{Arkov200067}",
}
@InProceedings{Arpaia:2009:I2MTC,
author = "Pasquale Arpaia and Fabrizio Clemente and Carlo Manna
and Giuseppe Montenero",
title = "Automatic modeling based on cultural programming for
osseointegration diagnosis",
booktitle = "IEEE Instrumentation and Measurement Technology
Conference, I2MTC '09",
year = "2009",
pages = "1274--1277",
address = "Singapore",
month = "5-7 " # may,
keywords = "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",
isbn13 = "978-1-4244-3352-0",
ISSN = "1091-5281",
doi = "doi:10.1109/IMTC.2009.5168651",
size = "4 pages",
abstract = "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.",
notes = "Also known as \cite{5168651}",
}
@Article{arslan:2005:GPEM,
author = "Tughrul Arslan",
title = "Book Review: Evolvable Components--From Theory to
Hardware Implementations",
journal = "Genetic Programming and Evolvable Machines",
year = "2005",
volume = "6",
number = "4",
pages = "461--462",
month = dec,
keywords = "genetic algorithms, evolvable hardware",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-005-3718-x",
size = "2 pages",
abstract = "Book Review: Evolvable Components--From Theory to
Hardware Implementations by Lukas Sekanina Springer,
2003, ISBN 3-540-40377-9",
notes = "review of \cite{sekanina:2003:book}",
}
@InProceedings{Arvaneh:2009:ICBPE,
author = "M. Arvaneh and H. Ahmadi and A. Azemi and M. Shajiee
and Z. S. Dastgheib",
title = "Prediction of Paroxysmal Atrial Fibrillation by
dynamic modeling of the {PR} interval of {ECG}",
booktitle = "International Conference on Biomedical and
Pharmaceutical Engineering, ICBPE '09",
year = "2009",
month = "2-4 " # dec,
pages = "1--5",
keywords = "genetic algorithms, genetic programming, ECG signal,
PR interval, Paroxysmal Atrial Fibrillation,
electrocardiography, neural networks,
electrocardiography, neural nets",
doi = "doi:10.1109/ICBPE.2009.5384063",
abstract = "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.",
notes = "Also known as \cite{5384063}",
}
@Article{Asadi:2010:ASC,
author = "Mojtaba Asadi and Mehdi Eftekhari and Mohammad Hossein
Bagheripour",
title = "Evaluating the strength of intact rocks through
genetic programming",
journal = "Applied Soft Computing",
year = "2011",
volume = "11",
number = "2",
pages = "1932--1937",
month = mar,
keywords = "genetic algorithms, genetic programming, Information
criterion, Intact rock, Failure criteria",
ISSN = "1568-4946",
URL = "http://www.sciencedirect.com/science/article/B6W86-50CVPW4-2/2/863c13a5a1c7be6da7b1ea6592b11bd3",
doi = "doi:10.1016/j.asoc.2010.06.009",
size = "13 pages",
abstract = "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.",
notes = "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",
}
@InCollection{ashcraft:2003:NEBCEGA,
author = "Kenneth Ashcraft",
title = "Nark: Evolving Bug-Finding Compiler Extensions with
Genetic Algorithms",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2003",
year = "2003",
editor = "John R. Koza",
pages = "11--20",
address = "Stanford, California, 94305-3079 USA",
month = "4 " # dec,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms",
URL = "http://www.genetic-programming.org/sp2003/Ashcraft.pdf",
notes = "part of \cite{koza:2003:gagp}",
}
@Article{Ashiru:1998:MM,
author = "I. Ashiru and C. A. Czarnecki",
title = "Evolving communicating controllers for multiple mobile
robot systems",
journal = "Microprocessors and Microsystems",
year = "1998",
volume = "21",
pages = "393--402",
number = "6",
keywords = "genetic algorithms, genetic programming, Mobile
robots, Communication",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6V0X-3TB0788-6/2/445577f1e7cd0c0d531457835edf327e",
ISSN = "0141-9331",
doi = "doi:10.1016/S0141-9331(98)00054-4",
abstract = "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.",
}
@InProceedings{ashlock:1997:GPdd,
author = "Dan Ashlock",
title = "{GP}-Automata for Dividing the Dollar",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "18--26",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/ashlock_1997_GPdd.pdf",
size = "9 pages",
notes = "GP-97",
}
@InProceedings{ashlock:1997:spbs,
author = "Dan Ashlock and Charles Richter",
title = "The Effect of Splitting Populations on Bidding
Strategies",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "27--34",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/ashlock_1997_spbs.pdf",
size = "8 pages",
abstract = "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.",
notes = "GP-97",
}
@InProceedings{ashlock:1998:fctsGP,
author = "Dan Ashlock and James I. Lathrop",
title = "A Fully Characterized Test Suite for Genetic
Programming",
booktitle = "Evolutionary Programming VII: Proceedings of the
Seventh Annual Conference on Evolutionary Programming",
year = "1998",
editor = "V. William Porto and N. Saravanan and D. Waagen and A.
E. Eiben",
volume = "1447",
series = "LNCS",
pages = "537--546",
address = "Mission Valley Marriott, San Diego, California, USA",
publisher_address = "Berlin",
month = "25-27 " # mar,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-64891-7",
doi = "doi:10.1007/BFb0040753",
notes = "EP-98.
Iowa State University.",
}
@InProceedings{ashlock:1998:ISAc,
author = "Dan Ashlock and Mark Joenks",
title = "{ISA}c Lists, {A} Different Representation for Program
Induction",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "3--10",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-548-7",
notes = "GP-98",
}
@InProceedings{ashlock:2003:taaaogptve,
author = "Dan Ashlock and Kenneth M. Bryden",
title = "Thermal agents: An application of genetic programming
to virtual engineering",
booktitle = "Proceedings of the 2003 Congress on Evolutionary
Computation CEC2003",
editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass
and Kay Chen Tan and Bob McKay and Daryl Essam and Tom
Gedeon",
pages = "1340--1347",
year = "2003",
publisher = "IEEE Press",
address = "Canberra",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "8-12 " # dec,
organisation = "IEEE Neural Network Council (NNC), Engineers Australia
(IEAust), Evolutionary Programming Society (EPS),
Institution of Electrical Engineers (IEE)",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-7804-0",
abstract = "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.",
notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust,
the EPS, and the IEE.",
}
@InProceedings{Ashlock:2004:OToECP,
title = "On Taxonomy of Evolutionary Computation Problems",
author = "Daniel Ashlock and Kenneth Bryden and Steven Corns",
pages = "1713--1719",
booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary
Computation",
year = "2004",
volume = "2",
publisher = "IEEE Press",
month = "20-23 " # jun,
address = "Portland, Oregon",
ISBN = "0-7803-8515-2",
keywords = "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",
doi = "doi:10.1109/CEC.2004.1331102",
abstract = "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.",
notes = "Also known as \cite{1331102}. CEC 2004 - A joint
meeting of the IEEE, the EPS, and the IEE.",
}
@InProceedings{Ashlock:2004:CaT,
title = "Coevolution and Tartarus",
author = "Daniel Ashlock and Stephen Willson and Nicole Leahy",
pages = "1618--1624",
booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary
Computation",
year = "2004",
publisher = "IEEE Press",
month = "20-23 " # jun,
address = "Portland, Oregon",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming, Coevolution
\& collective behavior, Evolutionary intelligent
agents",
URL = "http://orion.math.iastate.edu/danwell/eprints/TartarusCE.pdf",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=01331089",
abstract = "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.",
notes = "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.",
}
@InProceedings{ashlock:2005:CECd,
author = "Daniel A. Ashlock and Kenneth M. Bryden and Wendy
Ashlock and Stephen P. Gent",
title = "Rapid Training of Thermal Agents with Single Parent
Genetic Programming",
booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary
Computation",
year = "2005",
editor = "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",
volume = "3",
pages = "2122--2129",
address = "Edinburgh, UK",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "2-5 " # sep,
organisation = "IEEE Computational Intelligence Society, Institution
of Electrical Engineers (IEE), Evolutionary Programming
Society (EPS)",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-9363-5",
abstract = "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.",
notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and
the EPS.",
}
@InProceedings{Ashlock:2006:CECtax,
author = "Daniel A. Ashlock and Kenneth M. Bryden and Steven
Corns and Justin Schonfeld",
title = "An Updated Taxonomy of Evolutionary Computation
Problems using Graph-based Evolutionary Algorithms",
booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary
Computation",
year = "2006",
editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and
Simon M. Lucas",
pages = "403--410",
address = "Vancouver",
month = "6-21 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-9487-9",
abstract = "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.",
notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
the IEE.
IEEE Catalog Number: 06TH8846D IEEE Xplore gives pages
as 96--103",
}
@Book{Ashlock:2006:book,
author = "Daniel Ashlock",
title = "Evolutionary Computation for Modeling and
Optimization",
publisher = "Springer",
year = "2006",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-0-387-22196-0",
URL = "http://www.springerlink.com/content/978-0-387-22196-0",
abstract = "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",
notes = "GP in Chapter 12 ISAc, Chapter 13 graph based EA,
Chapter 14 cellular encoding",
size = "572 pages",
}
@InProceedings{Ashlock:2006:ANNIE,
author = "Daniel Ashlock and Kenneth M. Bryden and Nathan G.
Johnson",
title = "Evolvable Threaded Controllers for a Multi-Agent Grid
Robot Task",
booktitle = "ANNIE 2006, Intelligent Engineering Systems through
Artificial Neural Networks",
year = "2006",
editor = "Cihan H. Dagli and Anna L. Buczak and David L. Enke
and Mark Embrechts and Okan Ersoy",
volume = "16",
address = "St. Louis, MO, USA",
month = nov # " 5-8",
keywords = "genetic algorithms, genetic programming",
isbn13 = "0791802566",
doi = "doi:10.1115/1.802566.paper22",
abstract = "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.",
}
@InProceedings{Ashlock:2006:ANNIEa,
author = "Daniel Ashlock and Kenneth M. Bryden",
title = "Function Stacks, {GBEA}s, and Crossover for the Parity
Problem",
booktitle = "ANNIE 2006, Intelligent Engineering Systems through
Artificial Neural Networks",
year = "2006",
editor = "Cihan H. Dagli and Anna L. Buczak and David L. Enke
and Mark Embrechts and Okan Ersoy",
volume = "16",
address = "St. Louis, MO, USA",
month = nov # " 5-8",
note = "Part I: Evolutionary Computation",
keywords = "genetic algorithms, genetic programming",
isbn13 = "0791802566",
doi = "doi:10.1115/1.802566.paper18",
abstract = "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.",
}
@InProceedings{Ashlock:2008:cec,
author = "Daniel Ashlock and Taika {von Konigslow}",
title = "Evolution of Artificial Ring Species",
booktitle = "2008 IEEE World Congress on Computational
Intelligence",
year = "2008",
editor = "Jun Wang",
address = "Hong Kong",
month = "1-6 " # jun,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-1823-7",
file = "EC0169.pdf",
abstract = "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.",
keywords = "genetic algorithms, genetic programming",
notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
EPS and the IET.",
}
@InProceedings{Ashlock3:2008:cec,
author = "Daniel Ashlock and Elizabeth Warner",
title = "The Geometry of Tartarus Fitness Cases",
booktitle = "2008 IEEE World Congress on Computational
Intelligence",
year = "2008",
editor = "Jun Wang",
address = "Hong Kong",
month = "1-6 " # jun,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-1823-7",
file = "EC0339.pdf",
abstract = "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.",
keywords = "genetic algorithms, genetic programming",
notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
EPS and the IET.",
}
@InProceedings{Ashlock5:2008:cec,
author = "Daniel A. Ashlock and Kenneth M. Bryden and Steven
Corns",
title = "Small Population Effects and Hybridization",
booktitle = "2008 IEEE World Congress on Computational
Intelligence",
year = "2008",
editor = "Jun Wang",
address = "Hong Kong",
month = "1-6 " # jun,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-1823-7",
file = "EC0599.pdf",
abstract = "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.",
keywords = "genetic algorithms, genetic programming",
notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
EPS and the IET.",
}
@InProceedings{Ashlock:2009:ANNIEa,
author = "Daniel Ashlock and Adam J. Shuttleworth and Kenneth M.
Bryden",
title = "Induction of Virtual Sensors with Function Stacks",
booktitle = "ANNIE 2009, Intelligent Engineering Systems through
Artificial Neural Networks",
year = "2009",
editor = "Cihan H. Dagli and K. Mark Bryden and Steven M. Corns
and Mitsuo Gen and Kagan Tumer and Gursel Suer",
volume = "19",
address = "St. Louis, MO, USA",
note = "Part I",
keywords = "genetic algorithms, genetic programming",
isbn13 = "9780791802953",
doi = "doi:10.1115/1.802953.paper4",
abstract = "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.",
}
@InProceedings{Ashlock:2009:ANNIE,
author = "Daniel Ashlock and Douglas McCorkle and Kenneth M.
Bryden",
title = "Logic Function Induction with the Blender Algorithm
Using Function Stacks",
booktitle = "ANNIE 2009, Intelligent Engineering Systems through
Artificial Neural Networks",
year = "2009",
editor = "Cihan H. Dagli and K. Mark Bryden and Steven M. Corns
and Mitsuo Gen and Kagan Tumer and Gursel Suer",
volume = "19",
pages = "189--196",
address = "St. Louis, MO, USA",
note = "Part III Evolutionary Computation",
keywords = "genetic algorithms, genetic programming",
isbn13 = "9780791802953",
doi = "doi:10.1115/1.802953.paper24",
abstract = "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.",
}
@InProceedings{Ashlock:2010:cec,
author = "Daniel Ashlock and Justin Schonfeld",
title = "Evolution for automatic assessment of the difficulty
of sokoban boards",
booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-6910-9",
abstract = "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.",
doi = "doi:10.1109/CEC.2010.5586239",
notes = "WCCI 2010. Also known as \cite{5586239}",
}
@InProceedings{ashlock:2005:CECw,
author = "Wendy Ashlock and Dan Ashlock",
title = "Single Parent Genetic Programming",
booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary
Computation",
year = "2005",
editor = "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",
volume = "2",
pages = "1172--1179",
address = "Edinburgh, UK",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "2-5 " # sep,
organisation = "IEEE Computational Intelligence Society, Institution
of Electrical Engineers (IEE), Evolutionary Programming
Society (EPS)",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-9363-5",
abstract = "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.",
notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and
the EPS.",
}
@InProceedings{ashlock:2006:cecW,
author = "Wendy Ashlock",
title = "Using Very Small Population Sizes in Genetic
Programming",
booktitle = "2006 IEEE World Congress on Computational
Intelligence, 2006 IEEE Congress on Evolutionary
Computation",
year = "2006",
pages = "1023--1030",
address = "Vancouver",
month = "16-21 " # jul,
keywords = "genetic algorithms, genetic programming",
size = "8 pages",
abstract = "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.",
}
@InProceedings{Ashlock:2006:ANNIEw,
author = "Wendy Ashlock",
title = "Mutation vs. Crossover with Genetic Programming",
booktitle = "ANNIE 2006, Intelligent Engineering Systems through
Artificial Neural Networks",
year = "2006",
editor = "Cihan H. Dagli and Anna L. Buczak and David L. Enke
and Mark Embrechts and Okan Ersoy",
volume = "16",
address = "St. Louis, MO, USA",
month = nov # " 5-8",
note = "Part I: Evolutionary Computation",
keywords = "genetic algorithms, genetic programming",
isbn13 = "0791802566",
doi = "doi:10.1115/1.802566.paper2",
abstract = "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.",
}
@Article{Ashour:2003:CS,
author = "A. F. Ashour and L. F. Alvarez and V. V. Toropov",
title = "Empirical modelling of shear strength of {RC} deep
beams by genetic programming",
journal = "Computers and Structures",
year = "2003",
volume = "81",
number = "5",
pages = "331--338",
month = mar,
keywords = "genetic algorithms, genetic programming, Reinforced
concrete deep beams, Empirical model building",
URL = "http://www.sciencedirect.com/science/article/B6V28-47S6J5M-5/2/03211d57903fd1d7c48ac56fb32d1d36",
doi = "doi:10.1016/S0045-7949(02)00437-6",
abstract = "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.",
}
@InProceedings{Aslam:2010:milcom,
author = "Muhammad Waqar Aslam and Zhechen Zhu and Asoke K.
Nandi",
title = "Automatic digital modulation classification using
Genetic Programming with {K}-Nearest Neighbor",
booktitle = "MILCOM 2010",
year = "2010",
month = oct # " 31-" # nov # " 3",
pages = "1731--1736",
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/MILCOM.2010.5680232",
ISSN = "2155-7578",
notes = "Also known as \cite{5680232}",
}
@Article{Asouti:2009:GPEM,
author = "V. G. Asouti and I. C. Kampolis and K. C.
Giannakoglou",
title = "A grid-enabled asynchronous metamodel-assisted
evolutionary algorithm for aerodynamic optimization",
journal = "Genetic Programming and Evolvable Machines",
year = "2009",
volume = "10",
number = "4",
pages = "373--389",
month = dec,
keywords = "genetic algorithms, Asynchronous evolutionary
algorithms, Metamodels, Grid computing, Aerodynamic
shape optimization",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-009-9090-5",
size = "17 pages",
abstract = "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.",
notes = "Parallel CFD & Optimization Unit, Lab. of Thermal
Turbomachines, School of Mechanical Engineering,
National Technical University of Athens, P.O. Box
64069, Athens, 15710, Greece",
}
@InProceedings{Atkin:1993:GPLAMS,
author = "M. Atkin and P. R. Cohen",
title = "Genetic programming to learn an agent's monitoring
strategy",
booktitle = "Proceedings of the AAAI-93 Workshop on Learning Action
Models",
year = "1993",
editor = "Wei-Min Shen",
pages = "36--41",
publisher = "AAAI Press",
URL = "http://www.aaai.org/Papers/Workshops/1993/WS-93-06/WS93-06-009.pdf",
URL = "http://www.aaai.org/Library/Workshops/ws93-06.php",
keywords = "genetic algorithms, genetic programming",
size = "6 pages",
abstract = "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.",
notes = "Also available as \cite{Atkin:1993:GPLAMSa}?",
}
@TechReport{Atkin:1993:GPLAMSa,
author = "M. Atkin and P. R. Cohen",
title = "Genetic programming to learn an agent's monitoring
strategy",
institution = "Computer Science Department, University of
Massachusetts",
year = "1993",
type = "Technical report",
number = "TR-93-26",
address = "Amherst, MA, USA",
URL = "http://www-eksl.cs.umass.edu/papers/93-26.ps",
keywords = "genetic algorithms, genetic programming",
notes = "Also available as \cite{Atkin:1993:GPLAMS}?",
size = "15 pages",
}
@InProceedings{Atkin:1994:LMSDGP,
author = "Marc S. Atkin and Paul R. Cohen",
title = "Learning monitoring strategies: {A} difficult genetic
programming application",
booktitle = "Proceedings of the 1994 IEEE World Congress on
Computational Intelligence",
year = "1994",
volume = "1",
pages = "328--332a",
address = "Orlando, Florida, USA",
month = "27-29 " # jun,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, cupcake
problem, agent control language, genetic programming
application, monitoring strategy learning, optimal
strategies, possible behaviour, learning (artificial
intelligence), monitoring; optimisation",
URL = "http://www-eksl.cs.umass.edu/papers/AtkinIEEE.pdf",
URL = "http://citeseer.ist.psu.edu/94049.html",
doi = "doi:10.1109/ICEC.1994.349931",
size = "6 pages",
abstract = "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.",
notes = "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?",
}
@TechReport{atkin:1995:mea,
author = "Marc S. Atkin and Paul R. Cohen",
title = "Monitoring in Embedded Agents",
institution = "Experimental Knowledge Systems Laboratory, Computer
Science Department, University of Massachusetts",
year = "1995",
type = "Computer Science Technical Report",
number = "95-66",
address = "Box 34610, Lederle Graduate Research Center, Amherst.
MA 01003-4610, USA",
keywords = "genetic algorithms, genetic programming",
URL = "http://www-eksl.cs.umass.edu/papers/ijcai95-msa_95-66.pdf",
abstract = "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.",
notes = "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.",
size = "11 pages",
}
@Article{atkin:1995:AB,
author = "Marc S. Atkin and Paul R. Cohen",
title = "Monitoring Strategies for Embedded Agents: Experiments
and Analysis",
journal = "Adaptive Behavior",
year = "1995",
volume = "4",
number = "2",
pages = "125--172",
month = "Fall",
keywords = "genetic algorithms, genetic programming, Monitoring,
embedded agents, planning",
URL = "http://www-eksl.cs.umass.edu/papers/atkin96.pdf",
abstract = "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.",
}
@InProceedings{Atkins:2010:cec,
author = "Daniel L Atkins and Roman Klapaukh and Will N Browne
and Mengjie Zhang",
title = "Evolution of aesthetically pleasing images without
human-in-the-loop",
booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-6910-9",
abstract = "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.",
doi = "doi:10.1109/CEC.2010.5586283",
notes = "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
\cite{5586283}",
}
@InProceedings{Atkins:2011:ADIGPAtAFEfIC,
title = "A Domain Independent Genetic Programming Approach to
Automatic Feature Extraction for Image Classification",
author = "Daniel Atkins and Kourosh Neshatian and Mengjie
Zhang",
pages = "238--245",
booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary
Computation",
year = "2011",
editor = "Alice E. Smith",
month = "5-8 " # jun,
address = "New Orleans, USA",
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "CEC2011 sponsored by the IEEE Computational
Intelligence Society, and previously sponsored by the
EPS and the IET.",
}
@InProceedings{atkinson-abutridy:1999:A,
author = "John A. Atkinson-Abutridy and Julio R. Carrasco-Leon",
title = "An evolutionary model for dynamically controlling a
behavior-based autonomous agent",
booktitle = "Late Breaking Papers at the 1999 Genetic and
Evolutionary Computation Conference",
year = "1999",
editor = "Scott Brave and Annie S. Wu",
pages = "16--24",
address = "Orlando, Florida, USA",
month = "13 " # jul,
notes = "GECCO-99LB",
}
@InProceedings{atlan:1994:gpjss,
author = "Laurent Atlan and Jerome Bonnet and Martine Naillon",
title = "Learning Distributed Reactive Strategies by Genetic
Programming for the General Job Shop Problem",
booktitle = "Proceedings of the 7th annual Florida Artificial
Intelligence Research Symposium",
year = "1994",
address = "Pensacola, Florida, USA",
month = may,
organisation = "Dassault-Aviation, Artificial Intelligence
Department",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
URL = "ftp://ftp.ens.fr/pub/reports/biologie/disgajsp.ps.Z",
size = "11 pages",
abstract = "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.",
notes = "{"}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",
}
@InProceedings{Atmosukarto:2010:UGP:1904935.1906046,
author = "Indriyati Atmosukarto and Linda G. Shapiro and Carrie
Heike",
title = "The Use of Genetic Programming for Learning 3{D}
Craniofacial Shape Quantifications",
booktitle = "Proceedings of the 2010 20th International Conference
on Pattern Recognition",
year = "2010",
editor = "Aytul Ercil",
pages = "2444--2447",
address = "Istanbul, Turkey",
month = "23-26 " # aug,
organisation = "International Association for Pattern Recognition
(IAPR)",
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming, 3D Shape
quantification",
isbn13 = "978-0-7695-4109-9",
URL = "www.cs.washington.edu/research/VACE/Multimedia/icpr10_Atmosukarto.pdf",
URL = "http://grail.cs.washington.edu/pub/papers/atmosukarto2010uog.pdf",
doi = "doi:10.1109/ICPR.2010.598",
acmid = "1906046",
size = "4 pages",
abstract = "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].",
notes = "ICPR '10",
}
@PhdThesis{AtmosukartoPhd,
author = "Indriyati Atmosukarto",
title = "{3D} Shape Analysis for Quantification,
Classification, and Retrieval",
school = "Computer Science and Engineering, University of
Washington",
year = "2010",
address = "USA",
keywords = "genetic algorithms, genetic programming",
URL = "http://grail.cs.washington.edu/theses/AtmosukartoPhd.pdf",
size = "139 pages",
abstract = "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.",
notes = "GPLAB, Matlab",
}
@Article{Atmosukarto:2011:GPEM,
author = "Indriyati Atmosukarto",
title = "{GPLAB}: software review",
journal = "Genetic Programming and Evolvable Machines",
year = "2012",
volume = "12",
number = "4",
pages = "457--459",
month = dec,
note = "Software Review",
keywords = "genetic algorithms, genetic programming",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-011-9142-5",
size = "3 pages",
notes = "Matlab",
}
@InProceedings{sbrn2000meta029,
author = "Douglas A. Augusto and Helio J. C. Barbosa",
title = "Symbolic Regression via Genetic Programming",
booktitle = "{VI} Brazilian Symposium on Neural Networks
(SBRN'00)",
year = "2000",
pages = "173",
address = "Rio de Janeiro, RJ, Brazil",
month = jan # " 22-25",
note = "VI Simposio Brasileiro de Redes Neurais",
keywords = "genetic algorithms, genetic programming",
identifier = "sbrn2000article029",
language = "eng",
source = "sbrn2000",
URL = "http://csdl.computer.org/comp/proceedings/sbrn/2000/0856/00/08560173abs.htm",
doi = "doi:10.1109/SBRN.2000.889734",
abstract = "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.",
}
@InProceedings{Augusto:2008:gecco,
author = "Douglas A. Augusto and Helio J. C. Barbosa and Nelson
F. F. Ebecken",
title = "Coevolution of data samples and classifiers integrated
with grammatically-based genetic programming for data
classification",
booktitle = "GECCO '08: Proceedings of the 10th annual conference
on Genetic and evolutionary computation",
year = "2008",
editor = "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",
isbn13 = "978-1-60558-130-9",
pages = "1171--1178",
address = "Atlanta, GA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1171.pdf",
doi = "doi:10.1145/1389095.1389328",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "12-16 " # jul,
keywords = "genetic algorithms, genetic programming, competitive
coevolution, context-free grammar, data
classification",
notes = "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 \cite{1389328}",
}
@InProceedings{Augusto:2010:gecco,
author = "Douglas Adriano Augusto and Helio Jose Correa Barbosa
and Nelson Francisco Favilla Ebecken",
title = "Coevolutionary multi-population genetic programming
for data classification",
booktitle = "GECCO '10: Proceedings of the 12th annual conference
on Genetic and evolutionary computation",
year = "2010",
editor = "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",
isbn13 = "978-1-4503-0072-8",
pages = "933--940",
keywords = "genetic algorithms, genetic programming, distributed
genetic programming",
month = "7-11 " # jul,
organisation = "SIGEVO",
address = "Portland, Oregon, USA",
doi = "doi:10.1145/1830483.1830650",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstrct = "This work presents a new evolutionary ensemble method
for data classification, which is inspired by the
concepts of bagging and boosting, and aims at combining
their good features while avoiding their weaknesses.
The approach is based on a distributed
multiple-population genetic programming (GP) algorithm
which exploits the technique of coevolution at two
levels. On the inter-population level the populations
cooperate in a semi-isolated fashion, whereas on the
intrapopulation level the candidate classifiers
coevolve competitively with the training data samples.
The final classifier is a voting committee composed by
the best members of all the populations. The
experiments performed in a varying number of
populations show that our approach outperforms both
bagging and boosting for a number of benchmark
problems.",
notes = "Also known as \cite{1830650} GECCO-2010 A joint
meeting of the nineteenth international conference on
genetic algorithms (ICGA-2010) and the fifteenth annual
genetic programming conference (GP-2010)",
}
@InProceedings{Augusto:2011:GECCOcomp,
author = "Douglas A. Augusto and Helio J. C. Barbosa and Andre
M. S. Barreto and Heder S. Bernardino",
title = "A new approach for generating numerical constants in
grammatical evolution",
booktitle = "GECCO '11: Proceedings of the 13th annual conference
companion on Genetic and evolutionary computation",
year = "2011",
editor = "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",
isbn13 = "978-1-4503-0690-4",
keywords = "genetic algorithms, genetic programming, grammatical
evolution: Poster",
pages = "193--194",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001858.2001966",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{2001966} Distributed on CD-ROM at
GECCO-2011.
ACM Order Number 910112.",
}
@InProceedings{Augustsson:2002:gecco,
author = "Peter Augustsson and Krister Wolff and Peter Nordin",
title = "Creation Of {A} Learning, Flying Robot By Means Of
Evolution",
booktitle = "GECCO 2002: Proceedings of the Genetic and
Evolutionary Computation Conference",
editor = "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",
year = "2002",
pages = "1279--1285",
address = "New York",
publisher_address = "San Francisco, CA 94104, USA",
month = "9-13 " # jul,
publisher = "Morgan Kaufmann Publishers",
keywords = "genetic algorithms, genetic programming, evolutionary
robotics, evolutionary algorithm, flying",
ISBN = "1-55860-878-8",
URL = "http://fy.chalmers.se/~wolff/Papers/ANW_gecco02.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-22.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/ROB196.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/ROB196.pdf",
size = "7 pages",
abstract = "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.",
notes = "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.",
}
@InProceedings{aurnhammer:evows07,
author = "Melanie Aurnhammer",
title = "Evolving Texture Features by Genetic Programming",
booktitle = "Applications of Evolutionary Computing,
EvoWorkshops2007: {EvoCOMNET}, {EvoFIN}, {EvoIASP},
{EvoInteraction}, {EvoMUSART}, {EvoSTOC},
{EvoTransLog}",
year = "2007",
month = "11-13 " # apr,
editor = "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",
series = "LNCS",
volume = "4448",
publisher = "Springer Verlag",
address = "Valencia, Spain",
pages = "351--358",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-540-71804-8",
doi = "doi:10.1007/978-3-540-71805-5_38",
abstract = "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.",
notes = "EvoWorkshops2007",
}
@TechReport{austin:2003:WP,
author = "M. P. Austin and R. G. Bates and M. A. H. Dempster and
S. N. Williams",
title = "Adaptive systems for foreign exchange trading",
institution = "Judge Institute of Management, University of
Cambridge",
year = "2003",
type = "Working paper",
number = "WP 15/2003",
address = "UK",
keywords = "genetic algorithms, genetic programming",
URL = "http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/WP1503.pdf",
notes = "Research Papers in Management Studies. To appear in
Eclectic \cite{Austin:2004:E}
See \cite{Austin:2004:QF}
",
size = "12 pages",
}
@Article{Austin:2004:E,
author = "Mark Austin and Graham Bates and Michael Dempster and
Stacy Williams",
title = "Adaptive systems for foreign exchange trading",
journal = "Eclectic",
year = "2004",
volume = "18",
pages = "21--26",
month = "Autumn",
keywords = "genetic algorithms, genetic programming",
URL = "http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/adaptive.pdf",
size = "6 pages",
abstract = "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.",
}
@Article{Austin:2004:QF,
title = "Adaptive systems for foreign exchange trading",
author = "Mark P. Austin and Graham Bates and Michael A. H.
Dempster and Vasco Leemans and Stacy N. Williams",
journal = "Quantitative Finance",
month = aug,
number = "4",
pages = "37--45",
publisher = "Routledge, part of the Taylor {\&} Francis Group",
volume = "4",
year = "2004",
keywords = "genetic algorithms, genetic programming, fx trading",
citeulike-article-id = "98141",
ISSN = "1469-7688",
URL = "http://www-cfr.jbs.cam.ac.uk/archive/PRESENTATIONS/seminars/2006/dempster2.pdf",
doi = "doi:10.1080/14697680400008593",
size = "9 pages",
abstract = "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.",
notes = "Also in Eclectic 18 Autumn (2004) pp21-26
\cite{Austin:2004:E} www.eclectic.co.uk and technical
report WP15/2003 \cite{austin:2003:WP}
",
}
@InProceedings{autones:2004:eurogp,
author = "Mathieu Autones and Aryel Beck and Phillippe Camacho
and Nicolas Lassabe and Herve Luga and Franccois
Scharffe",
title = "Evaluation of chess position by modular neural network
generated by genetic algorithm",
booktitle = "Genetic Programming 7th European Conference, EuroGP
2004, Proceedings",
year = "2004",
editor = "Maarten Keijzer and Una-May O'Reilly and Simon M.
Lucas and Ernesto Costa and Terence Soule",
volume = "3003",
series = "LNCS",
pages = "1--10",
address = "Coimbra, Portugal",
publisher_address = "Berlin",
month = "5-7 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-21346-5",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=1",
abstract = "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",
notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in
conjunction with EvoCOP2004 and EvoWorkshops2004",
}
@InProceedings{Aversano:2005:WSEC,
author = "Lerina Aversano and Massimiliano {Di Penta} and Kunal
Taneja",
title = "A genetic programming approach to support the design
of service compositions",
booktitle = "Proceedings of the first International Workshop of
Engineering Service Compositions, WESC'05",
year = "2005",
editor = "Christian Zirpins and Guadalupe Ortiz and Winfried
Lamersdorf and Wolfgang Emmerich",
pages = "17--24",
address = "Amsterdam, The Netherlands",
number = "RC23821 (W0512-008)",
month = dec,
series = "IBM Research Reports",
keywords = "genetic algorithms, genetic programming",
URL = "http://domino.research.ibm.com/library/cyberdig.nsf/papers/DE71563B7B69D362852570D000548D0D/$File/rc23821.pdf",
notes = "Slides
http://www.rcost.unisannio.it/mdipenta/papers/wesc05.pdf
parts of \cite{WESC05}",
}
@Article{Aversano:2006:IJCSSE,
author = "Lerina Aversano and Massimiliano {Di Penta} and Kunal
Taneja",
title = "A genetic programming approach to support the design
of service compositions",
journal = "International Journal of Computer Systems Science \&
Engineering",
year = "2006",
volume = "21",
number = "4",
pages = "247--254",
month = jul,
organisation = "Curtin University of Technology, Australia",
publisher = "CRL Publishing, admin@crlpublishing.co.uk",
keywords = "genetic algorithms, genetic programming, SBSE, service
compositions, distributed software, workflow",
ISSN = "0267 6192",
URL = "http://www.rcost.unisannio.it/mdipenta/papers/csse06.pdf",
size = "8 pages",
oai = "oai:CiteSeerXPSU:10.1.1.145.843",
abstract = "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.",
notes = "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
\cite{Rodriguez-Mier:2010:EI}, cites \{1068189} GECCO
2005. SeCSEP",
}
@InProceedings{conf/hais/AvilaGV09,
title = "Multi-label Classification with Gene Expression
Programming",
author = "J. L. Avila and Eva Lucrecia {Gibaja Galindo} and
Sebastian Ventura",
bibdate = "2009-06-24",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/hais/hais2009.html#AvilaGV09",
booktitle = "Hybrid Artificial Intelligence Systems, 4th
International Conference, {HAIS} 2009, Salamanca,
Spain, June 10-12, 2009. Proceedings",
publisher = "Springer",
year = "2009",
volume = "5572",
editor = "Emilio Corchado and Xindong Wu and Erkki Oja and
{\'A}lvaro Herrero and Bruno Baruque",
isbn13 = "978-3-642-02318-7",
pages = "629--637",
series = "Lecture Notes in Computer Science",
URL = "http://dx.doi.org/10.1007/978-3-642-02319-4",
keywords = "genetic algorithms, genetic programming, gene
expression programming",
}
@InCollection{Avila-Jimenez:2010:HAIS,
author = "Jose Luis Avila-Jimenez and Eva Gibaja and Sebastian
Ventura",
title = "Evolving Multi-label Classification Rules with Gene
Expression Programming: {A} Preliminary Study",
booktitle = "Hybrid Artificial Intelligence Systems",
year = "2010",
series = "Lecture Notes in Computer Science",
editor = "Emilio Corchado and Manuel {Grana Romay} and Alexandre
{Manhaes Savio}",
publisher = "Springer",
pages = "9--16",
volume = "6077",
address = "San Sebastian, Spain",
month = jun # " 23-25",
doi = "doi:10.1007/978-3-642-13803-4_2",
keywords = "genetic algorithms, genetic programming, gene
expression programming",
abstract = "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.",
affiliation = "University of Cordoba Department of Computer Sciences
and Numerical Analysis",
}
@Article{Aytek:2008:JH,
author = "Ali Aytek and Ozgur Kisi",
title = "A genetic programming approach to suspended sediment
modelling",
journal = "Journal of Hydrology",
year = "2008",
volume = "351",
number = "3-4",
pages = "288--298",
month = "15 " # apr,
keywords = "genetic algorithms, genetic programming, Suspended
sediment load, Rating curves, Soft computing",
doi = "doi:10.1016/j.jhydrol.2007.12.005",
abstract = "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.",
notes = "Gaziantep University, Civil Engineering Department,
Hydraulics Division, 27310 Gaziantep, Turkey
Erciyes University, Civil Engineering Department,
Hydraulics Division, 38039 Kayseri, Turkey",
}
@Article{Aytek:2008:JESS,
author = "Ali Aytek and M Asce and Murat Alp",
title = "An application of artificial intelligence for
rainfall-runoff modeling",
journal = "Journal of Earth System Science",
year = "2008",
volume = "117",
number = "2",
pages = "145--155",
month = apr,
email = "aytek@gantep.edu.tr",
keywords = "genetic algorithms, genetic programming, Gene
Expression Programming",
URL = "http://www.ias.ac.in/jess/apr2008/d093.pdf",
size = "11 pages",
abstract = "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.",
}
@InCollection{aytekin:1995:4-OPmap,
author = "Tevfik Aytekin and Emin Erkan Korkmaz and Halil Altay
G{\"{u}}vennir",
title = "An application of genetic programming to the 4-{OP}
problem using map-trees",
booktitle = "Progress in Evolutionary Computation",
publisher = "Springer-Verlag",
year = "1995",
editor = "Xin Yao",
volume = "956",
series = "Lecture Notes in Artificial Intelligence",
pages = "28--40",
address = "Heidelberg, Germany",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.bilkent.edu.tr/tech-reports/1994/BU-CEIS-9441.ps.z",
URL = "http://citeseer.ist.psu.edu/16240.html",
abstract = "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.",
notes = "
Also technical report BU-CEIS-9441 Bilkent University
Department of Computer Engineering",
}
@InProceedings{azad:2002:gecco,
author = "R. Muhammad Atif Azad and Conor Ryan and Mark E. Burke
and Ali R. Ansari",
title = "A Re-examination Of The Cart Centering Problem Using
The Chorus System",
booktitle = "GECCO 2002: Proceedings of the Genetic and
Evolutionary Computation Conference",
editor = "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",
year = "2002",
pages = "707--715",
address = "New York",
publisher_address = "San Francisco, CA 94104, USA",
month = "9-13 " # jul,
publisher = "Morgan Kaufmann Publishers",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-878-8",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/GP144.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/GP144.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf",
notes = "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",
}
@InProceedings{azad:2002:gecco:workshop,
title = "A Position Independent Evolutionary Automatic
Programming Algorithm - The {Chorus} System",
author = "R. Muhammad Atif Azad",
pages = "260--263",
booktitle = "Graduate Student Workshop",
editor = "Sean Luke and Conor Ryan and Una-May O'Reilly",
year = "2002",
month = "8 " # jul,
publisher = "AAAI",
address = "New York",
publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
notes = "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",
}
@InProceedings{azad:2003:gecco,
author = "R. Muhammad Atif Azad and Conor Ryan",
title = "Structural Emergence with Order Independent
Representations",
booktitle = "Genetic and Evolutionary Computation -- GECCO-2003",
editor = "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",
year = "2003",
pages = "1626--1638",
address = "Chicago",
publisher_address = "Berlin",
month = "12-16 " # jul,
volume = "2724",
series = "LNCS",
ISBN = "3-540-40603-4",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
abstract = "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.",
notes = "GECCO-2003. A joint meeting of the twelfth
International Conference on Genetic Algorithms
(ICGA-2003) and the eighth Annual Genetic Programming
Conference (GP-2003)",
}
@PhdThesis{Azad:thesis,
author = "Raja Muhammad Atif Azad",
title = "A Position Independent Representation for Evolutionary
Automatic Programming Algorithms - The Chorus System",
school = "University of Limerick",
year = "2003",
address = "Ireland",
month = dec,
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/azad_thesis.ps.gz",
size = "212 pages",
keywords = "genetic algorithms, genetic programming, Chorus
System, Grammatical Evolution",
abstract = "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.",
}
@Article{Azad:2004:ASC,
author = "R. Muhammad Atif Azad and Ali R. Ansari and Conor Ryan
and Michael Walsh and Tim McGloughlin",
title = "An evolutionary approach to Wall Sheer Stress
prediction in a grafted artery",
journal = "Applied Soft Computing",
publisher = "Elsevier",
year = "2004",
volume = "4",
number = "2",
pages = "139--148",
month = may,
keywords = "genetic algorithms, genetic programming, grammatical
evolution, chorus system, Wall Shear Stress, Laser
Doppler anemometry, Mathematical modeling,
Computational Fluid Dynamics",
ISSN = "1568-4946",
doi = "doi:10.1016/j.asoc.2003.11.001",
abstract = "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.",
notes = "http://www.elsevier.com/wps/find/journaldescription.cws_home/621920/description#description",
}
@InCollection{azad:2005:GPTP,
author = "R. Muhammad Atif Azad and Conor Ryan",
title = "An Examination of Simultaneous Evolution of Grammars
and Solutions",
booktitle = "Genetic Programming Theory and Practice {III}",
year = "2005",
editor = "Tina Yu and Rick L. Riolo and Bill Worzel",
volume = "9",
series = "Genetic Programming",
chapter = "10",
pages = "141--158",
address = "Ann Arbor",
month = "12-14 " # may,
publisher = "Kluwer",
keywords = "genetic algorithms, genetic programming, Grammatical
Evolution, Evolving Grammars, Grammatical ADFs,
Generative Representations",
ISBN = "0-387-28110-X",
size = "18 pages",
abstract = "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.",
notes = "part of \cite{yu:2005:GPTP} Published Jan 2006 after
the workshop",
}
@InProceedings{Azad:2008:geccocomp,
author = "R. Muhammad Atif Azad and Conor Ryan",
title = "Gecco 2008 grammatical evolution tutorial",
year = "2008",
editor = "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",
isbn13 = "978-1-60558-131-6",
booktitle = "GECCO-2008 tutorials",
pages = "2339--2366",
address = "Atlanta, GA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p2339.pdf",
doi = "doi:10.1145/1388969.1389058",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "12-16 " # jul,
keywords = "genetic algorithms, genetic programming, chorus,
GAuGE, genetic algorithms (GA), grammars, linear
strings",
notes = "Distributed on CD-ROM at GECCO-2008
ACM Order Number 910081. Also known as \cite{1389058}",
}
@InProceedings{Azad:2010:gecco,
author = "R. Muhammad Atif Azad and Conor Ryan",
title = "Abstract functions and lifetime learning in genetic
programming for symbolic regression",
booktitle = "GECCO '10: Proceedings of the 12th annual conference
on Genetic and evolutionary computation",
year = "2010",
editor = "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",
isbn13 = "978-1-4503-0072-8",
pages = "893--900",
keywords = "genetic algorithms, genetic programming",
month = "7-11 " # jul,
organisation = "SIGEVO",
address = "Portland, Oregon, USA",
doi = "doi:10.1145/1830483.1830645",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{1830645} GECCO-2010 A joint
meeting of the nineteenth international conference on
genetic algorithms (ICGA-2010) and the fifteenth annual
genetic programming conference (GP-2010)",
}
@InProceedings{Azad:2011:GECCO,
author = "R. Muhammad Atif Azad and Conor Ryan",
title = "Variance based selection to improve test set
performance in genetic programming",
booktitle = "GECCO '11: Proceedings of the 13th annual conference
on Genetic and evolutionary computation",
year = "2011",
editor = "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",
isbn13 = "978-1-4503-0557-0",
pages = "1315--1322",
keywords = "genetic algorithms, genetic programming",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001576.2001754",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{2001754} GECCO-2011 A joint
meeting of the twentieth international conference on
genetic algorithms (ICGA-2011) and the sixteenth annual
genetic programming conference (GP-2011)",
}
@InProceedings{azam:1998:dsi:cs,
author = "Farooq Azam and H. F. VanLandingham",
title = "Dynamic Systems Identification: {A} Comparitive
Study",
booktitle = "Late Breaking Papers at the Genetic Programming 1998
Conference",
year = "1998",
editor = "John R. Koza",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "Stanford, CA, USA",
month = "22-25 " # jul,
publisher = "Stanford University Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "GP-98LB",
}
@InProceedings{azam:1998:dsiGP,
author = "Farooq Azam and H. F. VanLandingham",
title = "Dynamic Systems Identification using Genetic
Programming",
booktitle = "Late Breaking Papers at the Genetic Programming 1998
Conference",
year = "1998",
editor = "John R. Koza",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "Stanford, CA, USA",
month = "22-25 " # jul,
publisher = "Stanford University Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "GP-98LB",
}
@Article{MDAZAMATHULLA2008477,
author = "H. Md Azamathulla and A. Ab. Ghani and N. A. Zakaria
and S. H. Lai and C. K. Chang and C. S. Leow and Z.
Abuhasan",
title = "Genetic programming to predict ski-jump bucket
spill-way scour",
journal = "Journal of Hydrodynamics, Ser. B",
volume = "20",
number = "4",
pages = "477--484",
year = "2008",
ISSN = "1001-6058",
doi = "doi:10.1016/S1001-6058(08)60083-9",
URL = "http://www.sciencedirect.com/science/article/B8CX5-4TCY8GV-B/2/f3004ab0cd7ed153a22b7f5d637afc89",
month = aug,
keywords = "genetic algorithms, genetic programming, neural
networks, spillway scour, ski-jump bucket",
abstract = "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.",
}
@Article{Azamathulla:2010:JHE,
author = "H. Md. Azamathulla and Aminuddin {Ab Ghani} and Nor
Azazi Zakaria and Aytac Guven",
title = "Genetic Programming to Predict Bridge Pier Scour",
journal = "Journal of Hydraulic Engineering",
year = "2010",
volume = "136",
number = "3",
pages = "165--169",
keywords = "genetic algorithms, genetic programming, Local scour,
Bridge pier, Artificial neural networks, Radial basis
function",
doi = "doi:10.1061/(ASCE)HY.1943-7900.0000133",
size = "5 page",
abstract = "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.",
}
@Article{Azamathulla2011,
author = "H. Md. Azamathulla and Aytac Guven and Yusuf Kagan
Demir",
title = "Linear genetic programming to scour below submerged
pipeline",
journal = "Ocean Engineering",
volume = "38",
number = "8-9",
pages = "995--1000",
year = "2011",
month = jun,
ISSN = "0029-8018",
doi = "doi:10.1016/j.oceaneng.2011.03.005",
URL = "http://www.sciencedirect.com/science/article/B6V4F-52M3TGW-1/2/279184e6554e6b6977d8b9f0180c9f53",
keywords = "genetic algorithms, genetic programming, Local scour,
Neuro-fuzzy, Pipelines",
abstract = "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.",
}
@Article{Azamathulla:2012:JH,
author = "H. Md. Azamathulla and Z. Ahmad",
title = "{GP} approach for critical submergence of intakes in
open channel flows",
journal = "Journal of Hydroinformatics",
note = "In Press, Uncorrected Proof",
keywords = "genetic algorithms, genetic programming, critical
submergence, intakes, open channel",
ISSN = "1464-7141",
URL = "http://www.iwaponline.com/jh/up/pdf/jh2012089.pdf",
doi = "doi:10.2166/hydro.2012.089",
size = "7 pages",
abstract = "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.",
notes = "Vortex, water dam",
}
@Article{Azamathulla:2012a:JH,
author = "H. Md. Azamathulla",
title = "Gene-expression programming to predict scour at a
bridge abutment",
journal = "Journal of Hydroinformatics",
year = "2012",
volume = "14",
number = "2",
pages = "324--331",
keywords = "genetic algorithms, genetic programming, gene
expression programming, artificial neural networks,
bridge abutments, local scour, radial basis function",
ISSN = "1464-7141",
URL = "http://www.iwaponline.com/jh/014/0324/0140324.pdf",
doi = "doi:doi:10.2166/hydro.2011.135",
size = "8 pages",
abstract = "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.",
notes = "ANN, RBF. 'The overall performance of the GEP model is
superior to the ANN model.' p330",
}
@InProceedings{eurogp:AzariaS05,
author = "Yaniv Azaria and Moshe Sipper",
editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre
Collet and Jano I. {van Hemert} and Marco Tomassini",
title = "Using {GP}-Gammon: Using Genetic Programming to Evolve
Backgammon Players",
booktitle = "Proceedings of the 8th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3447",
year = "2005",
address = "Lausanne, Switzerland",
month = "30 " # mar # " - 1 " # apr,
organisation = "EvoNet",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-25436-6",
pages = "132--142",
URL = "http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=132",
doi = "doi:10.1007/b107383",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in
conjunction with EvoCOP2005 and EvoWorkshops2005",
}
@Article{azaria:2005:GPEM,
author = "Yaniv Azaria and Moshe Sipper",
title = "{GP}-Gammon: Genetically Programming Backgammon
Players",
journal = "Genetic Programming and Evolvable Machines",
year = "2005",
volume = "6",
number = "3",
pages = "283--300",
month = sep,
note = "Published online: 12 August 2005",
keywords = "genetic algorithms, genetic programming, backgammon,
self-learning, STGP, demes, coevolution",
ISSN = "1389-2576",
URL = "http://www.cs.bgu.ac.il/~sipper/papabs/gpgammon.pdf",
doi = "doi:10.1007/s10710-005-2990-0",
abstract = "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.",
notes = "ECJ",
}
@InProceedings{baber:2002:EuroGP,
title = "Evolutionary Algorithm Approach to Bilateral
Negotiations",
author = "Vinaysheel Baber and Rema Ananthanarayanan and Krishna
Kummamuru",
editor = "James A. Foster and Evelyne Lutton and Julian Miller
and Conor Ryan and Andrea G. B. Tettamanzi",
booktitle = "Genetic Programming, Proceedings of the 5th European
Conference, EuroGP 2002",
volume = "2278",
series = "LNCS",
pages = "202--211",
publisher = "Springer-Verlag",
address = "Kinsale, Ireland",
publisher_address = "Berlin",
month = "3-5 " # apr,
year = "2002",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-43378-3",
abstract = "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.",
notes = "EuroGP'2002, part of \cite{lutton:2002:GP}",
}
@InProceedings{babovic:1994:camh,
author = "Vladan Babovic and A. W. Minns",
title = "Use of computational adaptive methodologies in
hydroinformatics",
booktitle = "Proceedings of the first international conference on
hydroinformatics, Delft, Netherlands",
year = "1994",
editor = "A. Verwey and A. W. Minns and V. Babovic and C.
Maksimovic",
pages = "201--210",
publisher_address = "P. O. Box 1675, Rotterdam, Netherlands",
month = "19--23 " # sep,
publisher = "A. A. Balkema",
keywords = "genetic algorithms, genetic programming",
ISBN = "90-5410-512-7",
URL = "http://www.amazon.co.uk/Hydroinformatics-Proceedings-International-Conference-Netherlands/dp/9054105127",
abstract = "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.",
notes = "Does not present clear winner (ANN, GP or traditional)
upto reader to choose approriate to their
problem.
IHE-Delft, The Netherlands
",
}
@InProceedings{babovic:1995:gmibed,
author = "Vladan Babovic",
title = "Genetic Model Induction Based on Experimental Data",
booktitle = "Proceedings of the XXVIth Congress of International
Association for Hydraulics Research",
year = "1995",
editor = "J. Gardiner",
address = "London, UK",
month = "11--15 " # sep,
organisation = "International Association of Hydraulic Research",
publisher = "Thomas Telford Ltd",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7277-2059-7",
URL = "http://www.iahr.net/e-shop/store/viewItem.asp?idProduct=91",
abstract = "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",
notes = "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
",
}
@PhdThesis{babovic:thesis,
author = "Vladan Babovic",
title = "Emergence, Evolution, Intelligence: Hydroinformatics",
school = "International Institute for Infrastructural, Hydraulic
and Environmental Engineering and Technical University
Delft",
year = "1996",
address = "The Netherlands",
month = "20 " # mar,
note = "Published by A. A. Balkema Publishers",
keywords = "genetic algorithms, genetic programming",
ISBN = "90-5410-404-X",
URL = "http://repository.tudelft.nl/view/ir/uuid%3A58c50efe-4a6a-40b4-8c60-2b81d629b49c/",
URL = "http://repository.tudelft.nl/assets/uuid:58c50efe-4a6a-40b4-8c60-2b81d629b49c/EMERGENCE__EVOLUTION__INTELLIGENCE_HYDROINFORMATICS.PDF",
size = "348 pages",
abstract = "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.",
notes = "Promotor: Abbott, M.B. See also \cite{babovic:book}",
}
@Book{babovic:book,
author = "Vladan Babovic",
title = "Emergence, evolution, intelligence; Hydroinformatics -
{A} study of distributed and decentralised computing
using intelligent agents",
publisher = "A. A. Balkema Publishers",
year = "1996",
address = "Rotterdam, Holland",
keywords = "genetic algorithms, genetic programming",
isbn-13 = "978-90-5410-404-9",
ISBN = "90-5410-404-X",
abstract = "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.",
notes = "publication of \cite{babovic:thesis}",
size = "344 pages",
}
@InCollection{babovic:1996:wmbAI,
author = "V. Babovic",
title = "Can water resources management benefit from artificial
intelligence?",
booktitle = "Computation Fluid Dynamics: Bunte Bilder in der
Praxis",
publisher = "Meinz Verlag",
year = "1996",
editor = "J. Kongeter",
pages = "337--358",
address = "Aachen, Germany",
keywords = "genetic algorithms, genetic programming",
notes = "26. IWASA International Wasserbau-Symposium Aachen
1995/96
",
size = "pages",
}
@Article{babovic:1997:eehd1,
author = "Vladan Babovic and Michael B. Abbott",
title = "The evolution of equation from hydraulic data, Part
{I}: Theory",
journal = "Journal of Hydraulic Research",
year = "1997",
volume = "35",
number = "3",
pages = "397--410",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1080/00221689709498420",
size = "14 pages",
abstract = "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.",
notes = "See also \cite{babovic:1997:eehd2}",
}
@Article{babovic:1997:eehd2,
author = "Vladan Babovic and Michael B. Abbott",
title = "The evolution of equation from hydraulic data, Part
{II}: Applications",
journal = "Journal of Hydraulic Research",
year = "1997",
volume = "35",
number = "3",
pages = "411--430",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1080/00221689709498421",
size = "20 pages",
abstract = "This second part of the paper
\cite{babovic: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",
}
@InCollection{babovic:1997:mfnls,
author = "Vladan Babovic",
title = "On the Modelling and Forecasting of Non-linear
Systems",
booktitle = "Operational Water Management: Proceedings of the
European Water Resources Association Conference,
Copenhagen, Denmark, 3-6 September 1997",
publisher = "Balkema",
year = "1997",
editor = "J. C. Refsgaard and E. A. Karalis",
pages = "195--202",
address = "Rotterdam",
keywords = "genetic algorithms, genetic programming",
ISBN = "90-5410-897-5",
URL = "http://www.amazon.co.uk/gp/search?index=books&linkCode=qs&keywords=9054108975",
size = "8 pages",
}
@InProceedings{babovic:1998:stdlkm,
author = "V. Babovic",
title = "Sediment transport data - Large knowledge mine",
booktitle = "Proceedings of the Third International Conference on
Hydroscience and Engineering",
year = "1998",
address = "Cottbus, Germany",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{babovic:1998:dmtsmf,
author = "V. Babovic",
title = "A data mining approach to time series modelling and
forecasting",
booktitle = "Proceeding of the Third International Conference on
Hydroinformatics",
year = "1998",
editor = "Babovic and Larsen",
pages = "847--856",
address = "Copenhagen, Denmark",
publisher_address = "Rotterdam",
publisher = "Balkema",
keywords = "genetic algorithms, genetic programming, Vltava River
system, flood control and protection of Prague,
artificial neural networks",
ISBN = "90-5410-983-1",
notes = "Hydroinformatics'98",
}
@InProceedings{babovic:1998:mstGP,
author = "Vladan Babovic",
title = "Mining sediment transport data with genetic
programming",
booktitle = "Proceedings of the First International Conference on
New Information Technologies for Decision Making in
Civil Engineering",
year = "1998",
pages = "875--886",
address = "Montreal, Canada",
month = "11-13 " # oct,
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{babovic:1999:cskd-veg,
author = "Vladan Babovic and Maarten Keijzer",
title = "Computer supported knowledge discovery - {A} case
study in flow resistance induced by vegetation",
booktitle = "Proceedings of the XXVIII Congress of International
Association for Hydraulic Research",
year = "1999",
address = "Graz, Austria",
month = "22-27 " # aug,
keywords = "genetic algorithms, genetic programming",
size = "7 pages",
}
@InProceedings{babovic:1999:d2k,
author = "V. Babovic and M. Keijzer",
title = "Data to knowledge - The new scientific paradigm",
booktitle = "Water Industry Systems",
year = "1999",
editor = "D. Savic and G. Walters",
pages = "3--14",
address = "Exeter, United Kingdom",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{me15,
author = "Vladan Babovic and Maarten Keijzer",
title = "Evolutionary algorithms approach to induction of
differential equations",
booktitle = "Proceedings of the Fourth International Conference on
Hydroinformatics",
address = "Iowa City, USA",
year = "2000",
keywords = "genetic algorithms, genetic programming",
}
@Article{babovic:1999:td2ksed,
author = "Vladan Babovic",
title = "Data Mining and Knowledge Discovery in Sediment
Transport",
journal = "Computer-Aided Civil and Infrastructure Engineering",
year = "2000",
volume = "15",
number = "5",
pages = "383--389",
month = sep,
keywords = "genetic algorithms, genetic programming",
ISSN = "1093-9687",
doi = "doi:10.1111/0885-9507.00202",
size = "7 pages",
abstract = "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.",
notes = "Article first published online: 17 DEC 2002",
}
@Article{babovic:1999:GPmie,
author = "Vladan Babovic and Maarten Keijzer",
title = "Genetic programming as a model induction engine",
journal = "Journal of Hydroinformatics",
year = "2000",
volume = "1",
number = "1",
pages = "35--60",
month = jan,
keywords = "genetic algorithms, genetic programming, data mining,
knowledge discovery",
ISSN = "1464-7141",
URL = "http://www.iwaponline.com/jh/002/jh0020035.htm",
size = "26 pages",
abstract = "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.",
notes = "dimensionally aware GP. Additional river water flow
resistance caused by flexible vegetation closure and
strong typing (STGP). dimensionally aware brood
selection. Kutija-Hong model.",
}
@InCollection{Babovic:2000:IAHR,
author = "Vladan Babovic and H. Bergmann",
title = "On Computer-Aided Discovery of Knowledge in Hydraulic
Engineering",
booktitle = "Advances in Hydraulic Research and Engineering",
publisher = "Technical University Graz",
year = "2000",
editor = "H. Bergmann",
address = "Graz",
keywords = "genetic algorithms, genetic programming",
}
@InCollection{me25,
author = "Vladan Babovic and Maarten Keijzer",
title = "On the introduction of declarative bias in knowledge
discovery computer systems",
booktitle = "New paradigms in river and estuarine management",
editor = "Peter Goodwin",
publisher = "Kluwer",
year = "2001",
keywords = "genetic algorithms, genetic programming",
notes = "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)",
}
@InProceedings{me27,
author = "Vladan Babovic and Maarten Keijzer and David Rodriguez
Aquilera and Joe Harrington",
title = "An evolutionary approach to knowledge induction:
Genetic Programming in Hydraulic Engineering",
booktitle = "Proceedings of the World Water and Environmental
Resources Congress",
year = "2001",
editor = "Don Phelps and Gerald Sehlke",
volume = "111",
pages = "64--64",
address = "Orlando, Florida, USA",
month = "20-24 " # may,
publisher = "ASCE",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.vu.nl/~mkeijzer/publications/ASCE_paper.pdf",
URL = "http://link.aip.org/link/?ASC/111/64/1",
doi = "doi:10.1061/40569(2001)64",
abstract = "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.",
notes = "World Water Congress 2001 number = 40569",
}
@Article{me24,
author = "Vladan Babovic and Jean-Philippe Drecourt and Maarten
Keijzer and Peter Friis Hansen",
title = "Modelling of water supply assets: a data mining
approach",
journal = "Urban Water",
year = "2002",
volume = "4",
number = "4",
pages = "401--414",
publisher = "Elsevier",
URL = "http://www.sciencedirect.com/science/article/B6VR2-4718F0J-1/2/e361659261f99d438f8f2207f67eedf8",
keywords = "genetic algorithms, genetic programming",
}
@Article{NordicHy,
author = "Vladan Babovic and Maarten Keijzer",
title = "Rainfall Runoff Modelling based on Genetic
Programming",
journal = "Nordic Hydrology",
year = "2002",
volume = "33",
number = "5",
pages = "331--346",
keywords = "genetic algorithms, genetic programming",
ISSN = "0029-1277",
URL = "http://www.iwaponline.com/nh/033/0331/0330331.pdf",
doi = "doi:10.2166/nh.2002.020",
abstract = "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).",
}
@Article{Babovic:2005:HP,
author = "Vladan Babovic",
title = "Data mining in hydrology",
journal = "Hydrological Processes",
year = "2005",
volume = "19",
number = "7",
pages = "1511--1515",
month = "30 " # apr,
keywords = "genetic algorithms, genetic programming",
ISSN = "1099-1085",
doi = "doi:10.1002/hyp.5862",
size = "5 pages",
abstract = "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.",
notes = "Invited Commentary",
}
@InCollection{Babovic:2006:,
author = "Vladan Babovic and Maarten Keijzer",
title = "Rainfall-Runoff Modeling Based on Genetic
Programming",
booktitle = "Encyclopedia of Hydrological Sciences",
publisher = "Wiley",
year = "2006",
editor = "Malcolm G. Anderson and Keith Beven and et al.",
month = "15 " # apr,
keywords = "genetic algorithms, genetic programming,
Hydroinformatics, symbolic regression, empirical
equations, rainfall-runoff",
isbn13 = "9780470848944",
URL = "http://onlinelibrary.wiley.com/doi/10.1002/0470848944.hsa017/abstract",
doi = "doi:10.1002/0470848944.hsa017",
abstract = "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).",
}
@InProceedings{Babovic:2007:NMHS,
author = "Vladan Babovic",
title = "Data-Driven Knowledge Discovery: Four Roads to
Vegetation-Induced Roughness Formulae",
booktitle = "Numerical Modelling of Hydrodynamics for Water
Resources: Proceedings of the International Workshop on
Numerical Modelling of Hydrodynamic Systems",
year = "2007",
editor = "Pilar Garcia Navarro and Enrique Playan",
pages = "67--76",
address = "Zaragoza, Spain",
month = "18-21 " # jun,
publisher = "Taylor \& Franics, Balkema",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-415-44056-4",
URL = "http://www.docstoc.com/docs/36112150/Numerical-Modelling-of-Hydrodynamics-for-Water-Resources",
URL = "http://www.amazon.com/Numerical-Modelling-Hydrodynamics-Water-Resources/dp/0415440564/ref=cm_cr_pr_pb_t",
notes = "http://www.unizar.es/nmhs/programme/programme.htm
published 2008?",
}
@Article{Babovic:2009:JH,
author = "Vladan Babovic",
title = "Introducing knowledge into learning based on genetic
programming",
journal = "Journal of Hydroinformatics",
year = "2009",
volume = "11",
number = "3-4",
pages = "181--193",
keywords = "genetic algorithms, genetic programming, empirical
equations, hydraulics, sediment transport, strong
typing, symbolic regression, units of measurement",
ISSN = "1464-7141",
URL = "http://www.iwaponline.com/jh/011/0181/0110181.pdf",
doi = "doi:10.2166/hydro.2009.041",
size = "13 pages",
abstract = "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.",
}
@InCollection{Babovic:2010:ECinH,
author = "Vladan Babovic and Raghuraj Rao",
title = "Evolutionary Computing in Hydrology",
booktitle = "Advances in Data-Based Approaches for Hydrologic
Modeling and Forecasting",
publisher = "World Scientific Publishing Co.",
year = "2010",
editor = "Bellie Sivakumar and Ronny Berndtsson",
chapter = "7",
pages = "347--369",
address = "Singapore",
keywords = "genetic algorithms, genetic programming",
ISBN = "981-4307-97-1",
URL = "http://ebooks.worldscinet.com/ISBN/9789814307987/9789814307987_0007.html",
abstract = "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",
notes = "http://www.worldscibooks.com/environsci/7783.html",
}
@Article{Babu:2007:EL,
author = "B. V. Babu and S. Karthik",
title = "Genetic Programming for Symbolic Regression of
Chemical Process Systems",
journal = "Engineering Letters",
volume = "14",
number = "2",
year = "2007",
pages = "42--55",
month = jun,
publisher = "International Association of Engineers",
keywords = "genetic algorithms, genetic programming",
ISSN = "1816-0948",
URL = "http://www.engineeringletters.com/issues_v14/issue_2/EL_14_2_6.pdf",
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.148.8378",
oai = "oai:CiteSeerXPSU:10.1.1.148.8378",
abstract = "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.",
notes = "http://www.engineeringletters.com/",
}
@Proceedings{Bacardit:2011:GECCOcomp,
title = "{GECCO} '11: Proceedings of the 13th annual conference
companion on Genetic and evolutionary computation",
year = "2011",
editor = "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",
address = "Dublin, Ireland",
publisher_address = "New York, NY, USA",
month = "12-16 " # jul,
organisation = "SIGEVO",
keywords = "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",
isbn13 = "978-1-4503-0690-4",
notes = "Distributed on CD-ROM at GECCO-2011.
ACM Order Number 910112.",
}
@InCollection{bachman:2000:UGAVLGA,
author = "Brandon M. Bachman",
title = "Using the Genetic Algorithm with a Variable Length
Genome for Architectural",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2000",
year = "2000",
editor = "John R. Koza",
pages = "33--39",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms",
notes = "part of \cite{koza:2000:gagp}",
}
@Article{back:1997:survey,
author = "Thomas Back and U. Hammel and H.-P. Schwefel",
title = "Evolutionary computation: comments on the history and
current state",
journal = "IEEE Transactions on Evolutionary Computation",
year = "1997",
volume = "1",
number = "1",
pages = "3--17",
month = apr,
keywords = "genetic algorithms, genetic programming, EA, CS,
evolutionstrategies, EP",
ISSN = "1089-778X",
URL = "http://ls11-www.cs.uni-dortmund.de/people/schwefel/publications/BHS97.ps.gz",
size = "15 pages",
abstract = "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",
notes = "Reference Cited: 220 CODEN: ITEVF5",
}
@InCollection{back:2000:EC1,
author = "Thomas Back and David B. Fogel and Darrell Whitley and
Peter J. Angeline",
title = "Mutation operators",
booktitle = "Evolutionary Computation 1 Basic Algorithms and
Operators",
publisher = "Institute of Physics Publishing",
year = "2000",
editor = "Thomas Baeck and David B. Fogel and Zbigniew
Michalewicz",
chapter = "32",
pages = "237--255",
address = "Bristol",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7503-0664-5",
notes = "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
\cite{montana:stgpEC})",
size = "19 pages",
}
@InProceedings{backer:1996:WSC,
author = "Gerriet Backer",
title = "Learning with missing data using Genetic Programming",
booktitle = "The 1st Online Workshop on Soft Computing (WSC1)",
year = "1996",
address = "http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/",
month = "19--30 " # aug,
organisation = "Research Group on ECOmp of the Society of Fuzzy Theory
and Systems (SOFT)",
publisher = "Nagoya University, Japan",
keywords = "genetic algorithms, genetic programming, Machine
learning, Missing data, Strongly Typed Genetic
Programming STGP",
URL = "http://www.pa.info.mie-u.ac.jp/bioele/wsc1/papers/d041.html",
URL = "http://www.pa.info.mie-u.ac.jp/bioele/wsc1/papers/files/backer.ps.gz",
abstract = "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.",
size = "5 pages",
notes = "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",
}
@InProceedings{conf/evoW/BackmanD08,
title = "A Generative Representation for the Evolution of Jazz
Solos",
author = "Kjell Backman and Palle Dahlstedt",
bibdate = "2008-04-15",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/evoW/evoW2008.html#BackmanD08",
booktitle = "Proceedings of Evo{COMNET}, Evo{FIN}, Evo{HOT},
Evo{IASP}, Evo{MUSART}, Evo{NUM}, Evo{STOC}, and
EvoTransLog, Applications of Evolutionary Computing,
EvoWorkshops",
publisher = "Springer",
year = "2008",
volume = "4974",
editor = "Mario Giacobini and Anthony Brabazon and Stefano
Cagnoni and Gianni {Di Caro} and Rolf Drechsler and
Anik{\'o} Ek{\'a}rt and Anna Esparcia-Alc{\'a}zar 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",
isbn13 = "978-3-540-78760-0",
pages = "371--380",
series = "Lecture Notes in Computer Science",
doi = "doi:10.1007/978-3-540-78761-7_40",
address = "Naples",
month = "26-28 " # mar,
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
}
@InProceedings{1277299,
author = "Mohamed Bahy Bader-El-Den and Riccardo Poli",
title = "A {GP}-based hyper-heuristic framework for evolving
3-{SAT} heuristics",
booktitle = "GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation",
year = "2007",
editor = "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",
volume = "2",
isbn13 = "978-1-59593-697-4",
pages = "1749--1749",
address = "London",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1749.pdf",
doi = "doi:10.1145/1276958.1277299",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
month = "7-11 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming: Poster,
heuristics, hyper heuristic, SAT",
abstract = "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.",
notes = "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",
}
@InProceedings{bader-el-den07:_gener_sat_local_searc_heuris,
author = "Mohamed Bader-El-Den and Riccardo Poli",
title = "Generating {SAT} Local-Search Heuristics using a {GP}
Hyper-Heuristic Framework",
booktitle = "Evolution Artificielle, 8th International Conference",
year = "2007",
editor = "Nicolas Monmarch{\'e} and El-Ghazali Talbi and Pierre
Collet and Marc Schoenauer and Evelyne Lutton",
volume = "4926",
series = "Lecture Notes in Computer Science",
pages = "37--49",
address = "Tours, France",
month = "29-31 " # oct,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-540-79304-5",
doi = "doi:10.1007/978-3-540-79305-2_4",
abstract = "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.",
notes = "EA'07",
}
@InProceedings{Bader-El-Den:2008:evocop,
title = "Inc*: An Incremental Approach for Improving Local
Search Heuristics",
author = "Mohamed Bahy Bader-El-Den and Riccardo Poli",
bibdate = "2008-04-15",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/evoW/evocop2008.html#Bader-El-DenP08",
booktitle = "Proceedings of the 8th European Conference,
Evolutionary Computation in Combinatorial Optimization,
Evo{COP}",
publisher = "Springer",
year = "2008",
volume = "4972",
editor = "Jano I. van Hemert and Carlos Cotta",
isbn13 = "978-3-540-78603-0",
pages = "194--205",
series = "Lecture Notes in Computer Science",
doi = "doi:10.1007/978-3-540-78604-7_17",
address = "Naples, Italy",
month = mar # " 26-28",
keywords = "genetic algorithms, genetic programming",
notes = "also known as \cite{conf/evoW/Bader-El-DenP08}",
}
@InProceedings{Bader-El-Den:2008:WCCI,
author = "Mohamed Bader-El-Den and Riccardo Poli",
title = "Analysis and Extension of the Inc* on the
Satisfiability Testing Problem",
booktitle = "2008 IEEE World Congress on Computational
Intelligence",
year = "2008",
editor = "Jun Wang",
pages = "3342--3349",
address = "Hong Kong",
month = "1-6 " # jun,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming, SAT",
isbn13 = "978-1-4244-1823-7",
file = "EC0725.pdf",
doi = "doi:10.1109/CEC.2008.4631250",
abstract = "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*. Preliminary experiments on the
satisfiability problem (SAT) problem have shown that
Inc* is a competitive approach. In this
paper we enhance Inc* 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.",
notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
EPS and the IET.",
}
@InCollection{Bader-El-Den:2008:GPTP,
author = "Mohamed Bader-El-Den and Riccardo Poli",
title = "Evolving Effective Incremental Solvers for {SAT} with
a Hyper-Heuristic Framework Based on Genetic
Programming",
booktitle = "Genetic Programming Theory and Practice {VI}",
year = "2008",
editor = "Rick L. Riolo and Terence Soule and Bill Worzel",
series = "Genetic and Evolutionary Computation",
chapter = "11",
pages = "163--179",
address = "Ann Arbor",
month = "15-17" # may,
publisher = "Springer",
size = "16 pages",
isbn13 = "978-0-387-87622-1",
notes = "part of \cite{Riolo:2008:GPTP} To be published late
2008. Also known as \cite{El-den:2008:GPTP}",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{Bader-El-Den:2008:gecco,
author = "Mohamed Bader-El-Den and Riccardo Poli",
title = "Evolving Heuristics with Genetic Programming",
booktitle = "GECCO '08: Proceedings of the 10th annual conference
on Genetic and evolutionary computation",
year = "2008",
editor = "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",
isbn13 = "978-1-60558-130-9",
pages = "601--602",
address = "Atlanta, GA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p601.pdf",
doi = "doi:10.1145/1389095.1389212",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "12-16 " # jul,
keywords = "genetic algorithms, genetic programming, heuristics,
hyperheuristics, Inc*, SAT, Evolutionary combinatorial
optimisation: Poster",
notes = "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 \cite{1389212}",
}
@InProceedings{BaderElDen:2009:cec,
author = "Mohamed {Bader El Den} and Riccardo Poli",
title = "Grammar-Based Genetic Programming for Timetabling",
booktitle = "2009 IEEE Congress on Evolutionary Computation",
year = "2009",
editor = "Andy Tyrrell",
pages = "2532--2539",
address = "Trondheim, Norway",
month = "18-21 " # may,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-2959-2",
file = "P677.pdf",
doi = "doi:10.1109/CEC.2009.4983259",
abstract = "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.",
keywords = "genetic algorithms, genetic programming,
hyperheuristics",
notes = "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",
}
@PhdThesis{Bader-El-Den:thesis,
author = "Mohamed {Bader El Den}",
title = "Investigation of the role of Genetic Programming in a
Hyper-Heuristic Framework for Combinatorial
Optimization Problems",
school = "School of Computer Science and Electronic Engineering,
University of Essex",
year = "2009",
address = "UK",
keywords = "genetic algorithms, genetic programming",
notes = "http://www.essex.ac.uk/csee/department/news/newsletter/28_09_09.aspx",
}
@InProceedings{conf/ijcci/Bader-El-DenF09,
author = "Mohamed Bahy Bader-El-Den and Shaheen Fatima",
title = "Evolving Effective Bidding Functions for Auction based
Resource Allocation Framework",
year = "2009",
booktitle = "International Conference on Evolutionary Computation
(ICEC 2009)",
editor = "Agostinho Rosa",
address = "Madeira, Portugal",
month = "5-7 " # oct,
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-989-674-014-6",
bibdate = "2010-03-03",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/ijcci/ijcci2009.html#Salehi-AbariW09",
abstract = "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.",
notes = "http://www.icec.ijcci.org/Abstracts/2009/ICEC_2009_Abstracts.htm",
}
@Article{journals/memetic/Bader-El-DenPF09,
title = "Evolving timetabling heuristics using a grammar-based
genetic programming hyper-heuristic framework",
author = "Mohamed Bahy Bader-El-Den and Riccardo Poli and
Shaheen Fatima",
journal = "Memetic Computing",
year = "2009",
number = "3",
volume = "1",
pages = "205--219",
keywords = "genetic algorithms, genetic programming, timetabling,
Hyper-heuristics, Heuristics",
doi = "doi:10.1007/s12293-009-0022-y",
bibdate = "2009-12-11",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/memetic/memetic1.html#Bader-El-DenPF09",
abstract = "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.",
}
@InProceedings{Bader-El-Den:2010:EuroGP,
author = "Mohamed Bader-El-Den and Shaheen Fatima",
title = "Genetic Programming for Auction Based Scheduling",
booktitle = "Proceedings of the 13th European Conference on Genetic
Programming, EuroGP 2010",
year = "2010",
editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara
Silva and Stephen Dignum and A. Sima Uyar",
volume = "6021",
series = "LNCS",
pages = "256--267",
address = "Istanbul",
month = "7-9 " # apr,
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-12147-0",
doi = "doi:10.1007/978-3-642-12148-7_22",
abstract = "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.",
notes = "Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010
held in conjunction with EvoCOP2010 EvoBIO2010 and
EvoApplications2010",
}
@InProceedings{1277272,
author = "Khaled M. S. Badran and Peter I. Rockett",
title = "The roles of diversity preservation and mutation in
preventing population collapse in multiobjective
genetic programming",
booktitle = "GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation",
year = "2007",
editor = "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",
volume = "2",
isbn13 = "978-1-59593-697-4",
pages = "1551--1558",
address = "London",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1551.pdf",
doi = "doi:10.1145/1276958.1277272",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
month = "7-11 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, bloat,
diversity preservation, multiobjective optimisation,
population collapse",
abstract = "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.",
notes = "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",
}
@InProceedings{conf/eurogp/BadranR08,
title = "Integrating Categorical Variables with Multiobjective
Genetic Programming for Classifier Construction",
author = "Khaled M. S. Badran and Peter Rockett",
bibdate = "2008-04-15",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#BadranR08",
booktitle = "Proceedings of the 11th European Conference on Genetic
Programming, EuroGP 2008",
address = "Naples",
month = "26-28 " # mar,
publisher = "Springer",
year = "2008",
volume = "4971",
editor = "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",
isbn13 = "978-3-540-78670-2",
pages = "301--311",
series = "Lecture Notes in Computer Science",
doi = "doi:10.1007/978-3-540-78671-9_26",
keywords = "genetic algorithms, genetic programming",
notes = "Part of \cite{conf/eurogp/2008} EuroGP'2008 held in
conjunction with EvoCOP2008, EvoBIO2008 and
EvoWorkshops2008",
}
@Article{Badran:2009:GPEM,
author = "Khaled Badran and Peter I. Rockett",
title = "The influence of mutation on population dynamics in
multiobjective genetic programming",
journal = "Genetic Programming and Evolvable Machines",
year = "2010",
volume = "11",
number = "1",
pages = "5--33",
month = mar,
keywords = "genetic algorithms, genetic programming,
Multiobjective genetic programming, Population
collapse, Mutation, Population dynamics, MOGP, bloat",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-009-9084-3",
abstract = "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.",
notes = "Steady-state algorithm depth-fair crossover/depth-fair
mutation",
}
@Article{Badran:2011:GPEM,
author = "Khaled Badran and Peter Rockett",
title = "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",
journal = "Genetic Programming and Evolvable Machines",
year = "2012",
volume = "13",
number = "1",
pages = "33--63",
month = mar,
note = "Special Section on Evolutionary Algorithms for Data
Mining",
keywords = "genetic algorithms, genetic programming, Multi-class
pattern classification, Feature extraction, Feature
selection, Multi-objective genetic programming",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-011-9143-4",
size = "31 pages",
abstract = "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.",
affiliation = "Vision and Information Engineering Research Group,
Department of Electronic and Electrical Engineering,
The University of Sheffield, Mappin Street, Sheffield,
S1 3D UK",
}
@Article{journals/soco/BaeJKKKHM10,
title = "Optimization of silicon solar cell fabrication based
on neural network and genetic programming modeling",
author = "Hyeon Bae and Tae-Ryong Jeon and Sungshin Kim and
Hyun-Soo Kim and DongSeop Kim and Seung Soo Han and
Gary S. May",
journal = "Soft Computing - A Fusion of Foundations,
Methodologies and Applications",
year = "2010",
number = "2",
volume = "14",
pages = "161--169",
keywords = "genetic algorithms, genetic programming, Neural
network, Particle swarm optimization, Silicon solar
cell fabrication",
ISSN = "1432-7643",
doi = "doi:10.1007/s00500-009-0438-9",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/soco/soco14.html#BaeJKKKHM10",
abstract = "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.",
}
@InProceedings{bael:1999:TJSPSSBESE,
author = "Patrick Van Bael and Dirk Devogelaere and M.
Rijckaert",
title = "The Job Shop Problem Solved with Simple, Basic
Evolutionary Search Elements",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "665--669",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{Baele:2009:cec,
author = "Guy Baele and Nicolas Bredeche and Evert Haasdijk and
Steven Maere and Nico Michiels and Yves {Van de Peer}
and Christopher Schwarzer and Ronald Thenius",
title = "Open-Ended On-Board Evolutionary Robotics for Robot
Swarms",
booktitle = "2009 IEEE Congress on Evolutionary Computation",
year = "2009",
editor = "Andy Tyrrell",
pages = "-",
address = "Trondheim, Norway",
month = "18-21 " # may,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-2959-2",
file = "P485.pdf",
abstract = "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.",
keywords = "genetic algorithms, genetic programming",
notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and
the IET. IEEE Catalog Number: CFP09ICE-CDR",
}
@InProceedings{DBLP:conf/iscc/BagheriD06,
author = "Ebrahim Bagheri and Hossein Deldari",
title = "Dejong Function Optimization by Means of a Parallel
Approach to Fuzzified Genetic Algorithm",
booktitle = "Proceedings of the 11th IEEE Symposium on Computers
and Communications (ISCC 2006)",
year = "2006",
editor = "Paolo Bellavista and Chi-Ming Chen and Antonio Corradi
and Mahmoud Daneshmand",
pages = "675--680",
address = "Cagliari, Sardinia, Italy",
month = "26-29 " # jun,
publisher = "IEEE Computer Society",
keywords = "genetic algorithms",
ISBN = "0-7695-2588-1",
bibsource = "DBLP, http://dblp.uni-trier.de",
doi = "doi:10.1109/ISCC.2006.57",
abstract = "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.",
}
@InProceedings{baglioni:2000:eampaa,
author = "Stefania Baglioni and Celia da Costa Pereira and Dario
Sorbello and Andrea G. B. Tettamanzi",
title = "An Evolutionary Approach to Multiperiod Asset
Allocation",
booktitle = "Genetic Programming, Proceedings of EuroGP'2000",
year = "2000",
editor = "Riccardo Poli and Wolfgang Banzhaf and William B.
Langdon and Julian F. Miller and Peter Nordin and
Terence C. Fogarty",
volume = "1802",
series = "LNCS",
pages = "225--236",
address = "Edinburgh",
publisher_address = "Berlin",
month = "15-16 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-67339-3",
URL = "http://mago.crema.unimi.it/pub/BaglioniDaCostaPereiraSorbelloTettamanzi2000.ps",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=225",
abstract = "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.",
notes = "EuroGP'2000, part of \cite{poli:2000:GP}",
}
@InProceedings{bagnall:1999:UAABSMUME,
author = "A. J. Bagnall and G. D. Smith",
title = "Using an Adaptive Agent to Bid in a Simplified Model
of the {UK} Market in Electricity",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "774",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems, poster
papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/Bagnall1999b.ps.gz",
URL = "http://www.cs.bris.ac.uk/~kovacs/lcs.archive/Bagnall1999b.ps.gz",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{Bagula:2005:ciS,
author = "Antoine B. Bagula and Hong F. Wang",
title = "On the Relevance of Using Gene Expression Programming
in Destination-Based Traffic Engineering",
booktitle = "Computational Intelligence and Security",
year = "2005",
volume = "3801",
series = "Lecture Notes in Computer Science",
pages = "224--229",
keywords = "genetic algorithms, genetic programming, Gene
Expression Programming",
isbn_13 = "978-3-540-30818-8",
doi = "doi:10.1007/11596448",
abstract = "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.",
}
@InProceedings{bagula_2006_NOMS,
author = "Antoine B. Bagula",
title = "Traffic Engineering Next Generation {IP} Networks
Using Gene Expression Programming",
booktitle = "10th IEEE/IFIP Network Operations and Management
Symposium, NOMS 2006",
year = "2006",
pages = "230--239",
address = "Vancouver",
organisation = "IFIP",
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming, Gene
Expression Programming",
doi = "doi:10.1109/NOMS.2006.1687554",
size = "10 pages",
abstract = "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.",
}
@PhdThesis{urn_nbn_se_kth_diva-4213-2__fulltext,
author = "Antoine B. Bagula",
title = "Hybrid Routing in Next Generation {IP} Networks: Qo{S}
Routing Mechanisms and Network Control Strategies",
school = "Royal Institute of Technology (KTH)",
year = "2006",
type = "Doctoral Thesis",
address = "Stockholm, Sweden",
month = dec,
keywords = "genetic algorithms, genetic programming, Gene
Expression Programming",
URL = "http://www.diva-portal.org/diva/getDocument?urn_nbn_se_kth_diva-4213-2__fulltext.pdf",
size = "78 pages",
}
@Article{Bahiraie:2009:AJAS,
author = "Alireza Bahiraie and Noor {Akma bt Ibrahim} and A. K.
M. Azhar",
title = "On the Predictability of Risk Box Approach by Genetic
Programming Method for Bankruptcy Prediction",
journal = "American Journal of Applied Sciences",
year = "2009",
volume = "6",
number = "9",
pages = "1748--1757",
keywords = "genetic algorithms, genetic programming, ratios
analysis, risk box, bankruptcy prediction",
ISSN = "1546-9239",
URL = "http://www.scipub.org/fulltext/ajas/ajas691748-1757.pdf",
oai = "oai:doaj-articles:dbcbc387f7a40da02a20dffdfbef123f",
size = "10 pages",
abstract = "{\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.",
ISSN = "15469239",
bibsource = "OAI-PMH server at www.doaj.org",
}
@InProceedings{Bai:2010:ISDA,
author = "Haiying Bai and Noriko Yata and Tomoharu Nagao",
title = "Efficient evolutionary image processing using genetic
programming: Reducing computation time for generating
feature images of the Automatically Construction of
Tree-Structural Image Transformation ({ACTIT})",
booktitle = "10th International Conference on Intelligent Systems
Design and Applications (ISDA 2010)",
year = "2010",
month = nov # " 29-" # dec # " 1",
pages = "302--307",
abstract = "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.",
keywords = "genetic algorithms, genetic programming, ACTIT,
automatically construction of tree-structural image
transformation, evolutionary image processing, image
feature filters, transformation images, image
processing",
doi = "doi:10.1109/ISDA.2010.5687249",
notes = "Grad. Sch. of Environ. & Inf. Sci., Yokohama Nat.
Univ., Yokohama, Japan. Also known as \cite{5687249}",
}
@InProceedings{Bai:2008:ieeeSMI,
author = "Linge Bai and Manolya Eyiyurekli and David E. Breen",
title = "Self-organizing primitives for automated shape
composition",
booktitle = "IEEE International Conference on Shape Modeling and
Applications, SMI 2008",
year = "2008",
month = jun,
pages = "147--154",
keywords = "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",
doi = "doi:10.1109/SMI.2008.4547962",
abstract = "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.",
notes = "Also known as \cite{4547962}",
}
@InProceedings{Bai:2008:gecco,
author = "Linge Bai and Manolya Eyiyurekli and David E. Breen",
title = "Automated shape composition based on cell biology and
distributed genetic programming",
booktitle = "GECCO '08: Proceedings of the 10th annual conference
on Genetic and evolutionary computation",
year = "2008",
editor = "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",
isbn13 = "978-1-60558-130-9",
pages = "1179--1186",
address = "Atlanta, GA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1179.pdf",
doi = "doi:10.1145/1389095.1389329",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "12-16 " # jul,
keywords = "genetic algorithms, genetic programming, chemotaxis,
distributed genetic programming, morphogenesis,
self-organisation, shape composition",
notes = "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 \cite{1389329}",
}
@InProceedings{Bai:2008:SASO,
author = "Linge Bai and Manolya Eyiyurekli and David E. Breen",
title = "An Emergent System for Self-Aligning and
Self-Organizing Shape Primitives",
booktitle = "Second IEEE International Conference on Self-Adaptive
and Self-Organizing Systems, SASO '08",
year = "2008",
month = oct,
pages = "445--454",
keywords = "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",
doi = "doi:10.1109/SASO.2008.54",
abstract = "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.",
notes = "Also known as \cite{4663447}",
}
@InProceedings{bain:2004:eafcs,
title = "Evolving Algorithms for Constraint Satisfaction",
author = "Stuart Bain and John Thornton and Abdul Sattar",
pages = "265--272",
booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary
Computation",
year = "2004",
publisher = "IEEE Press",
month = "20-23 " # jun,
address = "Portland, Oregon",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming, Combinatorial
\& numerical optimization",
URL = "http://stuart.multics.org/publications/CEC2004.pdf",
size = "8 pages",
abstract = "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.",
notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and
the IEE.",
}
@Article{bains:2002:CODDD,
author = "William Bains and Richard Gilbert and Lilya Sviridenko
and Jose-Miguel Gascon and Robert Scoffin and Kris
Birchall and Inman Harvey and John Caldwell",
title = "Evolutionary computational methods to predict oral
bioavailability {QSPR}s",
journal = "Current Opinion in Drug Discovery and Development",
year = "2002",
volume = "5",
number = "1",
pages = "44--51",
month = jan,
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "Amedis Pharmaceuticals. Review, Tutorial
",
}
@Article{bains:2004:PBMB,
author = "William Bains and Antranig Basman and Cat White",
title = "{HERG} binding specificity and binding site structure:
Evidence from a fragment-based evolutionary computing
{SAR} study",
journal = "Progress in Biophysics and Molecular Biology",
year = "2004",
volume = "86",
pages = "205--233",
number = "2",
month = oct,
keywords = "genetic algorithms, genetic programming, HERG, IKr,
QSAR, Molecular descriptors, Prediction",
doi = "doi:10.1016/j.pbiomolbio.2003.09.001",
abstract = "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.",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6TBN-4BS4DJM-1/2/2bd8833742e401378469ee988d571705",
size = "29 pages",
notes = "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].",
}
@Article{Baird:2006:RNA,
author = "Stephen D. Baird and Marcel Turcotte and Robert G.
Korneluk and Martin Holcik",
title = "Searching for {IRES}",
journal = "RNA",
year = "2006",
volume = "12",
number = "10",
pages = "1755--1785",
month = oct,
publisher = "RNA Society",
keywords = "genetic algorithms, genetic programming, IRES, RNA,
secondary structure, prediction software",
doi = "doi:10.1261/rna.157806",
abstract = "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.",
notes = "Paragraph on \cite{Yuh-JyhHu:2003:NAR} PMCID:
PMC1581980",
}
@InProceedings{Bajurnow:aspgp03,
author = "Andrei Bajurnow and Vic Ciesielski",
title = "Function and terminal Set Selection for Evolving Goal
Scoring Behaviour in Soccer Players",
booktitle = "Proceedings of The First Asian-Pacific Workshop on
Genetic Programming",
year = "2003",
editor = "Sung-Bae Cho and Nguyen Xuan Hoai and Yin Shan",
pages = "38--44",
address = "Rydges (lakeside) Hotel, Canberra, Australia",
month = "8 " # dec,
keywords = "genetic algorithms, genetic programming",
ISBN = "0-9751724-0-9",
notes = "\cite{aspgp03}",
}
@InProceedings{bajurnow:2004:llfegsbisp,
title = "Layered Learning for Evolving Goal Scoring Behavior in
Soccer Players",
author = "Andrei Bajurnow and Vic Ciesielski",
pages = "1828--1835",
booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary
Computation",
year = "2004",
publisher = "IEEE Press",
month = "20-23 " # jun,
address = "Portland, Oregon",
ISBN = "0-7803-8515-2",
URL = "http://goanna.cs.rmit.edu.au/~vc/papers/cec2004-bajurnow.pdf",
size = "8 pages",
keywords = "genetic algorithms, genetic programming, Evolutionary
intelligent agents, Evolutionary Computation and
Games",
abstract = "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.",
notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and
the IEE.",
}
@InProceedings{Baker:2010:ieeeICWITS,
author = "James Baker and Nuri Celik and Nobutaka Omaki and Jill
Kobashigawa and Hyoung-Sun Youn and Magdy F. Iskander",
title = "On the design of integrated {HF} radar systems for
Homeland Security applications",
booktitle = "2010 IEEE International Conference on Wireless
Information Technology and Systems (ICWITS)",
year = "2010",
month = "28 " # oct # "-" # sep # " 3",
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/ICWITS.2010.5611859",
notes = "Also known as \cite{5611859}",
}
@PhdThesis{bakkoury:thesis,
author = "Zohra Bakkoury",
title = "Feasibility Assessement and Optimal Scheduling of
Water Supply Projects",
school = "School of Engineering and Computer Science, Exeter
University",
year = "2002",
keywords = "genetic algorithms",
}
@InProceedings{balakrishnan:1996:ser,
author = "Karthik Balakrishnan and Vasant Honavar",
title = "On Sensor Evolution in Robotics",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "Genetic Algorithms",
pages = "455--460",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96 GA paper",
}
@InProceedings{Balakrishnan:1997:slrl,
author = "Karthik Balakrishnan and Vasant Honavar",
title = "Spatial Learning for Robot Localization",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "Artifical life and evolutionary robotics",
pages = "389--397",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
notes = "GP-97",
}
@Article{Balasubramaniam:2009:GPEM,
author = "P. Balasubramaniam and A. Vincent Antony Kumar",
title = "Solution of matrix {Riccati} differential equation for
nonlinear singular system using genetic programming",
journal = "Genetic Programming and Evolvable Machines",
year = "2009",
volume = "10",
number = "1",
pages = "71--89",
month = mar,
keywords = "genetic algorithms, genetic programming, Matrix
Riccati differential equation, Nonlinear, Optimal
control, Runge Kutta method, Singular system",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-008-9072-z",
size = "19 pages",
abstract = "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.",
}
@InProceedings{Balazs:2010:ieee-fuzz,
author = "Krisztian Balazs and Janos Botzheim and Laszlo T.
Koczy",
title = "Hierarchical fuzzy system modeling by Genetic and
Bacterial Programming approaches",
booktitle = "IEEE International Conference on Fuzzy Systems
(FUZZ-IEEE 2010)",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
pages = "1866--1871",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-6920-8",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/botzheim/Balazs_2010_ieee-fuzz.pdf",
doi = "doi:10.1109/FUZZY.2010.5584220",
size = "6 pages",
abstract = "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.",
notes = "WCCI 2010. Also known as \cite{5584220}",
}
@InProceedings{Balazs:2010:WAC,
author = "Krisztian Balazs and Janos Botzheim and Laszlo T.
Koczy",
title = "Hierarchical fuzzy system construction applying
genetic and bacterial programming algorithms with
expression tree building restrictions",
booktitle = "World Automation Congress (WAC 2010)",
year = "2010",
month = "19-23 " # sep,
address = "Kobe, Japan",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/botzheim/Balazs_2010_WAC.pdf",
URL = "http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5665326",
abstract = "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.",
keywords = "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)",
ISSN = "2154-4824",
notes = "Also known as \cite{5665326}",
}
@InProceedings{balazs:1999:AE,
author = "Marton E. Balazs and Daniel L. Richter",
title = "A genetic algorithm with dynamic population:
Experimental results",
booktitle = "Late Breaking Papers at the 1999 Genetic and
Evolutionary Computation Conference",
year = "1999",
editor = "Scott Brave and Annie S. Wu",
pages = "25--30",
address = "Orlando, Florida, USA",
month = "13 " # jul,
keywords = "Genetic Algorithms",
notes = "GECCO-99LB",
}
@InProceedings{conf/egice/BaldockS06,
title = "Structural Topology Optimization of Braced Steel
Frameworks Using Genetic Programming",
author = "Robert Baldock and Kristina Shea",
booktitle = "Intelligent Computing in Engineering and Architecture,
13th {EG}-{ICE} Workshop",
publisher = "Springer",
year = "2006",
volume = "4200",
editor = "Ian F. C. Smith",
ISBN = "3-540-46246-5",
pages = "54--61",
series = "Lecture Notes in Computer Science",
address = "Ascona, Switzerland",
month = jun # " 25-30",
note = "Revised Selected Papers",
bibdate = "2006-12-06",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/egice/egice2006.html#BaldockS06",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1007/11888598",
abstract = "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.",
notes = "(1) Engineering Design Centre, University of
Cambridge, Cambridge, CB2 1PZ, UK (2) Product
Development, Technical University of Munich,
Boltzmannstrasse 15, D-85748 Garching, Germany",
}
@InProceedings{Baldwin:1999:SIF,
author = "James F. Baldwin and Trevor P. Martin and James G.
Shanahan",
title = "System Identification of Fuzzy Cartesian Granules
Feature Models Using Genetic Programming",
booktitle = "Fuzzy Logic in Artificial Intelligence, IJCAI'97
Workshop, Selected and Invited Papers",
year = "1997",
editor = "Anca L. Ralescu and James G. Shanahan",
volume = "1566",
series = "Lecture Notes in Artificial Intelligence",
pages = "91--116",
address = "Nagoya, Japan",
month = aug # " 23-24",
publisher = "Springer",
note = "Published 1999",
keywords = "genetic algorithms, genetic programming, artificial
intelligence, fuzzy logic, IJCAI",
CODEN = "LNCSD9",
ISBN = "3-540-66374-6",
ISSN = "0302-9743",
bibdate = "Tue Sep 14 06:09:05 MDT 1999",
acknowledgement = ack-nhfb,
URL = "http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-164-22-1637718-0,00.html",
notes = "DBLP, http://dblp.uni-trier.de
DBLP:conf/ijcai/1997fl",
}
@Article{Baldwin:1999:IJAR,
author = "James F. Baldwin and Trevor P. Martin and James G.
Shanahan",
title = "Controlling with words using automatically identified
fuzzy Cartesian granule feature models",
journal = "International Journal of Approximate Reasoning",
volume = "22",
pages = "109--148",
year = "1999",
number = "1-2",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.sciencedirect.com/science/article/B6V07-3XWJVTP-K/1/fca9fc7ee54707e1f2ed9847e29c1b7e",
abstract = "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.",
}
@Book{balic:book,
author = "Joze Balic",
title = "Flexible Manufacturing Systems; Development -
Structure - Operation - Handling - Tooling",
publisher = "DAAAM International",
year = "1999",
series = "Manufacturing technology",
series_editor = "B. Katalinic",
address = "Vienna",
email = "joze.balic@uni-mb.si",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-901509-03-8",
URL = "http://www.daaam.com/daaam/IMS%20Seite/DAAAMInternationalBooks.html",
URL = "http://www.amazon.com/Contribution-integrated-manufacturing-Publishing-Manufacturing/dp/3901509038/ref=sr_1_1?ie=UTF8&s=books&qid=1254069037&sr=1-1",
notes = "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",
size = "223 pages",
}
@Misc{oai:CiteSeerPSU:316448,
title = "Modeling Of Mechanical Parts Compositions Using
Genetic Programming",
author = "Joze Balic and Miran Brezocnik and Franci Cus",
abstract = "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.",
citeseer-isreferencedby = "oai:CiteSeerPSU:116960;
oai:CiteSeerPSU:38303; oai:CiteSeerPSU:462740",
citeseer-references = "oai:CiteSeerPSU:212034",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:316448",
rights = "unrestricted",
URL = "http://citeseer.ist.psu.edu/316448.html",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/13061/http:zSzzSzwww.faim2000.isr.umd.eduzSzfaimzSzexportzSz27e8am-b.pdf/modeling-of-mechanical-parts.pdf",
keywords = "genetic algorithms, genetic programming",
size = "9 pages",
year = "2000",
notes = "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",
}
@Article{Balic:2002:EAAI,
author = "J. Balic and M. Nastran",
title = "An on-line predictive system for steel wire
straightening using genetic programming",
journal = "Engineering Applications of Artificial Intelligence",
year = "2002",
volume = "15",
pages = "559--565",
number = "6",
abstract = "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.",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6V2M-48BKR53-2/2/4a53f22927ad32b0580540322d7c8868",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1016/S0952-1976(03)00021-6",
}
@Article{Balic20021171,
author = "Joze Balic and Marjan Korosec",
title = "Intelligent tool path generation for milling of free
surfaces using neural networks",
journal = "International Journal of Machine Tools and
Manufacture",
volume = "42",
number = "10",
pages = "1171--1179",
year = "2002",
email = "joze.balic@uni-mb.si",
keywords = "Neural network, CAD/CAM system, CAPP, ICAM, Milling
strategy",
ISSN = "0890-6955",
doi = "doi:10.1016/S0890-6955(02)00045-7",
URL = "http://www.sciencedirect.com/science/article/B6V4B-45YG41B-6/2/09eff48a04f9b22be6b2ed2dd0e6d3b1",
abstract = "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]",
notes = "Not on GP",
}
@Article{Balic:2006:JIM,
author = "Joze Balic and Miha Kovacic and Bostjan Vaupotic",
title = "Intelligent Programming of {CNC} Turning Operations
using Genetic Algorithm",
journal = "Journal of intelligent manufacturing",
year = "2006",
volume = "17",
number = "3",
pages = "331--340",
month = jun,
keywords = "genetic algorithms, genetic programming, CNC
programming, GA, Intelligent CAM, Turning, Tool path
generation",
ISSN = "0956-5515",
doi = "doi:10.1007/s10845-005-0001-1",
abstract = "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.",
}
@InProceedings{conf/icsoft/Balicki07,
author = "Jerzy Marian Balicki",
title = "Multi-Criterion Genetic Programming With Negative
Selection for Finding Pareto Solutions",
booktitle = "Proceedings of the Second International Conference on
Software and Data Technologies, ICSOFT 2007",
year = "2007",
editor = "Joaquim Filipe and Boris Shishkov and Markus Helfert",
pages = "120--127",
address = "Barcelona, Spain",
month = "22-25 " # jul,
publisher = "INSTICC Press",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-989-8111-05-0",
abstract = "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.",
notes = "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 ?",
bibdate = "2009-02-24",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/icsoft/icsoft2007-1.html#Balicki07",
}
@Article{baluja:1994:taaecgi,
author = "Shumeet Baluja and Dean Pomerleau and Todd Jochem",
title = "Towards Automated Artificial Evolution for
Computer-generated Images",
journal = "Connection Science",
year = "1994",
volume = "6",
number = "2 and 3",
pages = "325--354",
keywords = "genetic algorithms, genetic programming, artificial
neural networks (ANN), simulated evolution, computer
graphics",
URL = "http://www.ri.cmu.edu/pubs/pub_1718.html",
URL = "http://www.ri.cmu.edu/pub_files/pub3/baluja_shumeet_1994_1/baluja_shumeet_1994_1.pdf",
size = "30 pages",
abstract = "
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.",
notes = "also CMU techical report CMU//CS-93-198
",
}
@Article{Bankhead:2007:GPEM,
author = "Armand {Bankhead III} and Robert B. Heckendorn",
title = "Using evolvable genetic cellular automata to model
breast cancer",
journal = "Genetic Programming and Evolvable Machines",
year = "2007",
volume = "8",
number = "4",
pages = "381--393",
month = dec,
note = "special issue on medical applications of Genetic and
Evolutionary Computation",
keywords = "genetic algorithms, Genetic cellular automata, DCIS,
Progenitor hierarchy, Ductal simulation, Hereditary
genetic predisposition, Hereditary breast cancer, CA",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-007-9042-x",
abstract = "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.",
notes = "155 node beowulf cluster",
}
@InProceedings{banks:2004:lbp,
author = "Edwin Roger Banks and James Hayes and Edwin Nunez",
title = "Parametric Regression Through Genetic Programming",
booktitle = "Late Breaking Papers at the 2004 Genetic and
Evolutionary Computation Conference",
year = "2004",
editor = "Maarten Keijzer",
address = "Seattle, Washington, USA",
month = "26 " # jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/LBP001.pdf",
abstract = "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.",
notes = "Part of \cite{keijzer:2004:GECCO:lbp}",
}
@InProceedings{banks:2004:msa:erban,
author = "E. R. Banks and J. C. Hayes and E. Nunez",
title = "Parametric Regression Through Genetic Programming",
editor = "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",
booktitle = "GECCO 2004 Workshop Proceedings",
year = "2004",
month = "26-30 " # jun,
address = "Seattle, Washington, USA",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/WMSA003.pdf",
notes = "GECCO-2004WKS Distributed on CD-ROM at GECCO-2004",
}
@InProceedings{Banks:gecco05lbp,
author = "Edwin Roger Banks and Edwin Nunez and Paul Agarwal and
Claudette Owens and Marshall McBride and Ron Liedel",
title = "Genetic Programming for Discrimination of Buried
Unexploded Ordnance ({UXO})",
booktitle = "Late breaking paper at Genetic and Evolutionary
Computation Conference {(GECCO'2005)}",
year = "2005",
month = "25-29 " # jun,
editor = "Franz Rothlauf",
address = "Washington, D.C., USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/66-banks.pdf",
keywords = "genetic algorithms, genetic programming",
abstract = "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",
notes = "Distributed on CD-ROM at GECCO-2005",
}
@InProceedings{Banks:gecco06lbp,
author = "Edwin Roger Banks and Edwin Nunez and Paul Agarwal and
Marshall McBride and Ronald Liedel and Claudette
Owens",
title = "A Comparison of Evolutionary Computing Techniques Used
to Model Bi-Directional Reflectance Distribution
Functions",
booktitle = "Late breaking paper at Genetic and Evolutionary
Computation Conference {(GECCO'2006)}",
year = "2006",
month = "8-12 " # jul,
editor = "J{\"{o}}rn Grahl",
address = "Seattle, WA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006etc/papers/lbp128.pdf",
notes = "Distributed on CD-ROM at GECCO-2006",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
}
@InProceedings{DBLP:conf/gecco/BanksAMO09,
author = "Edwin Roger Banks and Paul Agarwal and Marshall
McBride and Claudette Owens",
title = "A comparison of selection, recombination, and mutation
parameter importance over a set of fifteen optimization
tasks",
booktitle = "GECCO-2009 Late-Breaking Papers",
year = "2009",
editor = "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",
pages = "1971--1976",
address = "Montreal",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "8-12 " # jul,
organisation = "SigEvo",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-60558-325-9",
bibsource = "DBLP, http://dblp.uni-trier.de",
doi = "doi:10.1145/1570256.1570261",
abstract = "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.",
notes = "Distributed on CD-ROM at GECCO-2009.
ACM Order Number 910092.",
}
@InProceedings{DBLP:conf/gecco/BanksAMO09a,
author = "Edwin Roger Banks and Paul Agarwal and Marshall
McBride and Claudette Owens",
title = "Lessons learned in application of evolutionary
computation to a set of optimization tasks",
booktitle = "GECCO-2009 Late-Breaking Papers",
year = "2009",
editor = "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",
pages = "1977--1982",
address = "Montreal",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "8-12 " # jul,
organisation = "SigEvo",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-60558-325-9",
bibsource = "DBLP, http://dblp.uni-trier.de",
doi = "doi:10.1145/1570256.1570262",
abstract = "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.",
notes = "Distributed on CD-ROM at GECCO-2009.
ACM Order Number 910092.",
}
@InProceedings{Banks:2009:HPCMP-UGC,
author = "Edwin Roger Banks and Paul Agarwal and Marshall
McBride and Claudette Owens",
title = "Evolving Image Noise Filters through Genetic
Programming",
booktitle = "DoD High Performance Computing Modernization Program
Users Group Conference (HPCMP-UGC), 2009",
year = "2009",
month = "15-18 " # jun,
pages = "307--312",
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/HPCMP-UGC.2009.50",
notes = "COLSA Corp., Huntsville, AL, USA Also known as
\cite{5729481}",
}
@InProceedings{DBLP:conf/gecco/BanksAMO09b,
author = "Edwin Roger Banks and Paul Agarwal and Marshall
McBride and Claudette Owens",
title = "Toward a universal operator encoding for genetic
programming",
booktitle = "GECCO-2009 Late-Breaking Papers",
year = "2009",
editor = "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",
pages = "1983--1986",
address = "Montreal",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "8-12 " # jul,
organisation = "SigEvo",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-60558-325-9",
bibsource = "DBLP, http://dblp.uni-trier.de",
doi = "doi:10.1145/1570256.1570263",
size = "4 pages",
abstract = "The 2002 CEC paper entitled {"}Genetic Programming
with Smooth Operators for Arithmetic Expressions:
Diviplication and Subdition{"} by Ursem and Krink
\cite{ursem: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.",
notes = "cites \cite{page: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.",
}
@TechReport{banzhaf:mrl:tech,
author = "Wolfgang Banzhaf",
title = "Genetic Programming for Pedestrians",
institution = "Mitsubishi Electric Research Labs",
year = "1993",
type = "MERL Technical Report",
number = "93-03",
address = "Cambridge, MA, USA",
keywords = "genetic algorithms, genetic programming",
URL = "ftp://lumpi.informatik.uni-dortmund.de/pub/biocomp/papers/pedes93.ps.gz",
abstract = "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.",
notes = "As \cite{banzhaf:mrl}",
}
@InProceedings{banzhaf:mrl,
author = "Wolfgang Banzhaf",
title = "Genetic Programming for Pedestrians",
institution = "Mitsubishi Electrical Research Laboratories, Cambridge
Research Center",
year = "1993",
booktitle = "Proceedings of the 5th International Conference on
Genetic Algorithms, ICGA-93",
editor = "Stephanie Forrest",
publisher = "Morgan Kaufmann",
pages = "628",
address = "University of Illinois at Urbana-Champaign",
month = "17-21 " # jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/GenProg_forPed.ps.Z",
abstract = "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.",
notes = "Also available as MRL Technical Report 93-03 11 pages.
(\cite{banzhaf: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.
",
}
@InProceedings{banzhaf:1994:ppsn3,
author = "Wolfgang Banzhaf",
title = "Genotype-Phenotype-Mapping and Neutral Variation --
{A} case study in Genetic Programming",
booktitle = "Parallel Problem Solving from Nature III",
year = "1994",
editor = "Yuval Davidor and Hans-Paul Schwefel and Reinhard
M{\"a}nner",
series = "LNCS",
volume = "866",
pages = "322--332",
address = "Jerusalem",
publisher_address = "Berlin, Germany",
month = "9-14 " # oct,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-58484-6",
URL = "ftp://lumpi.informatik.uni-dortmund.de/pub/biocomp/papers/ppsn94.ps.gz",
URL = "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-58484-6",
doi = "doi:10.1007/3-540-58484-6_276",
size = "10 pages",
abstract = "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.",
notes = "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.
",
}
@InProceedings{banzhaf:1997:gabrrfr,
author = "Wolfgang Banzhaf and Peter Nordin and Markus Olmer",
title = "Generating Adaptive Behavior for a Real Robot using
Function Regression within Genetic Programming",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "35--43",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
URL = "http://www.cs.mun.ca/~banzhaf/papers/robot_over.pdf",
size = "11 pages",
abstract = "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",
notes = "GP-97",
}
@InCollection{Banzhaf:1997:HEC,
author = "Wolfgang Banzhaf",
title = "Interactive Evolution",
booktitle = "Handbook of Evolutionary Computation",
publisher = "Oxford University Press",
publisher_2 = "Institute of Physics Publishing",
year = "1997",
editor = "Thomas Baeck and David B. Fogel and Zbigniew
Michalewicz",
chapter = "section C2.9",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7503-0392-1",
doi = "doi:10.1201/9781420050387.ptc",
}
@Book{banzhaf:1997:book,
author = "Wolfgang Banzhaf and Peter Nordin and Robert E. Keller
and Frank D. Francone",
title = "Genetic Programming -- An Introduction; On the
Automatic Evolution of Computer Programs and its
Applications",
publisher = "Morgan Kaufmann",
publisher2 = "dpunkt.verlag",
year = "1998",
address = "San Francisco, CA, USA",
address2 = "Heidelberg",
month = jan,
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-510-X",
ISBN = "3-920993-58-6",
URL = "http://www.elsevier.com/wps/find/bookdescription.cws_home/677869/description#description",
notes = "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.
",
size = "480 pages",
}
@Proceedings{banzhaf:1998:GP,
title = "Genetic Programming",
year = "1998",
editor = "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer
and Terence C. Fogarty",
volume = "1391",
series = "LNCS",
address = "Paris",
publisher_address = "Berlin",
month = "14-15 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-64360-5",
doi = "doi:10.1007/BFb0055924",
size = "232 pages",
notes = "EuroGP'98",
}
@Article{lemonde:1998:23apr,
key = "lemonde",
title = "Les Robots inventeent la vie",
journal = "Le Monde",
year = "1998",
month = "23 Avril",
keywords = "genetic algorithms, genetic programming",
notes = "in french, Description of EvoRobot'98 in particular:
Stefanio Nolfi and Dario Floreano, Jean Arcady-Meyer,
Henrik Lund, \cite{dittrich:1998:lmrrm}, Nick Jakobi",
}
@Proceedings{banzhaf:1999:gecco99,
title = "{GECCO}-99: Proceedings of the Genetic and
Evolutionary Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-611-4",
URL = "http://www.amazon.com/exec/obidos/ASIN/1558606114/qid%3D977054373/105-7666192-3217523",
size = "2 volumes",
notes = "GECCO-99",
}
@Misc{oai:CiteSeerPSU:400591,
title = "Artificial Intelligence: Genetic Programming",
author = "Wolfgang Banzhaf",
year = "2000",
month = jul # "~04",
note = "Contract no: 20851A2/2/102",
keywords = "genetic algorithms, genetic programming",
citeseer-isreferencedby = "oai:CiteSeerPSU:54843;
oai:CiteSeerPSU:537988; oai:CiteSeerPSU:536890;
oai:CiteSeerPSU:275725",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:400591",
rights = "unrestricted",
URL = "http://web.cs.mun.ca/~banzhaf/papers/ency.pdf",
URL = "http://citeseer.ist.psu.edu/400591.html",
size = "13 pages",
abstract = "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.",
notes = "Survey/introduction to GP. See also
\cite{Banzhaf2001789}",
}
@TechReport{oai:CiteSeerPSU:324880,
author = "Wolfgang Banzhaf and Dirk Banscherus and Peter
Dittrich",
title = "Hierarchical Genetic Programming Using Local Modules",
institution = "University of Dortmund",
address = "Dortmund, Germany",
year = "1998",
type = "Technical Report",
number = "50/98",
keywords = "genetic algorithms, genetic programming",
URL = "http://hdl.handle.net/2003/5365",
URL = "https://eldorado.uni-dortmund.de/dspace/bitstream/2003/5365/1/ci56.pdf",
citeseer-isreferencedby = "oai:CiteSeerPSU:39828",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:324880",
rights = "unrestricted",
URL = "http://citeseer.ist.psu.edu/324880.html",
abstract = "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.",
notes = "see also \cite{banzhaf:2000:IJ}",
size = "pages",
}
@Article{banzhaf:2000:IJ,
author = "Wolfgang Banzhaf and Dirk Banscherus and Peter
Dittrich",
title = "Hierarchical Genetic Programming using Local Modules",
journal = "InterJournal Complex Systems",
year = "2000",
volume = "228",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.interjournal.org/manuscript_abstract.php?44691",
URL = "http://web.cs.mun.ca/~banzhaf/papers/iccs98.html",
URL = "https://eldorado.uni-dortmund.de/bitstream/2003/5365/1/ci56.pdf",
size = "18 pages",
abstract = "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.",
notes = "Category: Brief Article Status: Accepted Manuscript
Number: [228] Submission Date: 981210 Revised On: 815
Subject(s): CX, CX.66
See also \cite{oai:CiteSeerPSU:324880}",
}
@Article{banzhaf:2000:genpletter,
author = "W. Banzhaf and W. B. Langdon",
title = "Some considerations on the reason for bloat",
journal = "Genetic Programming and Evolvable Machines",
year = "2002",
volume = "3",
number = "1",
pages = "81--91",
month = mar,
email = "banzhaf@tarantoga.cs.uni-dortmund.de",
keywords = "genetic algorithms, genetic programming, linear
genomes, effective fitness, neutral variations",
ISSN = "1389-2576",
URL = "http://web.cs.mun.ca/~banzhaf/papers/genp_bloat.pdf",
doi = "doi:10.1023/A:1014548204452",
abstract = "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.",
notes = "Article ID: 395990",
}
@Article{banzhaf:2000:ei,
author = "Wolfgang Banzhaf",
title = "Editorial Introduction",
journal = "Genetic Programming and Evolvable Machines",
year = "2000",
volume = "1",
number = "1/2",
pages = "5--6",
month = apr,
ISSN = "1389-2576",
doi = "doi:10.1023/A:1010026829303",
notes = "Article ID: 253701",
}
@Article{banzhaf:2000:IS,
author = "Wolfgang Banzhaf",
title = "The artificial evolution of computer code",
journal = "IEEE Intelligent Systems",
year = "2000",
volume = "15",
number = "3",
pages = "74--76",
month = may # "-" # jun,
keywords = "genetic algorithms, genetic programming",
ISSN = "1094-7167",
URL = "http://ieeexplore.ieee.org/iel5/5254/18363/00846288.pdf",
broken = "http://web.cs.mun.ca/~banzhaf/papers/ieee_intelligentsystems.pdf",
URL = "http://citeseer.ist.psu.edu/399369.html",
size = "3 pages",
notes = "part of \cite{hirsh:2000:GP}",
}
@Article{banzhaf:2000:ack,
author = "W. Banzhaf",
title = "Acknowledgement",
journal = "Genetic Programming and Evolvable Machines",
year = "2000",
volume = "1",
number = "4",
pages = "307",
month = oct,
ISSN = "1389-2576",
doi = "doi:10.1023/A:1010022522223",
notes = "Article ID: 273809",
}
@InCollection{Banzhaf2001789,
author = "W. Banzhaf",
title = "Artificial Intelligence: Genetic Programming",
editor = "Neil J. Smelser and Paul B. Baltes",
booktitle = "International Encyclopedia of the Social \& Behavioral
Sciences",
publisher = "Pergamon",
address = "Oxford",
year = "2001",
pages = "789--792",
isbn13 = "978-0-08-043076-8",
doi = "doi:10.1016/B0-08-043076-7/00557-X",
URL = "http://www.sciencedirect.com/science/article/B7MRM-4MT09VJ-403/2/fa4e06852750b95eb2734f9ca37ae6ad",
abstract = "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.",
}
@Article{banzhaf:2001:intro,
author = "W. Banzhaf",
title = "Editorial Introduction",
journal = "Genetic Programming and Evolvable Machines",
year = "2001",
volume = "2",
number = "1",
pages = "5",
month = mar,
ISSN = "1389-2576",
doi = "doi:10.1023/A:1010076931170",
size = "1 page",
notes = "Article ID: 319810",
}
@Article{banzhaf:2001:ack,
author = "W. Banzhaf",
title = "Acknowledgement",
journal = "Genetic Programming and Evolvable Machines",
year = "2001",
volume = "2",
number = "4",
pages = "315--315",
month = dec,
ISSN = "1389-2576",
doi = "doi:10.1023/A:1017497620393",
notes = "Article ID: 386360",
}
@Article{banzhaf:2002:intro,
author = "W. Banzhaf",
title = "Editorial Introduction",
journal = "Genetic Programming and Evolvable Machines",
year = "2002",
volume = "3",
number = "1",
pages = "5--6",
month = mar,
ISSN = "1389-2576",
doi = "doi:10.1023/A:1017427619473",
size = "2 pages",
notes = "Article ID: 395987",
}
@Article{banzhaf:2002:ack,
author = "W. Banzhaf",
title = "Acknowledgement",
journal = "Genetic Programming and Evolvable Machines",
year = "2002",
volume = "3",
number = "4",
pages = "327",
month = dec,
ISSN = "1389-2576",
doi = "doi:10.1023/A:1020989508176",
notes = "Article ID: 5103871",
}
@Article{banzhaf:2003:intro,
author = "Wolfgang Banzhaf",
title = "Editorial Introduction",
journal = "Genetic Programming and Evolvable Machines",
year = "2003",
volume = "4",
number = "1",
pages = "5--6",
month = mar,
ISSN = "1389-2576",
doi = "doi:10.1023/A:1021808625350",
notes = "Article ID: 5113069",
}
@InCollection{banzhaf:2003:GPTP,
author = "Wolfgang Banzhaf",
title = "Artificial Regulatory Networks and Genetic
Programming",
booktitle = "Genetic Programming Theory and Practice",
publisher = "Kluwer",
year = "2003",
editor = "Rick L. Riolo and Bill Worzel",
chapter = "4",
pages = "43--62",
keywords = "genetic algorithms, genetic programming, Regulatory
Networks, Artificial Evolution, Evolutionary
Algorithms, Development, Heterochrony",
notes = "Part of \cite{RioloWorzel:2003}",
size = "20 pages",
}
@InCollection{banzhaf:2003:ACI,
author = "Wolfgang Banzhaf and Markus Brameier and Marc Stautner
and Klaus Weinert",
title = "Genetic Programming and Its Application in Machining
Technology",
booktitle = "Advances in Computational Intelligence: Theory and
Practice",
publisher = "Springer",
year = "2003",
editor = "Hans-Paul Schwefel and Ingo Wegener and Klaus
Weinert",
series = "Natural Computing Series",
chapter = "7",
pages = "194--242?",
keywords = "genetic algorithms, genetic programming, Linear
Genetic Programming",
ISBN = "3-540-43269-8",
notes = "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",
}
@Article{banzhaf:2004:intro,
author = "Wolfgang Banzhaf",
title = "Editorial Introduction",
journal = "Genetic Programming and Evolvable Machines",
year = "2004",
volume = "5",
number = "1",
pages = "5",
month = mar,
ISSN = "1389-2576",
doi = "doi:10.1023/B:GENP.0000017050.75941.55",
notes = "Article ID: 5264730",
}
@Article{banzhaf:2004:ack,
author = "W. Banzhaf",
title = "Acknowledgement",
journal = "Genetic Programming and Evolvable Machines",
year = "2004",
volume = "5",
number = "1",
pages = "7",
month = dec,
ISSN = "1389-2576",
doi = "doi:10.1023/B:GENP.0000017051.93386.43",
notes = "Article ID: 5264731",
}
@Article{banzhaf:2004:biogec,
author = "Wolfgang Banzhaf and James Foster",
title = "Editorial Introduction",
journal = "Genetic Programming and Evolvable Machines",
year = "2004",
volume = "5",
number = "2",
pages = "119--120",
month = jun,
keywords = "genetic algorithms, genetic programming,
bioinformatics",
ISSN = "1389-2576",
doi = "doi:10.1023/B:GENP.0000023710.47388.8b",
notes = "BioGEC Special Issue on Biological Applications of
Genetic and Evolutionary Computation Guest Editor(s):
Wolfgang Banzhaf , James Foster",
}
@InCollection{banzhaf:2004:GPTP,
author = "Wolfgang Banzhaf and Christian W. G. Lasarczyk",
title = "Genetic Programming of an Algorithmic Chemistry",
booktitle = "Genetic Programming Theory and Practice {II}",
year = "2004",
editor = "Una-May O'Reilly and Tina Yu and Rick L. Riolo and
Bill Worzel",
chapter = "11",
pages = "175--190",
address = "Ann Arbor",
month = "13-15 " # may,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, artificial
chemistry",
ISBN = "0-387-23253-2",
URL = "http://www.cs.mun.ca/~banzhaf/papers/algochem.pdf",
doi = "doi:10.1007/0-387-23254-0_11",
size = "16 pages",
abstract = "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.",
notes = "part of \cite{oreilly:2004:GPTP2} Sin, UCI thyroid",
}
@Article{banzhaf:2004:BPC,
author = "Wolfgang Banzhaf and P. Dwight Kuo",
title = "Network motifs in natural and artificial
transcriptional regulatory networks",
journal = "Journal of Biological Physics and Chemistry",
year = "2004",
volume = "4",
number = "2",
pages = "50--63",
keywords = "artificial life",
ISBN = "0-387-23253-2",
URL = "http://www.cs.mun.ca/~kuo/Motifs_Numerical_journal.pdf",
URL = "http://www.amsi.ge/jbpc/20404/2040405.html",
size = "11 pages",
abstract = "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.",
}
@InCollection{banzhaf:2004:cc,
title = "The Challenge of Complexity",
author = "Wolfgang Banzhaf and Julian Miller",
booktitle = "Frontiers of Evolutionary Computation",
editor = "Anil Menon",
series = "Genetic Algorithms And Evolutionary Computation
Series",
volume = "11",
chapter = "11",
publisher = "Kluwer Academic Publishers",
address = "Boston, MA, USA",
year = "2004",
pages = "73--99",
keywords = "genetic algorithms, genetic programming, Evolutionary
Algorithm, Complexity, Scaling Problem, Development,
Heterochrony",
ISBN = "1-4020-7524-3",
URL = "http://www.cs.mun.ca/~banzhaf/papers/challenge_rev.pdf",
abstract = "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",
size = "pages",
}
@InProceedings{banzhaf:2005:cPC,
author = "Wolfgang Banzhaf",
title = "Challenging the Program Counter",
booktitle = "The Grand Challenge in Non-Classical Computation:
International Workshop",
year = "2005",
editor = "Susan Stepney and Stephen Emmott",
address = "York, UK",
month = "18-19 " # apr,
organisation = "University of York and Microsoft Research",
keywords = "genetic algorithms, genetic programming, artificial
chemistry",
URL = "http://www.cs.york.ac.uk/nature/workshop/papers/Banzhaf.pdf",
size = "3 pages",
notes = "http://www.cs.york.ac.uk/nature/workshop/",
}
@Article{banzhaf:2005:intro,
author = "Wolfgang Banzhaf",
title = "Editorial",
journal = "Genetic Programming and Evolvable Machines",
year = "2005",
volume = "5",
number = "2",
pages = "135--136",
month = jun,
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-005-6162-z",
size = "1.1 pages",
}
@Article{banzhaf:2005:ack,
author = "W. Banzhaf",
title = "Acknowledgement",
journal = "Genetic Programming and Evolvable Machines",
year = "2005",
volume = "5",
number = "2",
pages = "137--138",
month = jun,
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-005-6163-y",
size = "1.2 pages",
}
@InCollection{banzhaf:2005:GPTP,
author = "Wolfgang Banzhaf and Andre Leier",
title = "Evolution on Neutral Networks in Genetic Programming",
booktitle = "Genetic Programming Theory and Practice {III}",
year = "2005",
editor = "Tina Yu and Rick L. Riolo and Bill Worzel",
volume = "9",
series = "Genetic Programming",
chapter = "14",
pages = "207--221",
address = "Ann Arbor",
month = "12-14 " # may,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, Neutrality,
Linear GP, Networks, Population Dynamics",
ISBN = "0-387-28110-X",
URL = "http://www.cs.mun.ca/~banzhaf/papers/GPTP3.pdf",
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.137.7947",
oai = "oai:CiteSeerXPSU:10.1.1.137.7947",
size = "15 pages",
abstract = "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.",
notes = "part of \cite{yu:2005:GPTP} Published Jan 2006 after
the workshop",
}
@Article{banzhaf:2006:intro,
author = "Wolfgang Banzhaf",
title = "Introduction",
journal = "Genetic Programming and Evolvable Machines",
year = "2006",
volume = "6",
number = "1",
pages = "5--6",
month = mar,
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-006-7015-0",
size = "1.1 pages",
}
@Article{banzhaf:2006:ack,
author = "W. Banzhaf",
title = "Acknowledgement",
journal = "Genetic Programming and Evolvable Machines",
year = "2006",
volume = "6",
number = "1",
pages = "7",
month = mar,
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-006-7016-z",
size = "1 page",
notes = "list of reviewers",
}
@Article{Banzhaf:2006:NRG,
author = "Wolfgang Banzhaf and Guillaume Beslon and Steffen
Christensen and James Foster and Francois Kepes and
Virginie Lefort and Julian Miller and Miroslav Radman
and Jeremy J. Ramsden",
title = "From Artificial Evolution to Computational Evolution:
{A} Research Agenda",
journal = "Nature Reviews Genetics",
year = "2006",
volume = "7",
number = "9",
pages = "729--735",
month = sep,
keywords = "genetic algorithms, genetic programming",
ISSN = "1471-0056",
doi = "doi:10.1038/nrg1921",
size = "7 pages",
abstract = "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.",
notes = "We thank the people at Genopole Recherche, Evry,
France, for generously sponsoring the meeting that
initiated this paper.",
}
@Article{banzhaf:2007:intro,
author = "Wolfgang Banzhaf",
title = "Editorial introduction",
journal = "Genetic Programming and Evolvable Machines",
year = "2007",
volume = "8",
number = "1",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-007-9022-1",
size = "2 pages",
abstract = "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.",
}
@InCollection{Banzhaf:2008:GPTP,
author = "Wolfgang Banzhaf and Simon Harding and William B.
Langdon and Garnett Wilson",
title = "Accelerating Genetic Programming through Graphics
Processing Units",
booktitle = "Genetic Programming Theory and Practice {VI}",
year = "2008",
editor = "Rick L. Riolo and Terence Soule and Bill Worzel",
series = "Genetic and Evolutionary Computation",
chapter = "15",
pages = "229--249",
address = "Ann Arbor",
month = "15-17" # may,
publisher = "Springer",
size = "20 pages",
keywords = "genetic algorithms, genetic programming, graphics
processing units, parallel processing, GPU",
doi = "doi:10.1007/978-0-387-87623-8_15",
size = "19 pages",
abstract = "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.",
isbn13 = "978-0-387-87622-1",
notes = "part of \cite{Riolo:2008:GPTP} To be published late
2008",
}
@InProceedings{Bao:2009:ICNC,
author = "Yun Bao and Erbo Zhao and Xiaocong Gan and Dan Luo and
Zhangang Han",
title = "A Review on Cutting-Edge Techniques in Evolutionary
Algorithms",
booktitle = "Fifth International Conference on Natural Computation,
2009. ICNC '09",
year = "2009",
month = aug,
volume = "5",
pages = "347--351",
keywords = "genetic algorithms, genetic programming, EA
performance improvements, convergence speed,
cutting-edge techniques, evolutionary algorithms,
nuclear power plant, evolutionary computation",
doi = "doi:10.1109/ICNC.2009.459",
abstract = "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.",
notes = "Also known as \cite{5364257}",
}
@PhdThesis{ceg_baptist_20050418,
author = "Martin Josephus Baptist",
title = "Modelling floodplain biogeomorphology",
school = "Technische Universiteit Delft",
year = "2005",
address = "Holland",
month = "18 " # apr,
publisher = "Delft University Press",
ISBN = "90-407-2582-9",
keywords = "genetic algorithms, genetic programming",
URL = "http://repository.tudelft.nl/assets/uuid...e2f6.../ceg_baptist_20050418.pdf",
size = "213 pages",
abstract = "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.
...",
notes = "Supervisor H.J. de Vriend. In english",
}
@Article{Baptist:2007:JHR,
author = "M. J. Baptist and Vladan Babovic and J. {Rodriguez
Uthurburu} and M. Keijzer and R. E. Uittenbogaard and
A. Mynett and A. Verwey",
title = "On inducing equations for vegetation resistance",
journal = "Journal of Hydraulic Research",
year = "2007",
volume = "45",
number = "4",
pages = "435--450",
keywords = "genetic algorithms, genetic programming, vegetation,
roughness, resistance, knowledge discovery",
doi = "doi:10.1080/00221686.2007.9521778",
size = "16 pages",
abstract = "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",
}
@Article{BarabasiEtAl01,
author = "Albert-Laszlo Barabasi and Vincent W. Freeh and
Hawoong Jeong and Jay B. Brockman",
title = "Parasitic Computing",
journal = "Nature",
volume = "412",
year = "2001",
pages = "894--897",
month = "30 " # aug,
keywords = "16-SAT",
URL = "http://www.nd.edu/~alb/Publication06/082%20Parasitic%20computing/Parasitic%20computing.pdf",
doi = "doi:10.1038/35091039",
size = "3 pages",
abstract = "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",
notes = "fig1 p895 Segement (TCP packet) dropped due to invalid
checksum
One property of the TCP checksum function is that it
forms a suf®cient 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 \cite{arXiv:cs/0701115v1}",
}
@InProceedings{baradavka03,
author = "Igor Baradavka and Tatiana Kalganova",
title = "Assembling Strategies in Extrinsic Evolvable Hardware
with Bidirectional Incremental Evolution",
booktitle = "Genetic Programming, Proceedings of EuroGP'2003",
year = "2003",
editor = "Conor Ryan and Terence Soule and Maarten Keijzer and
Edward Tsang and Riccardo Poli and Ernesto Costa",
volume = "2610",
series = "LNCS",
pages = "276--285",
address = "Essex",
publisher_address = "Berlin",
month = "14-16 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, evolvable
hardware",
ISBN = "3-540-00971-X",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=276",
abstract = "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).",
notes = "EuroGP'2003 held in conjunction with EvoWorkshops
2003",
}
@InProceedings{barash:1998:mGAofsalf,
author = "Danny Barash and Ann Orel and V. Rao Vemuri",
title = "Micro Genetic Algorithms in Finding the Optimal
Frequency for Stabilizing Atoms by High-intensity Laser
Fields",
booktitle = "Late Breaking Papers at the Genetic Programming 1998
Conference",
year = "1998",
editor = "John R. Koza",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "Stanford, CA, USA",
month = "22-25 " # jul,
publisher = "Stanford University Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "GP-98LB",
}
@InProceedings{DBLP:conf/ae/BarateM07,
author = "Renaud Barate and Antoine Manzanera",
title = "Automatic Design of Vision-Based Obstacle Avoidance
Controllers Using Genetic Programming",
year = "2007",
volume = "4926",
bibdate = "2008-05-16",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/ae/ae2007.html",
booktitle = "Artificial Evolution",
editor = "Nicolas Monmarch{\'e} and El-Ghazali Talbi and Pierre
Collet and Marc Schoenauer and Evelyne Lutton",
isbn13 = "978-3-540-79304-5",
pages = "25--36",
series = "Lecture Notes in Computer Science",
address = "Tours, France",
month = oct # " 29-31",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1007/978-3-540-79305-2_3",
abstract = "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.",
notes = "EA'07",
}
@InProceedings{Barate:2008:gecco,
author = "Renaud Barate and Antoine Manzanera",
title = "Generalization performance of vision based controllers
for mobile robots evolved with genetic programming",
booktitle = "GECCO '08: Proceedings of the 10th annual conference
on Genetic and evolutionary computation",
year = "2008",
editor = "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",
isbn13 = "978-1-60558-130-9",
pages = "1331--1332",
address = "Atlanta, GA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1331.pdf",
doi = "doi:10.1145/1389095.1389349",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "12-16 " # jul,
keywords = "genetic algorithms, genetic programming,
generalisation, Obstacle avoidance, robotic simulation,
vision, Poster",
notes = "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 \cite{1389349}",
}
@InProceedings{DBLP:conf/sab/BarateM08,
author = "Renaud Barate and Antoine Manzanera",
title = "Evolving Vision Controllers with a Two-Phase Genetic
Programming System Using Imitation",
booktitle = "From Animals to Animats 10, Proceedings of the 10th
International Conference on Simulation of Adaptive
Behavior, SAB 2008",
year = "2008",
editor = "Minoru Asada and John C. T. Hallam and Jean-Arcady
Meyer and Jun Tani",
series = "Lecture Notes in Computer Science",
volume = "5040",
pages = "73--82",
address = "Osaka, Japan",
month = jul # " 7-12",
publisher = "Springer",
bibsource = "DBLP, http://dblp.uni-trier.de",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-540-69133-4",
doi = "doi:10.1007/978-3-540-69134-1_8",
abstract = "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.",
notes = "part of \cite{DBLP:conf/sab/2008}",
notes = "From Animals to Animats 10",
}
@InProceedings{Barate:2008:ECSIS-LAB-RS,
author = "Renaud Barate and Antoine Manzanera",
title = "Learning Vision Algorithms for Real Mobile Robots with
Genetic Programming",
booktitle = "ECSIS Symposium on Learning and Adaptive Behaviors for
Robotic Systems, LAB-RS '08",
year = "2008",
month = aug,
pages = "47--52",
keywords = "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",
doi = "doi:10.1109/LAB-RS.2008.20",
abstract = "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.",
notes = "Also known as \cite{4599426}",
}
@PhdThesis{Barate:thesis,
author = "Renaud Barate",
title = "Learning Visual Functions for a Mobile Robot with
Genetic Programming",
title_fr = "Apprentissage de fonctions visuelles pour un robot
mobile par programmation genetique",
school = "ENSTA",
year = "2008",
address = "32 Bd Victor 75015 Paris",
month = nov,
note = "In French",
email = "Contact : Antoine.Manzanera@ensta.fr",
keywords = "genetic algorithms, genetic programming, Vision,
mobile robotics, obstacle avoidance",
URL = "http://www.ensta.fr/~manzaner/Publis/these-barate.pdf",
size = "149 pages",
abstract = "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.",
resume = "En robotique mobile, les techniques d'apprentissage
qui utilisent la vision artificielle representent le
plus souvent l'image par un ensemble de descripteurs
visuels. Ces descripteurs sont extraits en utilisant
une methode fixee a l'avance ce qui compromet les
capacites d'adaptation du systeme a un environnement
visuel changeant. Nous proposons une methode permettant
de decrire et d'apprendre des algorithmes de vision de
maniere globale, depuis l'image percue jusqu'a la
decision finale. L'application visee est la fonction
d'evitement d'obstacles, indispensable a tout robot
mobile. Nous decrivons de maniere formelle la structure
des algorithmes d'evitement d'obstacles bases sur la
vision en utilisant une grammaire. Notre systeme
utilise ensuite cette grammaire et des techniques de
programmation genetique pour apprendre automatiquement
des controleurs adaptes a un contexte visuel donne.
Nous utilisons un environnement de simulation pour
tester notre approche et mesurer les performances des
algorithmes evolues. Nous proposons plusieurs
techniques permettant d'accelerer l'evolution et
d'ameliorer les performances et les capacites de
generalisation des controleurs evolues. Nous comparons
notamment plusieurs methodes d'evolution guidee et nous
en presentons une nouvelle basee sur l'imitation d'un
comportement enregistre. Par la suite nous validons ces
methodes sur un robot reel se deplacant dans un
environnement interieur. Nous indiquons finalement
comment ce systeme peut etre adapte a d'autres
applications utilisant la vision et nous proposons des
pistes pour l'adaptation d'un comportement en temps
reel sur le robot.",
notes = "Francais",
}
@InProceedings{Barbulescu:2009:WSEAS,
author = "Alina Barbulescu and Elena Bautu",
title = "Meteorological time series modeling using an adaptive
gene expression programming",
booktitle = "Proceedings of the 10th WSEAS International Conference
on Evolutionary Computation",
year = "2009",
editor = "Nikos E. Mastorakis and Anca Croitoru and Valentina
Emilia Balas and Eduard Son and Valeri Mladenov",
pages = "17--22",
address = "Prague, Czech",
publisher_address = "Stevens Point, Wisconsin, USA",
month = "23-25 " # mar,
publisher = "World Scientific and Engineering Academy and Society
(WSEAS)",
keywords = "genetic algorithms, genetic programming, Gene
Expression Programming",
isbn13 = "978-960-474-067-3",
ISSN = "1790-5109",
acmid = "1561917",
abstract = "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.",
notes = "http://www.wseas.org/conferences/2009/prague/ec/index.html",
}
@InProceedings{Barbulescu:2009:WSEASb,
author = "Alina Barbulescu and Elena Bautu",
title = "{ARIMA} Models versus Gene Expression Programming in
Precipitation Modeling",
booktitle = "Proceedings of the 10th WSEAS International Conference
on Evolutionary Computation",
year = "2009",
editor = "Nikos E. Mastorakis and Anca Croitoru and Valentina
Emilia Balas and Eduard Son and Valeri Mladenov",
pages = "112--117",
address = "Prague, Czech",
publisher_address = "Stevens Point, Wisconsin, USA",
month = "23-25 " # mar,
publisher = "World Scientific and Engineering Academy and Society
(WSEAS)",
keywords = "genetic algorithms, genetic programming, Gene
Expression Programming",
isbn13 = "978-960-474-067-3",
ISSN = "1790-5109",
acmid = "1561931",
abstract = "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.",
notes = "http://www.wseas.org/conferences/2009/prague/ec/index.html",
}
@Article{Barbulescu20091,
author = "Alina Barbulescu and Elena Bautu",
title = "Alternative Models in Precipitation Analysis",
journal = "Analele Stiintifice ale Universitatii Ovidius
Constanta, Seria Matematica",
year = "2009",
volume = "XVII",
number = "3",
pages = "45--68",
keywords = "genetic algorithms, genetic programming, Gene
Expression Programming",
ISSN = "1844-0835",
URL = "http://www.anstuocmath.ro/mathematics/pdf19/Barbulescu_Bautu.pdf",
size = "24 pages",
abstract = "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.",
notes = "http://www.anstuocmath.ro/",
}
@Article{Barbulescu20092,
author = "Alina Barbulescu and Elena Bautu",
title = "Time Series Modeling Using an Adaptive Gene Expression
Programming Algorithm",
year = "2009",
journal = "International Journal of Mathematical Models and
Methods in Applied Sciences",
volume = "3",
pages = "85--93",
number = "2",
keywords = "genetic algorithms, genetic programming, Gene
Expression Programming",
ISSN = "1998-0140",
URL = "http://www.naun.org/journals/m3as/mmmas-134.pdf",
size = "9 pages",
abstract = "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.",
notes = "http://www.naun.org/journals/m3as/",
}
@Article{Barbulescu201003,
author = "Alina Barbulescu and Elena Bautu",
title = "Mathematical models of climate evolution in
{Dobrudja}",
journal = "Theoretical and Applied Climatology",
year = "2010",
pages = "29--44",
volume = "100",
issue = "1",
month = mar,
publisher = "Springer Wien",
keywords = "genetic algorithms, genetic programming, gene
expression programming, ARIMA, Earth and Environmental
Science",
ISSN = "0177-798X",
doi = "doi:10.1007/s00704-009-0160-7",
size = "16 pages",
abstract = "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.",
affiliation = "Ovidius University of Constanta Faculty of Mathematics
and Informatics Constanta Romania",
}
@InProceedings{DBLP:conf/seal/BarileCT08,
author = "Perry Barile and Victor Ciesielski and Karen Trist",
title = "Non-photorealistic Rendering Using Genetic
Programming",
booktitle = "Proceedings of the 7th International Conference on
Simulated Evolution And Learning (SEAL '08)",
year = "2008",
editor = "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{\"u}rgen Branke
and Yuhui Shi",
volume = "5361",
series = "Lecture Notes in Computer Science",
pages = "299--308",
address = "Melbourne, Australia",
month = dec # " 7-10",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming,
non-photorealistic rendering, evolutionary
computation",
isbn13 = "978-3-540-89693-7",
doi = "doi:10.1007/978-3-540-89694-4_31",
abstract = "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.",
bibsource = "DBLP, http://dblp.uni-trier.de",
}
@InProceedings{DBLP:conf/gecco/BarileCBT09,
author = "Perry Barile and Victor Ciesielski and Marsha Berry
and Karen Trist",
title = "Animated drawings rendered by genetic programming",
booktitle = "GECCO '09: Proceedings of the 11th Annual conference
on Genetic and evolutionary computation",
year = "2009",
editor = "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",
pages = "939--946",
address = "Montreal",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "8-12 " # jul,
organisation = "SigEvo",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-60558-325-9",
bibsource = "DBLP, http://dblp.uni-trier.de",
doi = "doi:10.1145/1569901.1570030",
abstract = "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.",
notes = "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.",
}
@MastersThesis{barlow2004-thesis,
author = "Gregory J. Barlow",
title = "Design of Autonomous Navigation Controllers for
Unmanned Aerial Vehicles Using Multi-objective Genetic
Programming",
school = "North Carolina State University",
year = "2004",
address = "Raleigh, NC, USA",
month = mar,
keywords = "genetic algorithms, genetic programming, mobile
robotics, evolutionary robotics, multi-objective
optimization, incremental evolution, unmanned aerial
vehicles",
URL = "http://www.andrew.cmu.edu/user/gjb/includes/publications/thesis/barlow2004-thesis/barlow2004-thesis.pdf",
size = "181 pages",
abstract = "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.",
notes = "ADA460111",
}
@InProceedings{barlow:2004:lbp,
author = "Gregory J. Barlow and Choong K. Oh and Edward Grant",
title = "Incremental Evolution of Autonomous Controllers for
Unmanned Aerial Vehicles using Multi-objective Genetic
Programming",
booktitle = "Late Breaking Papers at the 2004 Genetic and
Evolutionary Computation Conference",
year = "2004",
editor = "Maarten Keijzer",
address = "Seattle, Washington, USA",
month = "26 " # jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.andrew.cmu.edu/user/gjb/includes/publications/other/barlow2004-geccolbp/barlow2004-geccolbp.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/LBP011.pdf",
abstract = "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.",
notes = "Part of \cite{keijzer:2004:GECCO:lbp}",
}
@InProceedings{barlow:2004:geccogsw,
author = "Gregory J. Barlow",
title = "Autonomous Controller Design for Unmanned Aerial
Vehicles using Multi-objective Genetic Programming",
booktitle = "Proceedings of the Graduate Student Workshop at the
2004 Genetic and Evolutionary Computation Conference
(GECCO-2004)",
editor = "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",
year = "2004",
address = "Seattle, Washington, USA",
month = "24-26 " # jun,
keywords = "genetic algorithms, genetic programming, evolutionary
robotics, multi-objective optimisation, unmanned aerial
vehicles",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/WGSW001.pdf",
URL = "http://www.andrew.cmu.edu/user/gjb/includes/publications/conference/barlow2004-geccogsw/barlow2004-geccogsw.pdf",
abstract = "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.",
notes = "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",
}
@InProceedings{barlow2004-cis,
author = "Gregory J. Barlow and Choong K. Oh and Edward Grant",
title = "Incremental Evolution of Autonomous Controllers for
Unmanned Aerial Vehicles using Multi-objective Genetic
Programming",
booktitle = "Proceedings of the 2004 IEEE Conference on Cybernetics
and Intelligent Systems (CIS)",
year = "2004",
pages = "688--693",
address = "Singapore",
month = "1-3 " # dec,
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming, incremental
evolution, multi-objective optimisation",
URL = "http://www.cs.cmu.edu/~gjb/includes/publications/conference/barlow2004-cis/barlow2004-cis.pdf",
abstract = "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.",
notes = "IEEE CIS RAM 2004 http://cis-ram.nus.edu.sg/",
}
@InProceedings{barlow2005-icra,
author = "Gregory J. Barlow and Leonardo S. Mattos and Edward
Grant and Choong K. Oh",
title = "Transference of Evolved Unmanned Aerial Vehicle
Controllers to a Wheeled Mobile Robot",
booktitle = "Proceedings of the IEEE International Conference on
Robotics and Automation",
year = "2005",
editor = "Ruediger Dillmann",
address = "Barcelona, Spain",
month = "18-22 " # apr,
organisation = "IEEE Robotics and Automation Society",
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.cmu.edu/~gjb/includes/publications/conference/barlow2005-icra/barlow2005-icra.pdf",
abstract = "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.",
notes = "http://www.icra2005.org/
",
}
@InProceedings{1144023,
author = "Gregory J. Barlow and Choong K. Oh",
title = "Robustness analysis of genetic programming controllers
for unmanned aerial vehicles",
booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference
on Genetic and evolutionary computation",
year = "2006",
editor = "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",
volume = "1",
ISBN = "1-59593-186-4",
pages = "135--142",
address = "Seattle, Washington, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p135.pdf",
doi = "doi:10.1145/1143997.1144023",
publisher = "ACM Press",
publisher_address = "New York, NY, 10286-1405, USA",
month = "8-12 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, Artificial
Life Evolutionary Robotics, Adaptive Behavior,
autonomous vehicles, program synthesis, reliability,
robustness, synthesis, transference, unmanned aerial
vehicles",
notes = "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",
}
@InProceedings{Barlow:2008:gecco,
author = "Gregory J. Barlow and Choong K. Oh and Stephen F.
Smith",
title = "Evolving cooperative control on sparsely distributed
tasks for {UAV} teams without global communication",
booktitle = "GECCO '08: Proceedings of the 10th annual conference
on Genetic and evolutionary computation",
year = "2008",
editor = "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",
isbn13 = "978-1-60558-130-9",
pages = "177--184",
address = "Atlanta, GA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p177.pdf",
doi = "doi:10.1145/1389095.1389125",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "12-16 " # jul,
keywords = "genetic algorithms, genetic programming, evolutionary
robotics, multiagent systems, multiobjective
optimisation, unmanned aerial vehicles, Artificial
life, adaptive behaviour, evolvable hardware",
notes = "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 \cite{1389125}",
}
@Article{Barmpalexis2011122,
author = "Panagiotis Barmpalexis and Kyriakos Kachrimanis and
Emanouil Georgarakis",
title = "Solid dispersions in the development of a nimodipine
floating tablet formulation and optimization by
artificial neural networks and genetic programming",
journal = "European Journal of Pharmaceutics and
Biopharmaceutics",
volume = "77",
number = "1",
pages = "122--131",
year = "2011",
ISSN = "0939-6411",
doi = "doi:10.1016/j.ejpb.2010.09.017",
URL = "http://www.sciencedirect.com/science/article/B6T6C-51696TP-1/2/61fc7d46e9a66d451646234b5e96dedb",
keywords = "genetic algorithms, genetic programming, Solid
dispersions, Nimodipine, Controlled release,
Effervescent floating tablets, Artificial neural
networks",
abstract = "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.",
}
@Article{Barmpalexis201175,
author = "P. Barmpalexis and K. Kachrimanis and A. Tsakonas and
E. Georgarakis",
title = "Symbolic regression via genetic programming in the
optimization of a controlled release pharmaceutical
formulation",
journal = "Chemometrics and Intelligent Laboratory Systems",
volume = "107",
number = "1",
pages = "75--82",
year = "2011",
ISSN = "0169-7439",
doi = "doi:10.1016/j.chemolab.2011.01.012",
URL = "http://www.sciencedirect.com/science/article/B6TFP-523CDG2-4/2/67c4e87b7f04a0e4f5f6fe07a1127ef8",
keywords = "genetic algorithms, genetic programming, Artificial
neural networks, Controlled release, Experimental
design, Optimisation",
abstract = "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).",
}
@Misc{oai:arXiv.org:quant-ph/9907056,
title = "A quantum circuit for {OR}",
author = "Howard Barnum and Herbert J. Bernstein and Lee
Spector",
year = "1999",
month = oct # "~08",
abstract = "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''.",
oai = "oai:arXiv.org:quant-ph/9907056",
URL = "http://arXiv.org/abs/quant-ph/9907056",
URL = "http://arxiv.org/PS_cache/quant-ph/pdf/9907/9907056.pdf",
howpublished = "arXiv.or",
keywords = "genetic algorithms, genetic programming",
size = "6 pages",
notes = "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",
}
@TechReport{2000-barnum-2,
author = "Howard Barnum and Herbert J Bernstein and Lee
Spector",
title = "Quantum circuits for {OR} and {AND} of {OR}'s",
year = "2000",
institution = "University of Bristol",
address = "UK",
month = aug,
keywords = "genetic algorithms, genetic programming",
abstract-url = "http://www.cs.bris.ac.uk/Publications/pub_info.jsp?id=1000497",
URL = "http://www.cs.bris.ac.uk/Publications/Papers/1000497.pdf",
pubtype = "117",
abstract = "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.",
notes = "See also \cite{barnum:2000:qc}",
size = "15 pages",
}
@Article{barnum:2000:qc,
author = "Howard Barnum and Herbert J Bernstein and Lee
Spector",
title = "Quantum circuits for {OR} and {AND} of {ORs}",
journal = "Journal of Physics A: Mathematical and General",
year = "2000",
volume = "33",
number = "45",
pages = "8047--8057",
month = "17 " # nov,
keywords = "genetic algorithms, genetic programming",
URL = "http://hampshire.edu/lspector/pubs/jpa.pdf",
URL = "http://hampshire.edu/lspector/pubs/jpa.ps",
abstract = "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",
notes = "reports new quantum algorithms discovered by GP, with
some details on the GP processes",
}
@InProceedings{baron:1999:S,
author = "Christophe Baron and Guy Gouarderes",
title = "Systemions to model alternative issues in problem
solving",
booktitle = "Late Breaking Papers at the 1999 Genetic and
Evolutionary Computation Conference",
year = "1999",
editor = "Scott Brave and Annie S. Wu",
pages = "31--37",
address = "Orlando, Florida, USA",
month = "13 " # jul,
notes = "GECCO-99LB",
}
@InProceedings{baronti:2002:gecco:lbp,
title = "Enhancing Tournament Selection to Prevent Code Bloat
in Genetic Programming",
author = "Flavio Baronti and Antonina Starita",
booktitle = "Late Breaking Papers at the Genetic and Evolutionary
Computation Conference ({GECCO-2002})",
editor = "Erick Cant{\'u}-Paz",
year = "2002",
month = jul,
pages = "17--22",
address = "New York, NY",
publisher = "AAAI",
publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
keywords = "genetic algorithms, genetic programming",
notes = "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",
}
@InProceedings{Barrero:2010:gecco,
author = "David F. Barrero and David Camacho and Maria D.
R-Moreno",
title = "Confidence intervals of success rates in evolutionary
computation",
booktitle = "GECCO '10: Proceedings of the 12th annual conference
on Genetic and evolutionary computation",
year = "2010",
editor = "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",
isbn13 = "978-1-4503-0072-8",
pages = "975--976",
keywords = "Genetic programming: Poster",
month = "7-11 " # jul,
organisation = "SIGEVO",
address = "Portland, Oregon, USA",
doi = "doi:10.1145/1830483.1830657",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Santa Fe trail artificial ant Also known as
\cite{1830657} GECCO-2010 A joint meeting of the
nineteenth international conference on genetic
algorithms (ICGA-2010) and the fifteenth annual genetic
programming conference (GP-2010)",
}
@InProceedings{barrero:2011:EuroGP,
author = "David F. Barrero and Bonifacio Casta\~no and Maria D.
R-Moreno and David Camacho",
title = "Statistical Distribution of Generation-to-Success in
{GP}: Application to Model Accumulated Success
Probability",
booktitle = "Proceedings of the 14th European Conference on Genetic
Programming, EuroGP 2011",
year = "2011",
month = "27-29 " # apr,
editor = "Sara Silva and James A. Foster and Miguel Nicolau and
Mario Giacobini and Penousal Machado",
series = "LNCS",
volume = "6621",
publisher = "Springer Verlag",
address = "Turin, Italy",
pages = "154--165",
organisation = "EvoStar",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-20406-7",
doi = "doi:10.1007/978-3-642-20407-4_14",
abstract = "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.",
notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in
conjunction with EvoCOP2011 EvoBIO2011 and
EvoApplications2011",
}
@InProceedings{Barrero:2011:AESotAoCEiGP,
title = "An Empirical Study on the Accuracy of Computational
Effort in Genetic Programming",
author = "David F. Barrero and Maria R-Moreno and Bonifacio
Castano and David Camacho",
pages = "1169--1176",
booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary
Computation",
year = "2011",
editor = "Alice E. Smith",
month = "5-8 " # jun,
address = "New Orleans, USA",
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "CEC2011 sponsored by the IEEE Computational
Intelligence Society, and previously sponsored by the
EPS and the IET.",
}
@Article{Barrett:2005:TP,
author = "John Barrett and Aneta Kostadinova and Juan Antonio
Raga",
title = "Mining parasite data using genetic programming",
journal = "Trends in Parasitology",
year = "2005",
volume = "21",
number = "5",
pages = "207--209",
month = may,
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1016/j.pt.2005.03.007",
abstract = "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.",
}
@InProceedings{barrett:2003:dmtmb,
author = "S. J. Barrett",
title = "Recurring Analytical Problems within Drug Discovery
and Development",
booktitle = "Data Mining and Text Mining for Bioinformatics:
Proceedings of the European Workshop",
year = "2003",
editor = "Tobias Scheffer and Ulf Leser",
pages = "6--7",
address = "Dubrovnik, Croatia",
month = "22 " # sep,
organisation = "KDnet",
note = "Invited talk",
keywords = "genetic algorithms, genetic programming, SVM, SNP",
URL = "http://www2.informatik.hu-berlin.de/~scheffer/publications/ProceedingsWS2003.pdf",
abstract = "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.",
notes = "Held in Conjunction with ECML / PKDD- 2003",
}
@InProceedings{barrett:2005:WSC,
author = "S. J. Barrett and W. B. Langdon",
title = "Advances in the Application of Machine Learning
Techniques in Drug Discovery, Design and Development",
booktitle = "Applications of Soft Computing: Recent Trends",
year = "2006",
editor = "Ashutosh Tiwari and Joshua Knowles and Erel Avineri
and Keshav Dahal and Rajkumar Roy",
series = "Advances in Soft Computing",
pages = "99--110",
address = "On the World Wide Web",
month = "19 " # sep # " - 7 " # oct # " 2005",
organisation = "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)",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming,
Pharmaceutical applications, Drug design, Particle
swarm optimisation, Support vector machines",
ISBN = "ISBN 3-540-29123-7",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/barrett_2005_WSC.pdf",
URL = "http://isxp1010c.sims.cranfield.ac.uk/Papers/paper196.pdf",
size = "21 pages",
abstract = "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.",
notes = "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.",
}
@Article{Barrett:2006:GPEM,
author = "Steven J. Barrett",
title = "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",
journal = "Genetic Programming and Evolvable Machines",
year = "2006",
volume = "7",
number = "3",
pages = "283--284",
month = oct,
note = "Book review",
keywords = "genetic algorithms, genetic programming",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-006-7003-4",
}
@InProceedings{Barriere:2008:PPSN,
author = "Olivier Barriere and Evelyne Lutton and Cedric Baudrit
and Mariette Sicard and Bruno Pinaud and Nathalie
Perrot",
title = "Modeling human expertise on a cheese ripening
industrial process using {GP}",
booktitle = "Parallel Problem Solving from Nature - PPSN X",
year = "2008",
editor = "Gunter Rudolph and Thomas Jansen and Simon Lucas and
Carlo Poloni and Nicola Beume",
volume = "5199",
series = "LNCS",
pages = "859--868",
address = "Dortmund",
month = "13-17 " # sep,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-87699-5",
url_fake = "http://metronum.futurs.inria.fr/html/Papers/files/pdf/Barriere_18-06-2008_INCALIN-PPSN2008-Final.pdf",
doi = "doi:10.1007/978-3-540-87700-4_85",
size = "10 pages",
abstract = "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.",
notes = "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
\cite{inria-00381681}
PPSN X",
}
@TechReport{inria-00381681,
title = "Modeling an agrifood industrial process using
cooperative coevolution Algorithms",
author = "Olivier Barriere and Evelyne Lutton and Pierre-Henri
Wuillemin and Cedric Baudrit and Mariette Sicard and
Bruno Pinaud and Nathalie Perrot",
institution = "INRIA",
year = "2009",
number = "inria-00381681, version 1",
address = "Parc Orsay, France",
month = "6 " # may,
keywords = "genetic algorithms, genetic programming, Parisian,
Computer Science, Artificial Intelligence, Life
Sciences/Food and Nutrition, Agrifood, Cheese ripening,
Cooperative coevolution, Parisian approach, Bayesian
Network",
URL = "http://hal.inria.fr/inria-00381681/en/",
URL = "http://hal.inria.fr/docs/00/38/16/81/PDF/RR2008.pdf",
bibsource = "OAI-PMH server at oai.archives-ouvertes.fr",
identifier = "HAL:inria-00381681, version 1",
language = "EN",
oai = "oai:hal.archives-ouvertes.fr:inria-00381681_v1",
abstract = "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.",
size = "51 pages",
}
@InProceedings{Barros:2011:GECCOcomp,
author = "Rodrigo C. Barros and Marcio P. Basgalupp and Andre C.
P. L. F. {de Carvalho} and Alex A. Freitas",
title = "Towards the automatic design of decision tree
induction algorithms",
booktitle = "GECCO 2011 1st workshop on evolutionary computation
for designing generic algorithms",
year = "2011",
editor = "Gisele L. Pappa and Alex A. Freitas and Jerry Swan and
John Woodward",
isbn13 = "978-1-4503-0690-4",
keywords = "genetic algorithms, genetic programming",
pages = "567--574",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001858.2002050",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{2002050} Distributed on CD-ROM at
GECCO-2011.
ACM Order Number 910112.",
}
@Article{Barros2011954,
author = "Rodrigo C. Barros and Duncan D. Ruiz and Marcio P.
Basgalupp",
title = "Evolutionary model trees for handling continuous
classes in machine learning",
journal = "Information Sciences",
year = "2011",
volume = "181",
number = "5",
pages = "954--971",
keywords = "genetic algorithms, genetic programming, Evolutionary
algorithms, Model trees, Continuous classes, Machine
learning",
ISSN = "0020-0255",
URL = "http://www.sciencedirect.com/science/article/B6V0C-51GHWYC-1/2/2ba74d92cb03abc637a4c377b47a4dbe",
doi = "doi:10.1016/j.ins.2010.11.010",
size = "18 pages",
abstract = "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.",
}
@InProceedings{barry:1999:AXCSPE,
author = "Alwyn Barry",
title = "Aliasing in {XCS} and the Consecutive State Problem: 1
- Effects",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "19--26",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-317.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-317.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{barry:1999:AXCSPS,
author = "Alwyn Barry",
title = "Aliasing in {XCS} and the Consecutive State Problem: 2
- Solutions",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "27--34",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-336.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-336.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@Proceedings{barry:2002:gecco:workshop,
title = "{GECCO 2002}: Proceedings of the Bird of a Feather
Workshops, Genetic and Evolutionary Computation
Conference",
editor = "Alwyn M. Barry",
year = "2002",
month = "8 " # jul,
publisher = "AAAI",
address = "New York",
publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
keywords = "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",
size = "330 pages",
notes = "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)",
}
@Proceedings{barry:2003:gecco:workshop,
title = "{GECCO 2003}: Proceedings of the Bird of a Feather
Workshops, Genetic and Evolutionary Computation
Conference",
editor = "Alwyn M. Barry",
year = "2003",
month = "11 " # jul,
publisher = "AAAI",
address = "Chigaco",
publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
keywords = "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",
size = "330 pages",
notes = "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",
}
@InProceedings{Bartoli:2011:EuroGP,
author = "Alberto Bartoli and Giorgio Davanzo and Andrea {De
Lorenzo} and Eric Medvet",
title = "{GP}-based Electricity Price Forecasting",
booktitle = "Proceedings of the 14th European Conference on Genetic
Programming, EuroGP 2011",
year = "2011",
month = "27-29 " # apr,
editor = "Sara Silva and James A. Foster and Miguel Nicolau and
Mario Giacobini and Penousal Machado",
series = "LNCS",
volume = "6621",
publisher = "Springer Verlag",
address = "Turin, Italy",
pages = "37--48",
organisation = "EvoStar",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-20406-7",
doi = "doi:10.1007/978-3-642-20407-4_4",
abstract = "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.",
notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in
conjunction with EvoCOP2011 EvoBIO2011 and
EvoApplications2011",
}
@InProceedings{DBLP:conf/rsctc/BartonV08,
author = "Alan J. Barton and Julio J. Valdes",
title = "Computational Intelligence Techniques Applied to
Magnetic Resonance Spectroscopy Data of Human Brain
Cancers",
booktitle = "Proceedings of the 6th International Conference on
Rough Sets and Current Trends in Computing, RSCTC
2008",
editor = "Chien-Chung Chan and Jerzy W. Grzymala-Busse and
Wojciech Ziarko",
series = "Lecture Notes in Computer Science",
volume = "5306",
year = "2008",
pages = "485--494",
address = "Akron, OH, USA",
month = oct # " 23-25",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-540-88423-1",
doi = "doi:10.1007/978-3-540-88425-5_50",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
}
@InProceedings{Barton:2009:IJCNN,
author = "Alan J. Barton and Julio J. Valdes and Robert
Orchard",
title = "Learning the neuron functions within a neural network
via Genetic Programming: Applications to geophysics and
hydrogeology",
booktitle = "International Joint Conference on Neural Networks,
IJCNN 2009",
year = "2009",
pages = "264--271",
address = "Atlanta, Georgia, USA",
month = jun # " 14-19",
keywords = "genetic algorithms, genetic programming, gene
expression programming, geophysics, geophysics
computing, hydrology, neural nets, geophysics,
hydrogeology, neural network classifier, neural network
neurons, neuron functions",
doi = "doi:10.1109/IJCNN.2009.5178731",
size = "8 pages",
abstract = "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).",
notes = "
one class membership. ANN variable with 0 mean 1
standard deviation.
Also known as \cite{5178731} See \cite{Barton2009614}",
}
@Article{Barton2009614,
author = "Alan J. Barton and Julio J. Valdes and Robert
Orchard",
title = "Neural networks with multiple general neuron models:
{A} hybrid computational intelligence approach using
Genetic Programming",
journal = "Neural Networks",
volume = "22",
number = "5-6",
pages = "614--622",
year = "2009",
note = "Advances in Neural Networks Research: IJCNN2009, 2009
International Joint Conference on Neural Networks",
editor = "S. Bressler and R. Kozma and L. Perlovsky and
Venayagamoorthy",
keywords = "genetic algorithms, genetic programming, General
neuron model, Evolutionary Computation, Hybrid
algorithm, Machine learning, Parameter space,
Visualization",
ISSN = "0893-6080",
doi = "doi:10.1016/j.neunet.2009.06.043",
URL = "http://www.sciencedirect.com/science/article/B6T08-4WNRK15-3/2/d8803b07859caa7efcd99475af7005ae",
abstract = "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.",
}
@InProceedings{Barton:2010:ieeeCIBCB,
author = "Alan J. Barton",
title = "Searching for a single mathematical function to
address the nonlinear retention time shifts problem in
nano{LC}-{MS} data: {A} fuzzy-evolutionary
computational proteomics approach",
booktitle = "2010 IEEE Symposium on Computational Intelligence in
Bioinformatics and Computational Biology (CIBCB)",
year = "2010",
month = may,
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/CIBCB.2010.5510688",
notes = "Also known as \cite{5510688}",
}
@InCollection{Bartram:2008:ECP,
author = "Derek Bartram and Michael Burrow and Xin Yao",
title = "A Computational Intelligence Approach to Railway Track
Intervention Planning",
booktitle = "Evolutionary Computation in Practice",
publisher = "Springer",
year = "2008",
editor = "Tina Yu and David Davis and Cem Baydar and Rajkumar
Roy",
volume = "88",
series = "Studies in Computational Intelligence",
chapter = "8",
pages = "163--198",
keywords = "genetic algorithms, genetic programming, k-means,
RPCL, learning",
isbn13 = "978-3-540-75770-2",
notes = "Part of \cite{TinaYu:2008:book}
Scheduling, railroad, maintenance, planning. Missing
data. Missing values.
p181 function set + - * / sin cos tan power",
}
@InProceedings{basanta03,
author = "David Basanta and Mark A. Miodownik and Elizabeth A.
Holm",
title = "Evolving Cellular Automata to Grow Microstructures",
booktitle = "Genetic Programming, Proceedings of EuroGP'2003",
year = "2003",
editor = "Conor Ryan and Terence Soule and Maarten Keijzer and
Edward Tsang and Riccardo Poli and Ernesto Costa",
volume = "2610",
series = "LNCS",
pages = "1--10",
address = "Essex",
publisher_address = "Berlin",
month = "14-16 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-00971-X",
URL = "http://rcswww.urz.tu-dresden.de/~basanta/eurogp03.pdf",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=1",
doi = "doi:10.1007/3-540-36599-0_1",
abstract = "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.",
notes = "EuroGP'2003 held in conjunction with EvoWorkshops
2003",
}
@InProceedings{Basanta:2004:EH,
author = "David Basanta and Mark A. Miodownik and Peter J.
Bentley and Elizabeth A. Holm",
title = "Evolving and Growing Microstructures of Materials
using Biologically Inspired {CA}",
booktitle = "2004 NASA/DoD Conference on Evolvable Hardware",
year = "2004",
publisher = "IEEE Computer Society",
editor = "RS Zebulum",
pages = "275--275",
address = "Seattle, Washington, USA",
month = jun # " 24-26",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7695-2145-2",
doi = "doi:10.1109/EH.2004.1310841",
abstract = "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.",
}
@Article{Bastian:2000:FSS,
author = "Andreas Bastian",
title = "Identifying fuzzy models utilizing genetic
programming",
journal = "Fuzzy Sets and Systems",
volume = "113",
pages = "333--350",
year = "2000",
number = "3",
month = "1 " # aug,
keywords = "genetic algorithms, genetic programming, System
identification, Fuzzy modeling",
URL = "http://www.sciencedirect.com/science/article/B6V05-4234BFC-1/1/261a04fa056f3f0dfe0fb79a773a971a",
abstract = "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.",
}
@Article{Batenkov:2010:HIG:1836543.1836558,
author = "Dmitry Batenkov",
title = "Hands-on introduction to genetic programming",
journal = "XRDS Crossroads",
year = "2010",
volume = "17",
number = "1",
pages = "46--51",
month = sep # " 2010",
note = "The ACM Magazine for Students",
keywords = "genetic algorithms, genetic programming, Coding Tools
and Techniques, Expressions and Their Representation,
Object-oriented Programming, Problem Solving, Control
Methods, Search",
ISSN = "1528-4972",
acmid = "1836558",
publisher = "ACM",
URL = "http://xrds.acm.org/article.cfm?aid=1836558",
doi = "doi:10.1145/1836543.1836558",
size = "2.5 pages",
abstract = "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.",
notes = "http://xrds.acm.org/ Christian Gagne's Open Beagle",
}
@Article{Batenkov:2011:GPEM,
author = "Dmitry Batenkov",
title = "Open {BEAGLE}: a generic framework for evolutionary
computations",
journal = "Genetic Programming and Evolvable Machines",
year = "2011",
volume = "12",
number = "3",
pages = "329--331",
month = sep,
note = "Software review",
keywords = "genetic algorithms, genetic programming",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-011-9135-4",
publisher = "Springer",
size = "3 pages",
affiliation = "Weizmann Institute of Science, Rehovot, Israel",
}
@InProceedings{Bates:2003:ICCIFE,
author = "R. G. Bates and M. A. H. Dempster and Y. S. Romahi",
title = "Evolutionary reinforcement learning in {FX} order book
and order flow analysis",
booktitle = "IEEE International Conference on Computational
Intelligence for Financial Engineering",
year = "2003",
pages = "355--362",
address = "Hong Kong",
month = "20-23 " # mar,
keywords = "genetic algorithms, genetic programming",
URL = "http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/2003/WP06.pdf",
doi = "doi:10.1109/CIFER.2003.1196282",
abstract = "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.",
notes = "Final report to HSBC Investment Bank, November (2002).
Location: Technical report WP06/2003
",
}
@InProceedings{battle:1999:GPFKBFLC,
author = "Daryl Battle and Abdollah Homaifar and Edward Tunstel
and Gerry Dozier",
title = "Genetic Programming of Full Knowledge Bases for Fuzzy
Logic Controllers",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1463--1468",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, real world
applications",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-730.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-730.ps",
abstract = "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.",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InCollection{bauer:1995:EEAGPACSS,
author = "Eric T. Bauer",
title = "Evolving Efficient Algorithms by Genetic Programming:
{A} Case Study in Sorting",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1995",
year = "1995",
editor = "John R. Koza",
pages = "1--10",
address = "Stanford, California, 94305-3079 USA",
month = "11 " # dec,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-195720-5",
notes = "part of \cite{koza:1995:gagp}",
}
@InProceedings{Bauer:2008:TREC,
author = "Johannes M. Bauer and Kurt DeMaagd",
title = "Network Management Practices and Sector Performance -
{A} Genetic Programming Approach",
booktitle = "36th Research Conference on Communications,
Information, and Internet Policy.",
year = "2008",
editor = "Elizabeth Mateja",
address = "Arlington, VA, USA",
month = sep # " 27",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.tprcweb.com/images/stories/2008/Bauer-DeMaagd-Network-Management-2008-TPRC-fin.pdf",
size = "27 pages",
abstract = "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.",
notes = "http://www.tprcweb.com/index.php?option=com_content&view=article&id=29&Itemid=18",
}
@InProceedings{baum:1998:tceae,
author = "Eric B. Baum and Igor Durdanovic",
title = "Toward Code Evolution By Artificial Economies",
booktitle = "Late Breaking Papers at the Genetic Programming 1998
Conference",
year = "1998",
editor = "John R. Koza",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "Stanford, CA, USA",
month = "22-25 " # jul,
publisher = "Stanford University Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "GP-98LB. See also \cite{baum:1998:tceaeTR}",
}
@TechReport{baum:1998:tceaeTR,
author = "Eric B. Baum and Igor Durdanovic",
title = "Toward Code Evolution By Artificial Economies",
institution = "NEC Research Institute",
year = "1998",
address = "4 Independence Way, Princeto, NJ 08540, USA",
month = "10 " # jul,
keywords = "genetic algorithms, genetic programming",
notes = "Hayek2 blocks world {"}crossover is much better than
headless chicken mutation{"} meta-agents, inherited
wealth, rent, intellectual property, strong typing
STGP. See also (\cite{baum:1998:tceae},
\cite{oai:CiteSeerPSU:5199}",
size = "53 pages",
}
@InProceedings{oai:CiteSeerPSU:5199,
author = "Eric Baum and Igor Durdanovic",
title = "Toward Code Evolution By Artificial Economies
(Extended Abstract)",
booktitle = "Evolution as Computation, DIMACS Workshop, Princeton,
January 1999",
year = "2001",
editor = "Laura F. Landweber and Erik Winfree",
series = "Natural Computing Series",
address = "Princeton University",
month = "11-12 " # jan,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-66709-1",
URL = "http://citeseer.ist.psu.edu/5199.html",
URL = "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",
size = "16 pages",
abstract = "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.",
notes = "see also \cite{baum:1998:tceaeTR},
http://dimacs.rutgers.edu/Workshops/Evolution/
Published Jan 2001
http://www.amazon.com/exec/obidos/ASIN/3540667091/dominantsystems/107-7663466-9560554",
}
@Article{Baumes2008,
author = "Laurent A. Baumes and Pierre Collet",
title = "Examination of genetic programming paradigm for
high-throughput experimentation and heterogeneous
catalysis",
journal = "Computational Materials Science",
year = "2009",
volume = "45",
number = "1",
pages = "27--40",
month = mar,
keywords = "genetic algorithms, genetic programming, Heterogeneous
catalysis, High-throughput, Materials, Combinatorial,
Representation, Data structure",
ISSN = "0927-0256",
doi = "doi:10.1016/j.commatsci.2008.03.051",
URL = "http://www.sciencedirect.com/science/article/B6TWM-4T4J19Y-1/2/809324138cc0b8f49634eae7f22e995f",
size = "14 pages",
abstract = "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.",
notes = "Selected papers from the E-MRS 2007 Fall Meeting
Symposium G: Genetic Algorithms in Materials Science
and Engineering - GAMS-2007",
}
@Article{BBSTLCC09,
author = "L. A. Baumes and A. Blansche and P. Serna and A.
Tchougang and N. Lachiche and P. Collet and A. Corma",
title = "Using Genetic Programming for an Advanced Performance
Assessment of Industrially Relevant Heterogeneous
Catalysts",
journal = "Materials and Manufacturing Processes",
year = "2009",
volume = "24",
number = "3",
pages = "282--292",
month = mar,
keywords = "genetic algorithms, genetic programming, Data mining,
Heterogeneous catalysis, High-throughput, Materials
science",
ISSN = "1042-6914",
publisher = "Taylor and Francis",
URL = "http://lsiit.u-strasbg.fr/Publications/2009/BBSTLCC09",
doi = "doi:10.1080/10426910802679196",
size = "11 pages",
abstract = "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.",
notes = "Affiliations: Institute of Chemical Technology,
CSIC-UPV, Valencia, Spain
Louis Pasteur University, LSIIT, FDBT, Illkirch,
France",
}
@Article{Bautu20071q,
author = "Andrei Bautu and Elena Bautu",
title = "Quantum Circuit Design By Means Of Genetic
Programming",
journal = "Romanian Journal of Physics",
year = "2007",
volume = "52",
number = "5-7",
pages = "697--704",
publisher = "Romanian Academy Publishing House",
address = "Bucharest, Romania",
keywords = "genetic algorithms, genetic programming, quantum
gates",
ISSN = "1221-146X",
URL = "http://www.nipne.ro/rjp/2007_52_5-6/0697_0705.pdf",
size = "8 pages",
abstract = "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.",
notes = "S-expressions. Paper presented at the 7th
International Balkan Workshop on Applied Physics, 5-7
July 2006, Constanta,
Romania.
http://www.nipne.ro/rjp/",
}
@InProceedings{conf:synasc:bautu2005,
author = "Elena Bautu and Andrei Bautu and Henri Luchian",
title = "A {GEP}-based approach for solving {Fredholm} first
kind integral equations",
booktitle = "Seventh International Symposium on Symbolic and
Numeric Algorithms for Scientific Computing, SYNASC
2005",
year = "2005",
pages = "325",
month = sep,
publisher = "IEEE Computer Society",
keywords = "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",
ISBN = "0-7695-2453-2",
doi = "doi:10.1109/SYNASC.2005.6",
size = "4 pages",
abstract = "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.",
}
@InProceedings{Bautu:2005:SYNASC,
author = "Elena Bautu and Andrei Bautu and Henri Luchian",
title = "Symbolic Regression on Noisy Data with Genetic and
Gene Expression Programming",
booktitle = "Seventh International Symposium on Symbolic and
Numeric Algorithms for Scientific Computing
(SYNASC'05)",
year = "2005",
pages = "321--324",
keywords = "genetic algorithms, genetic programming, Gene
Expression Programming",
doi = "doi:10.1109/SYNASC.2005.70",
abstract = "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.",
}
@InProceedings{conf/synasc/BautuBL07,
author = "Elena Bautu and Andrei Bautu and Henri Luchian",
title = "Ada{GEP} - An Adaptive Gene Expression Programming
Algorithm",
booktitle = "Proceedings of the Ninth International Symposium on
Symbolic and Numeric Algorithms for Scientific
Computing, {SYNASC} 2007",
year = "2007",
editor = "Viorel Negru and Tudor Jebelean and Dana Petcu and
Daniela Zaharie",
pages = "403--406",
address = "Timisoara, Romania",
month = sep # " 26-29",
publisher = "IEEE Computer Society",
keywords = "genetic algorithms, genetic programming, gene
expression programming",
isbn13 = "978-0-7695-3078-9",
doi = "doi:10.1109/SYNASC.2007.51",
abstract = "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.",
notes = "p406 'The results presented in this paper demonstrate
the superiority of AdaGEP over GEP on symbolic
regression problems.'",
bibdate = "2008-11-28",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/synasc/synasc2007.html#BautuBL07",
}
@Article{Bautu20071,
author = "Elena Bautu and Elena Pelican",
title = "Numerical Solution For {Fredholm} First Kind Integral
Equations Occurring In Synthesis of Electromagnetic
Fields",
journal = "Romanian Journal of Physics",
year = "2007",
volume = "52",
pages = "245--256",
number = "3-4",
publisher = "Romanian Academy Publishing House",
keywords = "genetic algorithms, genetic programming, Gene
Expression Programming, Fredholm integral equations of
the first kind, inverse problems",
ISSN = "1221-146X",
URL = "http://www.nipne.ro/rjp/2007_52_3-4/0245_0257.pdf",
size = "12 pages",
abstract = "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.",
notes = "Paper presented at the 7th International Balkan
Workshop on Applied Physics, 5-7 July 2006, Constanta,
Romania.
http://www.nipne.ro/rjp/",
}
@InProceedings{Bautu:2008:SYNASC,
author = "Elena Bautu and Andrei Bautu and Henri Luchian",
title = "An Evolutionary Approach for Modeling Time Series",
booktitle = "10th International Symposium on Symbolic and Numeric
Algorithms for Scientific Computing, SYNASC '08",
year = "2008",
month = sep,
pages = "507--513",
keywords = "genetic algorithms, genetic programming, change point
detection, data generation process, evolutionary
approach, genetic operator, time series modeling, time
series",
doi = "doi:10.1109/SYNASC.2008.63",
abstract = "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.",
notes = "Also known as \cite{5204862}",
}
@Article{Bautu20081,
author = "Elena Bautu and Elena Pelican",
title = "Symbolic approach for the generalized airfoil
equation",
journal = "Creative Mathematics and Informatics",
year = "2008",
volume = "17",
number = "2",
pages = "52--60",
keywords = "genetic algorithms, genetic programming, Gene
Expression Programming, Generalised airfoil equation,
Fredholm integral equation of the first kind, airfoil
equation",
ISSN = "1584-286X",
URL = "http://creative-mathematics.ubm.ro/issues/down.php?f=creative_17_2008_no2_052_060.pdf",
abstract = "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.",
notes = "http://creative-mathematics.ubm.ro/",
}
@InProceedings{conf/cisis/BautuBL10,
author = "Elena Bautu and Andrei Bautu and Henri Luchian",
title = "Evolving Gene Expression Programming Classifiers for
Ensemble Prediction of Movements on the Stock Market",
booktitle = "The Fourth International Conference on Complex,
Intelligent and Software Intensive Systems (CISIS
2010)",
address = "Krakow, Poland",
month = "15-18 " # feb,
year = "2010",
editor = "Leonard Barolli and Fatos Xhafa and Salvatore Vitabile
and Hui-Huang Hsu",
isbn13 = "978-0-7695-3967-6",
pages = "108--115",
keywords = "genetic algorithms, genetic programming, gene
expression programming",
doi = "doi:10.1109/CISIS.2010.101",
publisher = "IEEE Computer Society",
bibdate = "2010-04-23",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/cisis/cisis2010.html#BautuBL10",
}
@PhdThesis{bautu:thesis,
author = "Elena Bautu",
title = "Intelligent Techniques for Data Modeling Problems",
school = "Al. I. Cuza University",
year = "2010",
address = "Iasi, Romania",
month = jun,
note = "Romanian subtitle is Programare genetica pentru
probleme de optimizare in Inteligenta artificiala",
keywords = "genetic algorithms, genetic programming, gene
expression programming, inverse problems, financial
forecasting, data analysis, hypernetwork,
hybridization",
URL = "https://sites.google.com/site/ebautu/home/publications/thesis/thesis_elena_bautu.pdf",
URL = "https://sites.google.com/site/ebautu/home/publications/thesis",
size = "220 pages",
abstract = "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",
}
@Article{Bautu2012,
author = "Elena Bautu and Alina Barbulescu",
title = "A Hybrid Approach for Modelling Financial Time
Series",
year = "2012",
journal = "The International Arab Journal of Information
Technology (IAJIT)",
volume = "9",
issue = "3/July 2012",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{Baydar:2000:GECCO,
author = "Cem M. Baydar and Kazuhiro Saitou",
title = "A Genetic Programming Framework for Error Recovery in
Robotic Assembly Systems",
pages = "756",
year = "2000",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2000)",
editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz
and Lee Spector and Ian Parmee and Hans-Georg Beyer",
address = "Las Vegas, Nevada, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "10-12 " # jul,
keywords = "genetic algorithms, genetic programming, Poster",
ISBN = "1-55860-708-0",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/RW036.pdf",
notes = "A joint meeting of the ninth International Conference
on Genetic Algorithms (ICGA-2000) and the fifth Annual
Genetic Programming Conference (GP-2000) Part of
\cite{whitley:2000:GECCO}",
}
@InProceedings{oai:CiteSeerPSU:538284,
author = "Cem M. Baydar and Kazuhiro Saitou",
title = "Off-Line Error Recovery Logic Synthesis in Automated
Assembly Lines by using Genetic Programming",
booktitle = "Proceedings Of The 2000 Japan/USA Symposium On
Flexible Automation",
year = "2000",
editor = "Steven Y. Liang and Tatsuo Arai",
address = "Ann Arbor, MI, USA",
month = "23-26 " # jul,
organisation = "ASME",
email = "kazu@umich.edu",
keywords = "genetic algorithms, genetic programming, Error
Recovery Synthesis, Off-line Programming, Automated
Assembly Lines",
ISBN = "0-7918-1998-1",
broken = "http://www-personal.engin.umich.edu/~cbaydar/japan-usa-00.pdf",
URL = "http://citeseer.ist.psu.edu/538284.html",
size = "8 pages",
abstract = "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.",
notes = "http://www.asme.org/divisions/med/enewsletter/2000oct/JapanUSAsymp.html
http://members.asme.org/catalog/ItemView.cfm?ItemNumber=I464CD
ASME Order #: I464CD",
}
@InProceedings{oai:CiteSeerPSU:535775,
author = "Cem M Baydar and Kazuhiro Saitou",
title = "Generation of Robust Recovery Logic in Assembly
Systems using Multi-Level Optimization and Genetic
Programming",
booktitle = "Proceedings of DETC-00 ASME 2000 Design Engineering
Technical Conferences and Computers and Information in
Engineering Conference",
year = "2000",
address = "Baltimore, Maryland, USA",
month = "10-13 " # sep,
keywords = "genetic algorithms, genetic programming",
citeseer-isreferencedby = "oai:CiteSeerPSU:87724;
oai:CiteSeerPSU:467824; oai:CiteSeerPSU:161643",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:535775",
rights = "unrestricted",
URL = "http://citeseer.ist.psu.edu/535775.html",
size = "8 pages",
abstract = "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.",
notes = "not verified",
}
@InProceedings{Baydar:2001:ICRA,
author = "Cem M. Baydar and Kazuhiro Saitou",
title = "Off-line error prediction, diagnosis and recovery
using virtual assembly systems",
booktitle = "Proceedings of the IEEE International Conference on
Robotics and Automation, ICRA 2001",
year = "2001",
volume = "1",
pages = "818--823",
address = "Seoul, Korea",
month = "21-26 " # may,
publisher = "IEEE",
keywords = "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",
ISSN = "1050-4729",
ISBN = "0-7803-6576-3",
doi = "doi:10.1109/ROBOT.2001.932651",
size = "6 pages",
abstract = "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.",
notes = "GP creates code in RAPID language. Also known as
\cite{932651}",
}
@PhdThesis{Baydar:thesis,
author = "Cem M. Baydar",
title = "Off-line Error Prediction and Recovery Logic Synthesis
using Virtual Assembly Systems",
school = "The University of Michigan",
year = "2001",
keywords = "genetic algorithms, genetic programming",
size = "pages",
notes = "Chair: K. Saitou
http://me.engin.umich.edu/news/pubs/ar/200209annualreportbw.pdf",
}
@Article{Baydar200155,
author = "Cem M. Baydar and Kazuhiro Saitou",
title = "Automated generation of robust error recovery logic in
assembly systems using genetic programming",
journal = "Journal of Manufacturing Systems",
volume = "20",
number = "1",
pages = "55--68",
year = "2001",
ISSN = "0278-6125",
doi = "doi:10.1016/S0278-6125(01)80020-0",
URL = "http://www.sciencedirect.com/science/article/B6VJD-441R1H8-6/2/cdebaddb30a67a67dc7cb6dd41fabf9f",
keywords = "genetic algorithms, genetic programming, robotics,
Automated Assembly Systems, Error Recovery, Multi-Level
Optimization",
abstract = "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.",
notes = "IRB6000 KAREL2, ROUTINE GPcode26, Move to POS, Move
Relative...",
}
@Article{Baydar:2004:JIM,
author = "Cem Baydar and Kazuhiro Saitou",
title = "Off-line error prediction, diagnosis and recovery
using virtual assembly systems",
journal = "Journal of Intelligent Manufacturing",
year = "2004",
volume = "15",
number = "5",
pages = "679--692",
month = oct,
keywords = "genetic algorithms, genetic programming, Off-line
programming, robotic assembly systems, virtual
factories, error diagnosis and recovery",
ISSN = "0956-5515",
publisher = "Springer",
doi = "doi:10.1023/B:JIMS.0000037716.69868.d0",
size = "14 pages",
abstract = "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.",
notes = "GP section 3.3. They generate error recovery code
p688. linear chromosome Fig 4. Workspace Software.
Pictures much better than \cite{Baydar:2001:ICRA}",
}
@Article{Baykasoglu:2004:CCR,
author = "Adil Baykasoglu and Turkay Dereli and Serkan Tanis",
title = "Prediction of cement strength using soft computing
techniques",
journal = "Cement and Concrete Research",
year = "2004",
volume = "34",
pages = "2083--2090",
number = "11",
abstract = "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.",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6TWG-4CBVDJS-1/2/46a55d4141904806cf09f3c92f56beb4",
month = nov,
keywords = "genetic algorithms, genetic programming, Gene
expression programming, Modelling, Compressive
strength, Cement manufacture",
doi = "doi:10.1016/j.cemconres.2004.03.028",
notes = "
",
}
@InProceedings{Baykasoglu:2005:ICRM,
author = "Adil Baykasoglu",
title = "Soft computing approaches to production line design",
booktitle = "ICRM'2005 3rd International Conference on Responsive
Manufacturing",
year = "2005",
editor = "Nabil Gindy",
pages = "273--279",
address = "Guangzhou, China",
month = "12-14 " # sep,
organisation = "University of Nottingham, Guangdong University of
Technology",
keywords = "genetic algorithms, genetic programming, Gene
Expression Programming, Manufacturing system design,
soft computing",
abstract = "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.",
notes = "http://www.icrm2005.org/ broken Nov 2005
",
}
@Article{Baykasoglu2007767,
author = "Adil Baykasoglu and Lale Ozbakir",
title = "{MEPAR}-miner: Multi-expression programming for
classification rule mining",
journal = "European Journal of Operational Research",
volume = "183",
number = "2",
pages = "767--784",
year = "2007",
ISSN = "0377-2217",
doi = "DOI:10.1016/j.ejor.2006.10.015",
URL = "http://www.sciencedirect.com/science/article/B6VCT-4MJS038-M/2/f780e675b2900eb28473dcbf6cfa03fb",
keywords = "genetic algorithms, genetic programming, Data mining,
Classification rules, Multi-expression programming,
Evolutionary programming",
abstract = "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.",
}
@Article{Baykasoglu2008111,
author = "Adil Baykasoglu and Hamza Gullu and Hanifi Canakci and
Lale Ozbakir",
title = "Prediction of compressive and tensile strength of
limestone via genetic programming",
journal = "Expert Systems with Applications",
volume = "35",
number = "1-2",
pages = "111--123",
year = "2008",
ISSN = "0957-4174",
doi = "doi:10.1016/j.eswa.2007.06.006",
URL = "http://www.sciencedirect.com/science/article/B6V03-4NYJ0NK-1/2/00b6bf799aaf3df77a5e0fd846b85f20",
keywords = "genetic algorithms, genetic programming, multi
expression programming, gene expression programming,
Prediction, Limestone, Strength of materials",
abstract = "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.",
}
@Article{Baykasoglu2008,
author = "Adil Baykasoglu and Ahmet Oztas and Erdogan Ozbay",
title = "Prediction and multi-objective optimization of
high-strength concrete parameters via soft computing
approaches",
journal = "Expert Systems with Applications",
year = "2009",
volume = "36",
number = "3",
pages = "6145--6155",
month = apr,
keywords = "genetic algorithms, genetic programming, gene
expression programming, Multiple objective
optimization, Meta-heuristics, Prediction,
High-strength concrete",
ISSN = "0957-4174",
doi = "doi:10.1016/j.eswa.2008.07.017",
URL = "http://www.sciencedirect.com/science/article/B6V03-4T0WJSK-G/2/2dd2cbea4bb9a919e91f3953aecaaa06",
ISSN = "0957-4174",
size = "11 pages",
abstract = "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.",
}
@Article{Baykasoglu:2009:ESA,
author = "Adil Baykasoglu and Mustafa Gocken",
title = "Gene expression programming based due date assignment
in a simulated job shop",
journal = "Expert Systems with Applications",
year = "2009",
volume = "36",
pages = "12143--12150",
number = "10",
keywords = "genetic algorithms, genetic programming, Gene
expression programming, Due date assignment",
doi = "doi:10.1016/j.eswa.2009.03.061",
ISSN = "0957-4174",
URL = "http://www.sciencedirect.com/science/article/B6V03-4VY2C6B-1/2/d174ebf2e7f0566d9c964be7d6f4f2ab",
abstract = "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.",
}
@Article{Baykasoglu:2010:S,
title = "Genetic Programming Based Data Mining Approach to
Dispatching Rule Selection in a Simulated Job Shop",
author = "Adil Baykasoglu and Mustafa Gocken and Lale Ozbakir",
journal = "Simulation",
year = "2010",
number = "12",
volume = "86",
pages = "715--728",
keywords = "genetic algorithms, genetic programming, data mining,
dispatching rules",
doi = "doi:10.1177/0037549709346561",
size = "14 pages",
bibdate = "2011-02-04",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/simulation/simulation86.html#BaykasogluGO10",
abstract = "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.",
}
@Article{bayne:1997:ve,
author = "Michael D. Bayne",
title = "Vive l'evolution",
journal = "Deep Magic",
year = "1997",
month = "12 " # feb,
note = "www page",
keywords = "genetic algorithms, genetic programming, Java, www",
broken = "http://www.go2net.com/internet/deep/1997/02/12/",
URL = "http://samskivert.com/internet/deep/1997/02/12/",
size = "html page",
abstract = "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.",
notes = "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",
}
@Article{Baziar2011,
author = "Mohammad H. Baziar and Yaser Jafarian and Habib
Shahnazari and Vahid Movahed and Mohammad Amin
Tutunchian",
title = "Prediction of strain energy-based liquefaction
resistance of sand-silt mixtures: An evolutionary
approach",
journal = "Computer \& Geosciences",
volume = "37",
number = "11",
pages = "1883--1893",
year = "2011",
ISSN = "0098-3004",
doi = "doi:10.1016/j.cageo.2011.04.008",
URL = "http://www.sciencedirect.com/science/article/B6V7D-52R9DF5-2/2/08fa46566f649fc2348af34aa83ebbb2",
keywords = "genetic algorithms, genetic programming, Liquefaction,
Capacity energy, Sand, Silt, Wildlife",
abstract = "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.",
}
@InProceedings{Beadle:2008:CEC,
author = "Lawrence Beadle and Colin Johnson",
title = "Semantically Driven Crossover in Genetic Programming",
booktitle = "Proceedings of the IEEE World Congress on
Computational Intelligence",
year = "2008",
pages = "111--116",
editor = "Jun Wang",
address = "Hong Kong",
month = "1-6 " # jun,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, Program
Semantics, Crossover, Reduced Ordered Binary Decision
Diagrams",
isbn13 = "978-1-4244-1823-7",
file = "EC0044.pdf",
doi = "doi:10.1109/CEC.2008.4630784",
abstract = "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.",
notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
EPS and the IET.",
}
@Article{Beadle:2009:GPEM,
author = "Lawrence Beadle and Colin G. Johnson",
title = "Semantic Analysis of Program Initialisation in Genetic
Programming",
journal = "Genetic Programming and Evolvable Machines",
year = "2009",
volume = "10",
number = "3",
pages = "307--337",
month = sep,
keywords = "genetic algorithms, genetic programming, Program
initialisation, Program semantics, Program structure",
ISSN = "1389-2576",
URL = "http://www.springerlink.com/content/yn5p45723l6tr487",
doi = "doi:10.1007/s10710-009-9082-5",
abstract = "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.",
}
@InProceedings{Beadle:2009:cec,
author = "Lawrence Beadle and Colin G Johnson",
title = "Semantically Driven Mutation in Genetic Programming",
booktitle = "2009 IEEE Congress on Evolutionary Computation",
year = "2009",
editor = "Andy Tyrrell",
pages = "1336--1342",
address = "Trondheim, Norway",
month = "18-21 " # may,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-2959-2",
file = "P009.pdf",
doi = "doi:10.1109/CEC.2009.4983099",
abstract = "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.",
keywords = "genetic algorithms, genetic programming, Genetic
programming, program semantics, semantically driven
mutation, reduced ordered binary decision diagrams.",
notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and
the IET. IEEE Catalog Number: CFP09ICE-CDR",
}
@PhdThesis{Beadle:thesis,
author = "Lawrence Charles John Beadle",
title = "Semantic and Structural Analysis of Genetic
Programming",
school = "University of Kent",
year = "2009",
address = "Canterbury",
month = jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.beadle.me/Me/LBeadle_PhD_Thesis.pdf",
size = "194 pages",
abstract = "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.",
}
@InProceedings{beale:2002:RTIC,
author = "Stuart Beale",
title = "Traffic Data: Less is More",
booktitle = "Road Transport Information and Control",
year = "2002",
address = "Savoy Place, London, UK",
month = "19-21 " # mar,
organisation = "IEE",
email = "rtic2002@iee.org.uk",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1049/cp:20020233",
abstract = "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.",
notes = "RTIC 2002 http://conferences.iee.org.uk/RTIC/ For
{"}genetic algorithm{"} read {"}genetic
programming{"}",
}
@Article{ga:Beard93a,
author = "Nick Beard",
title = "The joy of genetic programming",
journal = "Personal Computer World",
year = "1993",
pages = "471--472",
month = jun,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/ga_beard93a.pdf",
size = "2 pages",
notes = "overview/introduction",
}
@InProceedings{Bearpark:2000:ACDM,
author = "K. Bearpark and A. J. Keane",
title = "Short term memory in genetic programming",
booktitle = "Fourth International Conference on Adaptive Computing
in Design and Manufacture, ACDM '00",
year = "2000",
editor = "I. C. Parmee",
pages = "309--320",
address = "London, UK",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
URL = "http://eprints.soton.ac.uk/21399/1/bear_00.pdf",
URL = "http://eprints.soton.ac.uk/21399/",
size = "12 pages",
abstract = "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",
notes = "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).",
}
@PhdThesis{Bearpark:thesis,
author = "Keith Bearpark",
title = "Learning and memory in genetic programming",
school = "School of Engineering Sciences, University of
Southampton",
year = "2000",
keywords = "genetic algorithms, genetic programming",
URL = "http://eprints.soton.ac.uk/45930/",
abstract = "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.",
}
@InProceedings{beaulieu:2002:gecco,
author = "Julie Beaulieu and Christian Gagn{\'e} and Marc
Parizeau",
title = "Lens System Design And Re-engineering With
Evolutionary Algorithms",
booktitle = "GECCO 2002: Proceedings of the Genetic and
Evolutionary Computation Conference",
editor = "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",
year = "2002",
pages = "155--162",
address = "New York",
publisher_address = "San Francisco, CA 94104, USA",
month = "9-13 " # jul,
publisher = "Morgan Kaufmann Publishers",
keywords = "genetic algorithms, genetic programming, evolvable
hardware, evolutionary reengineering, evolvable optics,
genetic algorithms, lens system design",
URL = "http://vision.gel.ulaval.ca/~parizeau/Publications/gecco02-lens.pdf",
URL = "http://vision.gel.ulaval.ca/en/publications/Id_44/PublDetails.php",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/EH274.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-04.pdf",
URL = "http://www.gel.ulaval.ca/~cgagne/pubs/lens-gecco02.pdf",
URL = "http://citeseer.ist.psu.edu/532763.html",
size = "8 pages",
abstract = "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.",
ISBN = "1-55860-878-8",
notes = "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",
}
@InProceedings{Beaumont:2009:cec,
author = "Darren Beaumont and Susan Stepney",
title = "Grammatical Evolution of {L}-systems",
booktitle = "2009 IEEE Congress on Evolutionary Computation",
year = "2009",
editor = "Andy Tyrrell",
pages = "-",
address = "Trondheim, Norway",
month = "18-21 " # may,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-2959-2",
file = "P007.pdf",
abstract = "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.",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and
the IET. IEEE Catalog Number: CFP09ICE-CDR",
}
@InProceedings{Bechmann:2010:ICES,
author = "Matthias Bechmann and Angelika Sebald and Susan
Stepney",
title = "From Binary to Continuous Gates - and Back Again",
booktitle = "Proceedings of the 9th International Conference
Evolvable Systems: From Biology to Hardware, ICES
2010",
year = "2010",
editor = "Gianluca Tempesti and Andy M. Tyrrell and Julian F.
Miller",
series = "Lecture Notes in Computer Science",
volume = "6274",
pages = "335--347",
address = "York",
month = sep # " 6-8",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, cartesian
genetic programming",
isbn13 = "978-3-642-15322-8",
doi = "doi:10.1007/978-3-642-15323-5_29",
abstract = "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.",
affiliation = "Department of Chemistry, University of York, YO10 5DD
UK",
}
@InProceedings{beck:1999:EAM,
author = "M. A. Beck and I. C. Parmee",
title = "Extending the bounds of the search space: {A}
Multi-Population approach",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1469--1476",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "real world applications",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-762.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-762.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@Misc{oai:arXiv.org:cs/0212019,
title = "Thinking, Learning, and Autonomous Problem Solving",
author = "Joerg D. Becker",
year = "2002",
month = dec # "~10",
abstract = "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.",
note = "Comment: 9 pages, 4 figures",
oai = "oai:arXiv.org:cs/0212019",
URL = "http://arXiv.org/abs/cs/0212019",
size = "27702 bytes",
}
@TechReport{becker:2003-09,
author = "Lee A. Becker and Mukund Seshadri",
title = "Comprehensibility and Overfitting Avoidance in Genetic
Programming for Technical Trading Rules",
institution = "Worcester Polytechnic Institute",
year = "2003",
month = may,
email = "mukund@cs.wpi.edu",
keywords = "genetic algorithms, genetic programming,
comprehensibility , Occam's razor, overfitting,
complexity penalising, S&P500, technical analysis,
market timing",
URL = "ftp://ftp.cs.wpi.edu/pub/techreports/pdf/03-09.pdf",
URL = "http://citeseer.ist.psu.edu/574013.html",
abstract = "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.",
}
@TechReport{becker:2003-15,
author = "Lee A. Becker and Mukund Seshadri",
title = "Cooperative Coevolution of Technical Trading Rules",
institution = "Worcester Polytechnic Institute",
year = "2003",
month = may,
email = "mukund@cs.wpi.edu",
keywords = "genetic algorithms, genetic programming",
URL = "ftp://ftp.cs.wpi.edu/pub/techreports/pdf/03-15.pdf",
abstract = "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.",
}
@InProceedings{becker:2003:CINC,
author = "Lee A. Becker and Mukund Seshadri",
title = "{GP}-evolved Technical Trading Rules Can Outperform
Buy and Hold",
booktitle = "Procceedings of the Sixth International Conference on
Computational Intelligence and Natural Computing",
year = "2003",
address = "Embassy Suites Hotel and Conference Center, Cary,
North Carolina USA",
month = sep # " 26-30",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/staff/W.Yan/gp-evolved-technical-trading.pdf",
size = "4 pages",
abstract = "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.",
notes = "http://axon.cs.byu.edu/CINC/
http://www.ee.duke.edu/JCIS/
Worcester Polytechnic Institute",
}
@InCollection{Becker:2006:GPTP,
author = "Ying Becker and Peng Fei and Anna M. Lester",
title = "Stock Selection : An Innovative Application of Genetic
Programming Methodology",
booktitle = "Genetic Programming Theory and Practice {IV}",
year = "2006",
editor = "Rick L. Riolo and Terence Soule and Bill Worzel",
volume = "5",
series = "Genetic and Evolutionary Computation",
chapter = "12",
pages = "315--334",
address = "Ann Arbor",
month = "11-13 " # may,
publisher = "Springer",
keywords = "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",
ISBN = "0-387-33375-4",
size = "16 pages",
abstract = "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.",
notes = "part of \cite{Riolo: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;",
}
@InCollection{Becker:2007:GPTP,
author = "Ying L. Becker and Harold Fox and Peng Fei",
title = "An Empirical Study of Multi-Objective Algorithms for
Stock Ranking",
booktitle = "Genetic Programming Theory and Practice {V}",
year = "2007",
editor = "Rick L. Riolo and Terence Soule and Bill Worzel",
series = "Genetic and Evolutionary Computation",
chapter = "14",
pages = "239--259",
address = "Ann Arbor",
month = "17-19" # may,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-0-387-76308-8",
doi = "doi:10.1007/978-0-387-76308-8_14",
size = "21 pages",
abstract = "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.",
affiliation = "Advanced Research Center, State Street Global Advisors
Boston MA 02111",
notes = "part of \cite{Riolo:2007:GPTP} published Jan 2008",
}
@InProceedings{BeckerO:2009:GEC,
author = "Ying L. Becker and Una-May O'Reilly",
title = "Genetic programming for quantitative stock selection",
booktitle = "GEC '09: Proceedings of the first ACM/SIGEVO Summit on
Genetic and Evolutionary Computation",
year = "2009",
editor = "Lihong Xu and Erik D. Goodman and Guoliang Chen and
Darrell Whitley and Yongsheng Ding",
bibsource = "DBLP, http://dblp.uni-trier.de",
pages = "9--16",
address = "Shanghai, China",
organisation = "SigEvo",
doi = "doi:10.1145/1543834.1543837",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = jun # " 12-14",
isbn13 = "978-1-60558-326-6",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "Also known as \cite{DBLP:conf/gecco/BeckerO09} part of
\cite{DBLP:conf/gec/2009}",
}
@InCollection{Bedner:1997:elca,
author = "Ilja Bedner",
title = "Evolving Light Cycle Algorithms",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1997",
publisher = "Stanford Bookstore",
year = "1997",
editor = "John R. Koza",
address = "Stanford, California, 94305-3079 USA",
month = "17 " # mar,
keywords = "genetic algorithms, genetic programming, games",
ISBN = "0-18-205981-2",
abstract = "Evolution of autonomous agents that must compete for
survival in the light-cycle game as seen in the movie
tron",
notes = "part of \cite{koza:1997:GAGPs}",
}
@InProceedings{Beham:2008:ieeeIPDPS,
author = "Andreas Beham and Stephan Winkler and Stefan Wagner
and Michael Affenzeller",
title = "A genetic programming approach to solve scheduling
problems with parallel simulation",
booktitle = "IEEE International Symposium on Parallel and
Distributed Processing, IPDPS 2008",
year = "2008",
month = apr,
pages = "1--5",
keywords = "genetic algorithms, genetic programming, dispatching,
fitness evaluation, parallel simulation, production
planning, scheduling problem, dispatching, production
planning, scheduling",
doi = "doi:10.1109/IPDPS.2008.4536379",
doi = "doi:10.1109/IPDPS.2008.4536362",
ISSN = "1530-2075",
abstract = "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.",
notes = "Also known as \cite{4536379} \cite{4536362}",
}
@Article{Behbahani:2012:transMechtron,
author = "Saeed Behbahani and Clarence W. {de Silva}",
title = "Mechatronic Design Evolution Using Bond Graphs and
Hybrid Genetic Algorithm With Genetic Programming",
journal = "IEEE/ASME Transactions on Mechatronics",
note = "Early Access Article",
keywords = "genetic algorithms, genetic programming, Bond graphs,
electrohydraulic systems",
ISSN = "1083-4435",
doi = "doi:10.1109/TMECH.2011.2165958",
size = "10 pages",
abstract = "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.",
notes = "Also known as \cite{6029337}",
}
@InCollection{beheler:1995:UGACFOSGPI,
author = "Joey Beheler",
title = "Using Genetic Algorithms and Convolution to Find
Optimal Strategies in Games without Perfect
Information",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1995",
year = "1995",
editor = "John R. Koza",
pages = "11--18",
address = "Stanford, California, 94305-3079 USA",
month = "11 " # dec,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms",
ISBN = "0-18-195720-5",
notes = "part of \cite{koza:1995:gagp}",
}
@Article{Beiki20101091,
author = "Morteza Beiki and Ali Bashari and Abbas Majdi",
title = "Genetic programming approach for estimating the
deformation modulus of rock mass using sensitivity
analysis by neural network",
journal = "International Journal of Rock Mechanics and Mining
Sciences",
volume = "47",
number = "7",
pages = "1091--1103",
year = "2010",
ISSN = "1365-1609",
doi = "doi:10.1016/j.ijrmms.2010.07.007",
URL = "http://www.sciencedirect.com/science/article/B6V4W-50RFN0V-1/2/fa0de8195c17e39f39b1ecead4df4da4",
keywords = "genetic algorithms, genetic programming, Deformation
modulus of rock mass, Relative strength of effect
(RSE), Sensitivity analysis about the mean",
abstract = "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.",
}
@Article{Beldek:2007:IS,
author = "Ulas Beldek and Kemal Leblebicioglu",
title = "Strategy creation, decomposition and distribution in
particle navigation",
journal = "Information Sciences",
year = "2007",
volume = "177",
number = "3",
pages = "755--770",
month = "1 " # feb,
keywords = "genetic algorithms, genetic programming, Rule-base,
Strategy planning, Robot navigation, Maze solving,
Optimization, Multi-agent systems",
doi = "doi:10.1016/j.ins.2006.07.008",
abstract = "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.",
}
@InProceedings{Belgasem:2002:ACDM,
author = "A. Belgasem and T. Kalganova and A. Almaini",
title = "Extrinsic Evolution of Finite State Machine",
booktitle = "Proc. of ACDM2002",
year = "2002",
editor = "I. C. Parmee",
pages = "157--168",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, evolvable
hardware",
abstract = "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.",
}
@InProceedings{beligiannis:1999:EMPFNS,
author = "G. N. Beligiannis and E. N. Demiris and S. D.
Likothanassis",
title = "Evolutionary Multimodel Partitioning Filters for
Nonlinear Systems",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1227",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, EHW, evolvable hardware, poster
papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-452.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-452.pdf",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@Article{Beligiannis:2005:tIM,
title = "Nonlinear model structure identification of complex
biomedical data using a genetic-programming-based
technique",
author = "Grigorios N. Beligiannis and Lambros V. Skarlas and
Spiridon D. Likothanassis and Katerina G. Perdikouri",
journal = "IEEE Transactions on Instrumentation and Measurement",
year = "2005",
volume = "54",
number = "6",
pages = "2184--2190",
month = dec,
keywords = "genetic algorithms, genetic programming, medical
signal processing, nonlinear dynamical systems complex
biomedical data identification, evolutionary multimodel
partitioning filters, nonlinear model structure",
doi = "doi:10.1109/TIM.2005.858573",
ISSN = "0018-9456",
size = "7 pages",
abstract = "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.",
notes = "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",
}
@InCollection{bell:1999:ESWRNNGA,
author = "Matt Bell",
title = "Evolving the Structure and Weights of Recurrent Neural
Network though Genetic Algorithms",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1999",
year = "1999",
editor = "John R. Koza",
pages = "11--20",
address = "Stanford, California, 94305-3079 USA",
month = "15 " # mar,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "part of \cite{koza:1999:GAGPs}",
}
@InProceedings{belpaeme:1999:evfd,
author = "Tony Belpaeme",
title = "Evolution of Visual Feature Detectors",
booktitle = "Late Breaking Papers at EvoISAP'99: the First European
Workshop on Evolutionary Computation in Image Analysis
and Signal Processing",
year = "1999",
editor = "Riccardo Poli and Stefano Cagnoni and Hans-Michael
Voigt and Terry Fogarty and Peter Nordin",
pages = "1--10",
address = "Goteborg, Sweden",
month = "28 " # may,
organisation = "EvoNet",
keywords = "genetic algorithms, genetic programming",
URL = "http://arti.vub.ac.be/~tony/papers/EvoIASP99.ps.gz",
URL = "http://citeseer.ist.psu.edu/362631.html",
abstract = "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.",
notes = "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.",
}
@InProceedings{Belur:1997:CORElb,
author = "Sheela V. Belur",
title = "{CORE}: Constrained Optimization by Random Evolution",
booktitle = "Late Breaking Papers at the 1997 Genetic Programming
Conference",
year = "1997",
editor = "John R. Koza",
pages = "280--286",
address = "Stanford University, CA, USA",
publisher_address = "Stanford University, Stanford, California,
94305-3079, USA",
month = "13--16 " # jul,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms",
ISBN = "0-18-206995-8",
notes = "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",
}
@InProceedings{Benbassat:2010:CIGPU,
author = "Amit Benbassat and Moshe Sipper",
title = "Evolving Lose-Checkers Players using Genetic
Programming",
booktitle = "IEEE Conference on Computational Intelligence and
Game",
year = "2010",
pages = "30--37",
address = "IT University of Copenhagen, Denmark",
month = "18-21 " # aug,
keywords = "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)",
URL = "http://game.itu.dk/cig2010/proceedings/papers/cig10_005_011.pdf",
doi = "doi:10.1109/ITW.2010.5593376",
size = "8 pages",
abstract = "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.",
notes = "http://game.itu.dk/cig2010/proceedings/wp-content/acceptedpapers.html
Also known as \cite{5593376}",
}
@InProceedings{Benbassat:2011:GECCOcomp,
author = "Amit Benbassat and Moshe Sipper",
title = "Evolving board-game players with genetic programming",
booktitle = "GECCO 2011 Graduate students workshop",
year = "2011",
editor = "Miguel Nicolau",
isbn13 = "978-1-4503-0690-4",
keywords = "genetic algorithms, genetic programming",
pages = "739--742",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001858.2002080",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{2002080} Distributed on CD-ROM at
GECCO-2011.
ACM Order Number 910112.",
}
@InProceedings{Bengio:1994:GPslrNN,
author = "Samy Bengio and Yoshua Bengio and Jocelyn Cloutier",
title = "Use of genetic programming for the search of a new
learning rule for neutral networks",
booktitle = "Proceedings of the 1994 IEEE World Congress on
Computational Intelligence",
year = "1994",
volume = "1",
pages = "324--327",
address = "Orlando, Florida, USA",
month = "27-29 " # jun,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
size = "4 pages",
URL = "http://www.idiap.ch/~bengio/cv/publications/ps/bengio_1994_wcci.ps.gz",
URL = "http://citeseer.ist.psu.edu/465154.html",
doi = "doi:10.1109/ICEC.1994.349932",
abstract = "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",
notes = "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",
}
@PhdThesis{BenHamid:thesis,
author = "Sana {Ben Hamida}",
title = "Evolutionary Algorithms: Handling Constraints and
Real-World Application",
school = "Ecole Polytechnique",
year = "2001",
address = "Paris",
month = "mars",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cmap.polytechnique.fr/~sana/these.ps.gz",
URL = "http://www.cmap.polytechnique.fr/~sana/indexAng.html",
size = "225 pages",
abstract = "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.",
notes = "In French. Chapter 7 GP v ES on laser. Supervisor:
Marc Schoenauer",
}
@InCollection{benini:1995:GFESOADF,
author = "Luca Benini",
title = "Genetic Fitting: Evolutionary Search of Optimal
Approximations for Discrete Functions",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1995",
year = "1995",
editor = "John R. Koza",
pages = "19--28",
address = "Stanford, California, 94305-3079 USA",
month = "11 " # dec,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-195720-5",
notes = "part of \cite{koza:1995:gagp}",
}
@InProceedings{Benjamin:2008:cec,
author = "Simon C. Benjamin",
title = "Evolutionary Route to Computation in Self-Assembled
Nanoarrays",
booktitle = "2008 IEEE World Congress on Computational
Intelligence",
year = "2008",
editor = "Jun Wang",
address = "Hong Kong",
month = "1-6 " # jun,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-1823-7",
file = "EC0685.pdf",
abstract = "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.",
keywords = "genetic algorithms, genetic programming",
notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
EPS and the IET.",
}
@InProceedings{Benkhelifa:2009:cec,
author = "E. Benkhelifa and G. Dragffy and A. G. Pipe and M.
Nibouche",
title = "Design Innovation for Real World Applications, Using
Evolutionary Algorithms",
booktitle = "2009 IEEE Congress on Evolutionary Computation",
year = "2009",
editor = "Andy Tyrrell",
pages = "-",
address = "Trondheim, Norway",
month = "18-21 " # may,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-2959-2",
file = "P692.pdf",
abstract = "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.",
keywords = "genetic algorithms, genetic programming",
notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and
the IET. IEEE Catalog Number: CFP09ICE-CDR",
}
@InProceedings{Benkhelifa:2010:cec,
author = "Elhadj Benkhelifa and Ashutosh Tiwari and Anthony
Pipe",
title = "Evolutionary design optimisation of a 32-Step Traffic
Lights Controller",
booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-6910-9",
abstract = "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.",
doi = "doi:10.1109/CEC.2010.5586108",
notes = "WCCI 2010. Also known as \cite{5586108}",
}
@InProceedings{Bennett:2007:SGAI,
author = "Andrew Bennett and Derek Magee",
title = "Learning Sets of Sub-Models for Spatio-Temporal
Prediction",
booktitle = "AI-2007 Twenty-seventh SGAI International Conference
on Artificial Intelligence",
year = "2007",
editor = "Max Bramer and Richard Ellis",
address = "Cambridge, UK",
month = "10-12 " # dec,
organisation = "British Computer Society's Specialist Group on
Artificial Intelligence (SGAI)",
keywords = "genetic algorithms, genetic programming, card game
playing",
URL = "http://www.bcs-sgai.org/ai2007/admin/papers2.php?f=techpapers",
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.150.6694",
URL = "http://citeseerx.ist.psu.edu/viewdoc/download/10.1.1.150.6694.pdf",
size = "14 page",
abstract = "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.",
notes = "University of Leeds, UK",
}
@InProceedings{Bennett:2008:CIMA,
author = "Andrew Bennett and Derek Magee",
title = "Using Genetic Programming to Learn Models Containing
Temporal Relations from Spatio-Temporal Data",
booktitle = "Proceedings of the 1st International Workshop on
Combinations of Intelligent Methods and Applications",
year = "2008",
editor = "Ioannis Hatzilygeroudis and Constantinos Koutsojannis
and Vasile Palade",
address = "Patras, Greece",
month = jul # " 22",
organisation = "CEUR",
keywords = "genetic algorithms, genetic programming",
URL = "http://ftp.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-375/paper2.pdf",
URL = "http://www.comp.leeds.ac.uk/andrewb/Publications/CIMA08.pdf",
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.142.8374",
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.150.6758",
URN = "urn:nbn:de:0074-375-1",
bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
contributor = "CiteSeerX",
language = "en",
oai = "oai:CiteSeerXPSU:10.1.1.142.8374",
oai = "oai:CiteSeerXPSU:10.1.1.150.6758",
abstract = "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.",
notes = "CIMA'08 Combinations of Intelligent Methods and
Applications
http://ftp.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-375/",
}
@PhdThesis{bennett_a,
author = "Andrew David Bennett",
title = "Using genetic programming to learn predictive models
from spatio-temporal data",
school = "School of Computing, University of Leeds",
year = "2010",
address = "UK",
month = jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://etheses.whiterose.ac.uk/1376/",
URL = "http://etheses.whiterose.ac.uk/1376/1/bennett_a.pdf",
size = "211 pages",
abstract = "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.",
notes = "noughts and crosses",
}
@InProceedings{bennett:1996:emaa,
author = "Forrest H {Bennett III}",
title = "Automatic Creation of an Efficient Multi-Agent
Architecture Using Genetic Programming with
Architecture-Altering Operations",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "30--38",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
size = "9 pages",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96",
}
@InProceedings{bennett:1996:emaant,
author = "Forrest H {Bennett III}",
title = "Emergence of a Multi-Agent Architecture and New
Tactics For the Ant Colony Foraging Problem Using
Genetic Programming",
booktitle = "Proceedings of the Fourth International Conference on
Simulation of Adaptive Behavior: From animals to
animats 4",
year = "1996",
editor = "Pattie Maes and Maja J. Mataric and Jean-Arcady Meyer
and Jordan Pollack and Stewart W. Wilson",
pages = "430--439",
address = "Cape Code, USA",
publisher_address = "Cambridge, MA, USA",
month = "9-13 " # sep,
publisher = "MIT Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-262-63178-4",
notes = "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.",
}
@InProceedings{bennet:1996:ices60db,
author = "Forrest H {Bennett III} and John R. Koza and David
Andre and Martin A. Keane",
title = "Evolution of a 60 Decibel op amp using genetic
programming",
booktitle = "Proceedings of International Conference on Evolvable
Systems: From Biology to Hardware (ICES-96)",
year = "1996",
editor = "Tetsuya Higuchi and Iwata Masaya and Weixin Liu",
volume = "1259",
series = "Lecture Notes in Computer Science",
address = "Tsukuba, Japan",
publisher_address = "Berlin, Germany",
month = "7-8 " # oct,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-63173-9",
ISSN = "0302-9743",
LCCN = "QA76.618 .I57 1996",
bibdate = "Mon Nov 24 10:31:37 1997",
acknowledgement = ack-nhfb,
URL = "http://www.genetic-programming.com/jkpdf/ices1996fhbamplifier60.pdf",
size = "18 pages",
abstract = "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.",
notes = "URL=version 1 as presented at the conference
http://www.etl.go.jp:8080/etl/kikou/ICES96/",
}
@InProceedings{bennet:1997:msrrrdpe,
author = "Forrest H {Bennett III}",
title = "A Multi-Skilled Robot that Recognizes and Responds to
Different Problem Environments",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "44--51",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/bennet_1997_msrrrdpe.pdf",
size = "8 pages",
notes = "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",
}
@InProceedings{bennett:1999:SCASE,
author = "Forrest H {Bennett III} and John R. Koza and Martin A.
Keane and David Andre",
title = "Darwinian Programming and Engineering Design using
Genetic Programming",
booktitle = "Proceedings of the 1st International Workshop on Soft
Computing Applied to Software Engineering",
year = "1999",
editor = "Conor Ryan and Jim Buckley",
pages = "31--40",
address = "University of Limerick, Ireland",
month = "12-14 " # apr,
organisation = "SCARE",
publisher = "Limerick University Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-874653-52-6",
URL = "http://www.genetic-programming.com/jkpdf/scase1999.pdf",
abstract = "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.",
notes = "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",
}
@InProceedings{bennet:1999:astsaecGP,
author = "Forrest H {Bennett III} and Martin A. Keane and David
Andre and John R. Koza",
title = "Automatic Synthesis of the Topology and Sizing for
Analog Electrical Circuits Using Genetic Programming",
booktitle = "Evolutionary Algorithms in Engineering and Computer
Science",
year = "1999",
editor = "Kaisa Miettinen and Marko M. Makela and Pekka
Neittaanmaki and Jacques Periaux",
pages = "199--229",
address = "Jyvaskyla, Finland",
publisher_address = "Chichester, UK",
month = "30 " # may # " - 3 " # jun,
publisher = "John Wiley \& Sons",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-471-99902-4",
URL = "http://www.genetic-programming.com/jkpdf/eurogen1999circuits.pdf",
abstract = "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.",
notes = "EUROGEN'99
http://www.wiley.com/Corporate/Website/Objects/Products/0,9049,91449,00.html",
}
@InProceedings{bennett:1999:AISB,
author = "Forrest H {Bennett III} and John R. Koza and Martin A.
Keane and David Andre",
title = "Genetic programming: Biologically inspired computation
that exhibits creativity in solving non-trivial
problems",
booktitle = "Proceedings of the AISB'99 Symposium on Scientific
Creativity",
year = "1999",
pages = "29--38",
address = "Edingburgh",
month = "8-9 " # apr,
organisation = "The Society for the Study of Artificial Intelligence
and Simulation of Behaviour",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.genetic-programming.com/jkpdf/aisb1999.pdf",
abstract = "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.",
notes = "AISB-99",
}
@InProceedings{bennett:1999:BPCSPHPD,
author = "Forrest H {Bennett III} and John R. Koza and James
Shipman and Oscar Stiffelman",
title = "Building a Parallel Computer System for \$18,000 that
Performs a Half Peta-Flop per Day",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1484--1490",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, real world
applications",
ISBN = "1-55860-611-4",
URL = "http://www.genetic-programming.com/jkpdf/gecco1999beowulf.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-788.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-788.ps",
abstract = "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.",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{bennett:1999:EMGPACPDF,
author = "Forrest H {Bennett III} and John R. Koza and Martin A.
Keane and Jessen Yu and William Mydlowec and Oscar
Stiffelman",
title = "Evolution by Means of Genetic Programming of Analog
Circuits that Perform Digital Functions",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1477--1483",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, real world
applications",
ISBN = "1-55860-611-4",
URL = "http://www.genetic-programming.com/jkpdf/gecco1999analog.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-787.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-787.ps",
abstract = "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.",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{bennett:2000:ICES,
author = "Forrest H {Bennett III} and John R. Koza and Jessen Yu
and William Mydlowec",
title = "Automatic synthesis, placement, and routing of an
amplifier circuit by means of genetic programming",
booktitle = "Evolvable Systems: From Biology to Hardware Third
International Conference, ICES 2000",
year = "2000",
editor = "Julian Miller and Adrian Thompson and Peter Thomson
and Terence C. Fogarty",
volume = "1801",
series = "LNCS",
pages = "1--10",
address = "Edinburgh, Scotland, UK",
month = "17-19 " # apr,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-67338-5",
URL = "http://www.genetic-programming.com/jkpdf/ices2000.pdf",
URL = "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-67338-5",
URL = "http://citeseer.ist.psu.edu/471655.html",
abstract = "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.",
notes = "ICES-2000",
}
@InProceedings{Bennett:2000:GECCOlb,
author = "Forrest H {Bennett III} and Eleanor G. Rieffel",
title = "Using Genetic Programming to Design Decentralized
Controllers for Self-Reconfigurable Modular Robots",
pages = "35--42",
booktitle = "Late Breaking Papers at the 2000 Genetic and
Evolutionary Computation Conference",
year = "2000",
editor = "Darrell Whitley",
address = "Las Vegas, Nevada, USA",
month = "8 " # jul,
keywords = "genetic algorithms, genetic programming",
notes = "Part of \cite{whitley:2000:GECCOlb}",
}
@InProceedings{bennett:2000:EH,
author = "F. H {Bennett III} and E. G. Rieffel",
title = "Design of Decentralized Controllers for
Self-Reconfigurable Modular Robots Using Genetic
Programming",
booktitle = "Proceedings of the Second NASA / DoD Workshop on
Evolvable Hardware",
year = "2000",
pages = "43--52",
address = "Palo Alto, California",
month = jul # " 13-15",
organisation = "Jet Propulsion Laboratory, California Institute of
Technology",
publisher = "IEEE Computer Society",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7695-0762-X",
abstract = "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. .",
notes = "EH-2000
http://ic-www.arc.nasa.gov/ic/eh2000/index.html
http://csdl.computer.org/comp/proceedings/eh/2000/0762/00/0762toc.htm",
}
@InProceedings{bennett:2001:EuroGP,
author = "Forrest H {Bennett III} and Brad Dolin and Eleanor G.
Rieffel",
title = "Programmable Smart Membranes: Using Genetic
Programming to Evolve Scalable Distributed Controllers
for a Novel Self-Reconfigurable Modular Robotic
Application",
booktitle = "Genetic Programming, Proceedings of EuroGP'2001",
year = "2001",
editor = "Julian F. Miller and Marco Tomassini and Pier Luca
Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
William B. Langdon",
volume = "2038",
series = "LNCS",
pages = "234--245",
address = "Lake Como, Italy",
publisher_address = "Berlin",
month = "18-20 " # apr,
organisation = "EvoNET",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, modular
robot, distributed control, smart membrane,
self-reconfigurable, scalable, robust",
ISBN = "3-540-41899-7",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=234",
size = "12 pages",
abstract = "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.",
notes = "EuroGP'2001, part of \cite{miller:2001:gp}",
}
@InProceedings{benson:2000:E,
author = "Karl Benson",
title = "Evolving automatic target detection algorithms",
booktitle = "Graduate Student Workshop",
year = "2000",
editor = "Conor Ryan and Una-May O'Reilly and William B.
Langdon",
pages = "249--252",
address = "Las Vegas, Nevada, USA",
month = "8 " # jul,
keywords = "genetic algorithms, genetic programming",
notes = "GECCO-2000WKS Part of \cite{wu:2000:GECCOWKS}",
}
@InProceedings{Benson:2000:GECCO,
author = "Karl A Benson and David Booth and James Cubillo and
Colin Reeves",
title = "Automatic Detection of Ships in Spaceborne {SAR}
Imagery",
pages = "767",
year = "2000",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2000)",
editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz
and Lee Spector and Ian Parmee and Hans-Georg Beyer",
address = "Las Vegas, Nevada, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "10-12 " # jul,
keywords = "genetic algorithms, genetic programming, Poster",
ISBN = "1-55860-708-0",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/RW002.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/RW002.ps",
notes = "A joint meeting of the ninth International Conference
on Genetic Algorithms (ICGA-2000) and the fifth Annual
Genetic Programming Conference (GP-2000) Part of
\cite{whitley:2000:GECCO}
",
}
@InProceedings{benson:2000:efsmegpatdsi,
author = "Karl A Benson",
title = "Evolving Finite State Machines with Embedded Genetic
Programming for Automatic Target Detection within {SAR}
Imagery",
booktitle = "Proceedings of the 2000 Congress on Evolutionary
Computation CEC00",
year = "2000",
pages = "1543--1549",
address = "La Jolla Marriott Hotel La Jolla, California, USA",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "6-9 " # jul,
organisation = "IEEE Neural Network Council (NNC), Evolutionary
Programming Society (EPS), Institution of Electrical
Engineers (IEE)",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, image
processing applications",
ISBN = "0-7803-6375-2",
abstract = "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.",
notes = "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
",
}
@InProceedings{benson:2000:PCEMMA,
author = "Karl Benson",
title = "Performing Classification with an Environment
Manipulating Mutable Automata",
booktitle = "Proceedings of the 2000 Congress on Evolutionary
Computation CEC00",
year = "2000",
pages = "264--271",
address = "La Jolla Marriott Hotel La Jolla, California, USA",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "6-9 " # jul,
organisation = "IEEE Neural Network Council (NNC), Evolutionary
Programming Society (EPS), Institution of Electrical
Engineers (IEE)",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, system
modeling and control",
ISBN = "0-7803-6375-2",
abstract = "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.",
notes = "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
",
}
@InProceedings{benson4,
author = "Karl A Benson and David Booth and James Cubillo and
Colin Reeves",
title = "On the use of evolution to construct finite state
machines and mathematical functions to perform
automatic target detection",
booktitle = "Proceedings of the 3rd {IMA} conference on image
processing: mathematical methods, algorithms and
applications",
year = "2000",
address = "Leicester, UK",
month = "13-15 " # sep,
publisher = "IEE",
organisation = "The Institute of Mathematics and its Applications, The
Institute of Physics, The Institute of Electrical
Engineers",
keywords = "genetic algorithms, genetic programming",
notes = "
",
}
@InProceedings{benson5,
author = "Karl A Benson",
title = "Evolving Automatic Target Detection Algorithms that
logically Combine Decision Spaces",
booktitle = "Proceedings of the 11th British Machine Vision
Conference",
year = "2000",
pages = "685--694",
address = "Bristol, UK",
month = "11-14 " # sep,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.bmva.ac.uk/bmvc/2000/papers/p69.pdf",
abstract = "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",
notes = "
",
}
@PhdThesis{Bensusan:thesis,
author = "Hilan N. Bensusan",
title = "Automatic bias learning: an inquiry into the inductive
basis of induction",
school = "University of Sussex",
year = "1999",
type = "D. Phil.",
month = feb,
keywords = "genetic algorithms, genetic programming, CIGA",
URL = "http://www.cs.bris.ac.uk/Publications/Papers/1000410.pdf",
size = "217 pages",
abstract = "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.",
notes = "System in \cite{bensusan:1996:ciGP} called CIGA
Constructive induction with a Genetic Algorithm",
}
@InProceedings{Bentley:1997:WSC2,
author = "P. J. Bentley and J. P. Wakefield",
title = "Generic Evolutionary Design",
booktitle = "Soft Computing in Engineering Design and
Manufacturing",
year = "1997",
editor = "Pravir K. Chawdhry and Rajkumar Roy and Raj K. Pant",
publisher_address = "Godalming, GU7 3DJ, UK",
month = "23-27 " # jun,
publisher = "Springer-Verlag",
pages = "289--298",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-540-76214-0",
URL = "http://eprints.hud.ac.uk/4053/",
URL = "http://www.springer.com/engineering/mechanical+eng/book/978-3-540-76214-0",
size = "10 pages",
abstract = "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.",
notes = "published 1998?",
}
@InProceedings{Bentley97,
author = "P. J. Bentley and J. P. Wakefield",
title = "Finding Acceptable Solutions in the {Pareto-Optimal}
Range using Multiobjective Genetic Algorithms",
booktitle = "Soft Computing in Engineering Design and
Manufacturing",
year = "1997",
editor = "P. K. Chawdhry and R. Roy and R. K. Pant",
pages = "231--240",
publisher_address = "Godalming, GU7 3DJ, UK",
month = "23-27 " # jun,
publisher = "Springer-Verlag London",
keywords = "genetic algorithms, MOGA",
ISBN = "3-540-76214-0",
URL = "http://eprints.hud.ac.uk/4052/",
abstract = "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.",
notes = "cited by \cite{Ross:2011:GPEM}. WSC2 Second On-line
World Conference on Soft Computing in Engineering
Design and Manufacturing",
size = "10 pages",
}
@InProceedings{Bentley:1999:AVOCAAD,
author = "Peter J. Bentley",
title = "The Future of Evolutionary Design Research",
booktitle = "AVOCAAD Second International Conference",
year = "1999",
pages = "349--350",
address = "Brussels, Belgium",
month = "8-10 " # apr,
keywords = "genetic algorithms, genetic programming, Computer,
design, International",
URL = "http://eprints.ucl.ac.uk/171652/",
notes = "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",
publicationstatus = "published",
}
@InProceedings{Bentley:1999:AISB,
author = "P. J. Bentley",
title = "Is evolution creative?",
booktitle = "Proceedings of the AISB'99 Symposium on Creative
Evolutionary Systems",
year = "1999",
editor = "P. J. Bentley and D. Corne",
pages = "28--34",
address = "Edinburgh",
publisher = "The Society for the Study of Artificial Intelligence
and Simulation of Behaviour",
keywords = "genetic algorithms, genetic programming, gades, CE,
sussex, System, systems",
ISBN = "1-902956-03-6",
URL = "http://www.cs.ucl.ac.uk/staff/P.Bentley/BEC6.pdf",
publicationstatus = "published",
size = "7 pages",
abstract = "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 â 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.",
notes = "The AISB'99 Convention took place in March 1999,
hosted jointly by the University of Edinburgh and the
Edinburgh College of Art",
}
@InProceedings{bentley:1999:TWGDACEEDP,
author = "Peter Bentley and Sanjeev Kumar",
title = "Three Ways to Grow Designs: {A} Comparison of
Embryogenies for an Evolutionary Design Problem",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "35--43",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-611-4",
URL = "http://www.cs.ucl.ac.uk/staff/p.bentley/BEKUC1.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-329.ps",
size = "9 pages",
abstract = "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.",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{bentley:1999:EA,
author = "Peter J. Bentley",
title = "Evolving fuzzy detectives: An investigation into the
evolution of fuzzy rules",
booktitle = "Late Breaking Papers at the 1999 Genetic and
Evolutionary Computation Conference",
year = "1999",
editor = "Scott Brave and Annie S. Wu",
pages = "38--47",
address = "Orlando, Florida, USA",
month = "13 " # jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/staff/P.Bentley/BEC7.pdf",
abstract = "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.",
notes = "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.",
}
@Book{Bentley:evdes,
editor = "Peter J. Bentley",
title = "Evolutionary Design by Computers",
publisher = "Morgan Kaufmann",
year = "1999",
keywords = "genetic algorithms, genetic programming, Computers",
ISBN = "1-55860-605-X",
isbn13 = "9781558606050",
URL = "http://www.cs.ucl.ac.uk/staff/p.bentley/evdes.html",
abstract = "By bringing together the highest achievers in these
fields for the first time, including a foreword by
Richard Dawkins, this book provides the definitive
...",
}
@InCollection{Bentley:1999:intro,
author = "Peter Bentley",
title = "An introduction to evolutionary design by computers",
booktitle = "Evolutionary Design by Computers",
publisher = "Morgan Kaufman",
year = "1999",
editor = "Peter J. Bentley",
chapter = "1",
pages = "1--73",
address = "San Francisco, USA",
keywords = "genetic algorithms, genetic programming, Computer,
Computers, design",
notes = "Part of \cite{Bentley:evdes}",
}
@InProceedings{Bentley:1999:WSC,
author = "P. J. Bentley",
booktitle = "Soft Computing in Industrial Applications",
publisher = "Springer-Verlag London",
title = "Evolving fuzzy detectives: an investigation into the
evolution of fuzzy rules",
year = "1999",
editor = "Yukinori Suzuki and Seppo J. Ovaska and Takeshi
Furuhashi and Rajkumar Roy and Yasuhiko Dote",
pages = "89--106",
keywords = "genetic algorithms, genetic programming, evolution,
fuzzy, industrial, industrial application, Rules",
ISBN = "1-85233-293-X",
URL = "http://www.cs.ucl.ac.uk/staff/P.Bentley/BECH4.pdf",
URL = "http://www.amazon.com/Computing-Industrial-Applications-Yukinori-Suzuki/dp/185233293X",
size = "18 pages",
abstract = "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.",
}
@InProceedings{Bentley:2000:ACDM,
author = "P. J. Bentley",
title = "Exploring component-based representations - the secret
of creativity by evolution?",
booktitle = "Evolutionary Design and Manufacture: Selected Papers
from ACDM'00",
year = "2000",
editor = "I. C. Parmee",
pages = "161--172",
address = "University of Plymouth, Devon, UK",
publisher_address = "Berlin/Heidelberg, Germany",
month = apr,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, Adaptive,
design",
isbn13 = "9781852333003",
URL = "http://www.cs.ucl.ac.uk/staff/P.Bentley/BEC9.pdf",
URL = "http://www.springer.com/engineering/mechanical+eng/book/978-1-85233-300-3",
size = "12 pages",
abstract = "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.",
notes = "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",
}
@InProceedings{Bentley:2000:EA,
author = "Peter J. Bentley",
title = "``Evolutionary, my dear Watson'' Investigating
Committee-based Evolution of Fuzzy Rules for the
Detection of Suspicious Insurance Claims",
pages = "702--709",
year = "2000",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2000)",
editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz
and Lee Spector and Ian Parmee and Hans-Georg Beyer",
address = "Las Vegas, Nevada, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "10-12 " # jul,
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-708-0",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/RW074.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/RW074.ps",
size = "8 pages",
notes = "See also \cite{bentley: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
\cite{whitley:2000:GECCO}",
}
@InProceedings{bentley:2001:NTEC,
author = "Peter J. Bentley and Timothy Gordon and Jungwon Kim
and Sanjeev Kumar",
title = "New Trends in Evolutionary Computation",
booktitle = "Proceedings of the 2001 Congress on Evolutionary
Computation CEC2001",
year = "2001",
volume = "1",
pages = "162--169",
address = "COEX, World Trade Center, 159 Samseong-dong,
Gangnam-gu, Seoul, Korea",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "27-30 " # may,
organisation = "IEEE Neural Network Council (NNC), Evolutionary
Programming Society (EPS), Institution of Electrical
Engineers (IEE)",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, new trends,
creative evolution, computation embryology, evolvable
hardware, artificial immune systems",
ISBN = "0-7803-6658-1",
doi = "doi:10.1109/CEC.2001.934385",
size = "8 pages",
abstract = "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",
notes = "gades
CEC-2001 - A joint meeting of the IEEE, Evolutionary
Programming Society, Galesia, and the IEE.
IEEE Catalog Number = 01TH8546C,
Library of Congress Number =",
}
@InProceedings{Bentley:2001:geccowks,
author = "Peter J. Bentley and Una-May O'Reilly",
title = "Ten steps to make a perfect creative evolutionary
design system",
booktitle = "Non-Routine Design with Evolutionary Systems,
GECCO-2001 Workshop",
year = "2001",
editor = "Peter Bentley and Mary Lou Maher and Josiah Poon",
month = "7 " # jul,
keywords = "genetic algorithms, genetic programming, Agency GP,
design, evolutionary, SYSTEM, SYSTEMS, WORKSHOP",
URL = "http://sydney.edu.au/engineering/it/~josiah/gecco_workshop_bentley.pdf",
size = "7 pages",
abstract = "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 authorsâ
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.",
notes = "http://sydney.edu.au/engineering/it/~josiah/gecco2001_workshop_schedule.html",
}
@InCollection{bentley:2001:CES,
author = "Peter J. Bentley and David W. Corne",
title = "An Introduction to Creative Evolutionary Systems",
booktitle = "Creative Evolutionary Systems",
publisher = "Morgan Kaufmann",
year = "2001",
editor = "Peter J. Bentley and David W. Corne",
pages = "1--75",
month = jul,
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-673-4",
notes = "GP include amongst other EC techniques. Part of
\cite{Bentley:2002:bookCES}",
size = "75 pages",
}
@Book{Bentley:2002:bookCES,
editor = "Peter Bentley and David Corne",
title = "Creative evolutionary systems",
year = "2002",
publisher = "Morgan Kaufmann",
address = "USA",
keywords = "genetic algorithms, genetic programming, Computers",
ISBN = "1-55860-673-4",
isbn13 = "9781558606739",
URL = "http://www.amazon.com/Creative-Evolutionary-Kaufmann-Artificial-Intelligence/dp/1558606734",
abstract = "This book concentrates on applying important ideas in
evolutionary computation to creative areas, such as
art, music, architecture, and design.",
notes = "Chapters on GP",
}
@Book{Bentley:2002:DB,
author = "Peter J. Bentley",
title = "Digital Biology. How Nature is Transforming Our
Technology and Our Lives",
publisher = "Simon and Schuster",
year = "2002",
address = "USA",
keywords = "genetic algorithms, genetic programming, biology,
digital, nature, technology",
ISBN = "0-7432-0447-6",
URL = "http://www.amazon.com/Digital-Biology-Peter-J-Bentley/dp/0743204476",
notes = "Hardback",
size = "272 pages",
}
@Article{bentley:2003:GPEM,
author = "Peter J. Bentley and Jon Timmis",
title = "Guest Editorial Special Issue on Artificial Immune
Systems",
journal = "Genetic Programming and Evolvable Machines",
year = "2003",
volume = "4",
number = "4",
pages = "307--309",
month = dec,
keywords = "artificial immune systems",
ISSN = "1389-2576",
doi = "doi:10.1023/A:1026182810701",
notes = "Special issue on artificial immune systems. Article
ID: 5144845",
}
@Article{bentley:2004:GPEM,
author = "Peter J. Bentley",
title = "Fractal Proteins",
journal = "Genetic Programming and Evolvable Machines",
year = "2004",
volume = "5",
number = "1",
pages = "71--101",
month = mar,
keywords = "genetic algorithms, fractal proteins, development,
evolvability, scalability, complexity",
ISSN = "1389-2576",
doi = "doi:10.1023/B:GENP.0000017011.51324.d2",
abstract = "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.",
notes = "Article ID: 5264735",
}
@Proceedings{DBLP:conf/icaris/2008,
editor = "Peter J. Bentley and Doheon Lee and Sungwon Jung",
title = "7th International Conference on Artificial Immune
Systems, {ICARIS} 2008",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "5132",
year = "2008",
address = "Phuket, Thailand",
month = aug # " 10-13",
isbn13 = "978-3-540-85071-7",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "This book constitutes the refereed proceedings of the
7th International Conference on Artificial Immune
Systems, ICARIS 2008, held in Phuket, Thailand, in
...",
ISBN = "3-540-85071-6",
isbn13 = "9783540850717",
keywords = "Computers",
}
@InProceedings{benhahia:1997:GPvd,
author = "Ilham Benyahia and J. Yves Potvin",
title = "Genetic Programming for Vehicle Dispatch",
booktitle = "Proceedings of the 1997 {IEEE} International
Conference on Evolutionary Computation",
year = "1997",
pages = "547--552",
address = "Indianapolis, USA",
publisher_address = "Piscataway, NJ, USA",
month = "13-16 " # apr,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1109/ICEC.1997.592371",
abstract = "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",
notes = "ICEC-97",
}
@Article{Benyahia:1998:SMC,
author = "Ilham Benyahia and Jean-Yves Potvin",
title = "Decision Support for Vehicle Dispatching Using Genetic
Programming",
journal = "IEEE Transactions on Systems, Man, and Cybernetics
part A: systems and humans",
year = "1998",
volume = "28",
number = "3",
pages = "306--314",
month = may,
keywords = "genetic algorithms, genetic programming",
URL = "http://ieeexplore.ieee.org/iel4/3468/14669/00668962.pdf",
size = "9 pages",
abstract = "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.",
}
@InProceedings{conf/dms/BenyahiaT08,
title = "Optimizing the Architecture of Adaptive Complex
Applications Using Genetic Programming",
author = "Ilham Benyahia and Vincent Talbot",
publisher = "Knowledge Systems Institute",
year = "2008",
bibdate = "2009-06-06",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/dms/dms2008.html#BenyahiaT08",
pages = "27--31",
booktitle = "The 14th International Conference on Distributed
Multimedia Systems, DMS'2008",
address = "Hyatt Harborside at Logan Int'l Airport, Boston, USA",
month = "4-6 " # sep,
organisation = "Knowledge Systems Institute",
keywords = "genetic algorithms, genetic programming",
abstract = "unseen",
notes = "http://www.ksi.edu/seke/dms08.html",
}
@Article{Berardi:2008:JH,
author = "L. Berardi and Z. Kapelan and O. Giustolisi and D. A.
Savic",
title = "Development of pipe deterioration models for water
distribution systems using {EPR}",
journal = "Journal of Hydroinformatics",
year = "2008",
volume = "10",
number = "2",
pages = "113--126",
keywords = "genetic algorithms, genetic programming, data-driven
modelling, evolutionary polynomial regression, failure
analysis, performance indicators, water systems",
ISSN = "1464-7141",
URL = "http://www.iwaponline.com/jh/010/0113/0100113.pdf",
doi = "doi:10.2166/hydro.2008.012",
size = "14 pages",
abstract = "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.",
notes = "Fig 5 bathtub curve
Hydroinformatics Group, Technical University of Bari,
via Orabona 4, I-70125, Bari, Italy",
}
@InProceedings{berarducci:2004:ugw:pber,
author = "Patrick Berarducci and Demetrius Jordan and David
Martin and Jebbifer Seitzer",
title = "{GEVOSH}: Using Grammatical Evolution to Generate
Hashing Functions",
editor = "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",
booktitle = "GECCO 2004 Workshop Proceedings",
year = "2004",
month = "26-30 " # jun,
address = "Seattle, Washington, USA",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/WUGW001.pdf",
size = "4 pages",
abstract = "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.",
notes = "GECCO-2004WKS Distributed on CD-ROM at GECCO-2004",
}
@InCollection{beretz:2002:EAMEABGP,
author = "John P. Beretz",
title = "Evolution of Algorithms for Multi-Species Emergent
Assembly Behavior using Genetic Programming",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2002",
year = "2002",
editor = "John R. Koza",
pages = "21--30",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "part of \cite{koza:2002:gagp}",
}
@InCollection{Bergen:2010:GPTP,
author = "Steven Bergen and Brian J. Ross",
title = "Evolutionary Art Using Summed Multi-Objective Ranks",
booktitle = "Genetic Programming Theory and Practice VIII",
year = "2010",
editor = "Rick Riolo and Trent McConaghy and Ekaterina
Vladislavleva",
series = "Genetic and Evolutionary Computation",
volume = "8",
address = "Ann Arbor, USA",
month = "20-22 " # may,
publisher = "Springer",
chapter = "14",
pages = "227--244",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4419-7746-5",
URL = "http://www.springer.com/computer/ai/book/978-1-4419-7746-5",
notes = "part of \cite{Riolo:2010:GPTP}",
}
@MastersThesis{Bergen:mastersthesis,
author = "Steve Bergen",
title = "Automatic Structure Generation using Genetic
Programming and Fractal Geometry",
school = "Brock University",
year = "2011",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{Bergen:2012:EvoMUSART,
author = "Steve Bergen and Brian Ross",
title = "Aesthetic {3D} Model Evolution",
booktitle = "Proceedings of the 1st International Conference on
Evolutionary and Biologically Inspired Music, Sound,
Art and Design, EvoMUSART 2012",
year = "2012",
month = "11-13 " # apr,
editor = "Penousal Machado and Juan Romero and Adrian
Carballal",
series = "LNCS",
volume = "7247",
publisher = "Springer Verlag",
address = "Malaga, Spain",
pages = "11--22",
organisation = "EvoStar",
doi = "doi:10.1007/978-3-642-29142-5_2",
keywords = "genetic algorithms, genetic programming, Aesthetics,
L-systems, 3D models, multi-objective evaluation",
abstract = "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.",
notes = "Part of \cite{Machado:2012:EvoMusArt} EvoMUSART'2012
held in conjunction with EuroGP2012, EvoCOP2012,
EvoBIO2012 and EvoApplications2012",
}
@InProceedings{berger:1999:AHGAVRPTWIC,
author = "Jean Berger and Mourad Sassi and Martin Salois",
title = "A Hybrid Genetic Algorithm for the Vehicle Routing
Problem with Time Windows and Itinerary Constraints",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "44--51",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InCollection{berger:2002:DMILFAGP,
author = "Eric Berger",
title = "Development of a Minimal Information Line Following
Algorithm using Genetic Programming",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2002",
year = "2002",
editor = "John R. Koza",
pages = "31--35",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.genetic-programming.org/sp2002/Berger.pdf",
notes = "part of \cite{koza:2002:gagp}",
}
@InProceedings{bergstrom:2000:eiraatrGP,
author = "Agneta Bergstrom and Patricija Jaksetic and Peter
Nordin",
title = "Enhancing Information Retrieval by Automatic
Acquisition of Textual Relations using Genetic
Programming",
booktitle = "IUI 2000",
year = "2000",
publisher = "ACM Press",
keywords = "genetic algorithms, genetic programming, machine
learning, natural language processing, semantic
networks, information retrieval",
URL = "http://web.media.mit.edu/~lieber/IUI/Bergstrom/Bergstrom.pdf",
size = "4 pages",
abstract = "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).",
notes = "www",
}
@InProceedings{bergstrom:2000:atrawGP,
author = "Agneta Bergstrom and Patricija Jaksetic and Peter
Nordin",
title = "Acquiring Textual Relations Automatically on the Web
using Genetic Programming",
booktitle = "Genetic Programming, Proceedings of EuroGP'2000",
year = "2000",
editor = "Riccardo Poli and Wolfgang Banzhaf and William B.
Langdon and Julian F. Miller and Peter Nordin and
Terence C. Fogarty",
volume = "1802",
series = "LNCS",
pages = "237--246",
address = "Edinburgh",
publisher_address = "Berlin",
month = "15-16 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-67339-3",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=237",
notes = "EuroGP'2000, part of \cite{poli:2000:GP}",
}
@InProceedings{Berlanga:2006:ICAISC,
author = "F. J. Berlanga and M. J. {del Jesus} and M. J. Gacto
and F. Herrera",
title = "A Genetic-Programming-Based Approach for the Learning
of Compact Fuzzy Rule-Based Classification Systems",
booktitle = "Proceedings 8th International Conference on Artificial
Intelligence and Soft Computing {ICAISC}",
year = "2006",
pages = "182--191",
series = "Lecture Notes on Artificial Intelligence (LNAI)",
volume = "4029",
publisher = "Springer-Verlag",
editor = "Leszek Rutkowski and Ryszard Tadeusiewicz and Lotfi A.
Zadeh and Jacek Zurada",
address = "Zakopane, Poland",
month = jun # " 25-29",
bibdate = "2006-07-05",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/icaisc/icaisc2006.html#BerlangaJGH06",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-35748-3",
doi = "doi:10.1007/11785231_20",
size = "10 pages",
abstract = "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.",
}
@InProceedings{Berlanga:2008:GEFS,
author = "Francisco Jose Berlanga and Maria Jose {del Jesus} and
Francisco Herrera",
title = "A novel genetic cooperative-competitive fuzzy rule
based learning method using genetic programming for
high dimensional problems",
booktitle = "3rd International Workshop on Genetic and Evolving
Fuzzy Systems, GEFS 2008",
year = "2008",
month = "4-7 " # mar,
address = "Witten-Boommerholz, Germany",
pages = "101--106",
keywords = "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)",
doi = "doi:10.1109/GEFS.2008.4484575",
abstract = "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.",
notes = "Also known as \cite{4484575}",
}
@Article{Berlanga20101183,
author = "F. J. Berlanga and A. J. Rivera and M. J. {del Jesus}
and F. Herrera",
title = "{GP}-{COACH}: Genetic Programming-based learning of
{CO}mpact and {AC}curate fuzzy rule-based
classification systems for High-dimensional problems",
journal = "Information Sciences",
volume = "180",
number = "8",
pages = "1183--1200",
year = "2010",
ISSN = "0020-0255",
doi = "doi:10.1016/j.ins.2009.12.020",
URL = "http://www.sciencedirect.com/science/article/B6V0C-4Y34R0J-1/2/82039ab1549f5a0d0fc4d73b2a30bfa6",
keywords = "genetic algorithms, genetic programming,
Classification, Fuzzy rule-based systems, Genetic fuzzy
systems, High-dimensional problems,
Interpretability-accuracy trade-off",
abstract = "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.",
}
@InProceedings{Bernal-Urbina:2008:ijcnn,
author = "M. Bernal-Urbina and A. Flores-Mendez",
title = "Time Series Forecasting through Polynomial Artificial
Neural Networks and Genetic Programming",
booktitle = "2008 IEEE World Congress on Computational
Intelligence",
year = "2008",
editor = "Jun Wang",
pages = "3325--3330",
address = "Hong Kong",
month = "1-6 " # jun,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-1821-3",
file = "NN0903.pdf",
doi = "doi:10.1109/IJCNN.2008.4634270",
ISSN = "1098-7576",
abstract = "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.",
keywords = "genetic algorithms, genetic programming",
notes = "Also known as \cite{4634270}. WCCI 2008 - A joint
meeting of the IEEE, the INNS, the EPS and the IET.",
}
@InProceedings{Bernard:2006:ECML,
author = "Marc Bernard and Amaury Habrard and Marc Sebban",
title = "Learning Stochastic Tree Edit Distance",
booktitle = "Machine Learning: ECML 2006",
year = "2006",
series = "Lecture Notes in Computer Science",
editor = "Johannes Furnkranz and Tobias Scheffer and Myra
Spiliopoulou",
publisher = "Springer",
pages = "42--53",
volume = "4212",
doi = "doi:10.1007/11871842_9",
abstract = "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.",
notes = "not GP but cited by \cite{mcdermott:2011:EuroGP}",
affiliation = "EURISE, Universite Jean Monnet de Saint-Etienne, 23,
rue Paul Michelon, 42023 cedex 2 Saint-Etienne,
France",
}
@InProceedings{Bernardi:2006:CEC,
author = "P. Bernardi and E. Sanchez and M. Schillaci and G.
Squillero and M. {Sonza Reorda}",
title = "An Evolutionary Methodology to Enhance Processor
Software-Based Diagnosis",
booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary
Computation",
year = "2006",
editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and
Simon M. Lucas",
pages = "3201--3206",
address = "Vancouver",
month = "6-21 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, microGP",
ISBN = "0-7803-9487-9",
size = "6 pages",
abstract = "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.",
notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
the IEE.
IEEE Catalog Number: 06TH8846D IEEE Xplore gives pages
= {"}859--864{"},",
}
@Article{Bernhardt:2008:EC,
author = "Knut Bernhardt",
title = "Finding Alternatives and Reduced Formulations for
Process-Based Models",
journal = "Evolutionary Computation",
year = "2008",
volume = "16",
number = "1",
pages = "63--88",
month = "Spring",
keywords = "genetic algorithms, genetic programming, Model
reduction, complexity, dimension reduction",
ISSN = "1063-6560",
doi = "doi:10.1162/evco.2008.16.1.63",
size = "26 pages",
abstract = "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.",
notes = "CVODE, SUNDIALS",
}
@InProceedings{bersano-begey:1996:pici,
author = "Tommaso F. Bersano-Begey and Jason M. Daida and John
F. Vesecky and Frank L. Ludwig",
title = "A Platform-Independent Collaborative Interface for
Genetic Programming Applications: Image Analysis for
Scientific Inquiry",
booktitle = "Late Breaking Papers at the Genetic Programming 1996
Conference Stanford University July 28-31, 1996",
year = "1996",
editor = "John R. Koza",
pages = "1--8",
address = "Stanford University, CA, USA",
publisher_address = "Stanford University, Stanford, California
94305-3079, USA",
month = "28--31 " # jul,
publisher = "Stanford Bookstore",
ISBN = "0-18-201031-7",
keywords = "genetic algorithms, genetic programming",
notes = "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",
}
@InProceedings{bersano-begey:1997:jcifGPa,
author = "Tommaso F. Bersano-Begey and Jason M. Daida and John
F. Vesecky and Frank L. Ludwig",
title = "A {Java} Collaborative Interface for Genetic
Programming Applications: Image Analysis and Scientific
Inquiry",
booktitle = "Proceedings of the 1997 {IEEE} International
Conference on Evolutionary Computation",
year = "1997",
address = "Indianapolis",
publisher_address = "Piscataway, NJ, USA",
month = "13-16 " # apr,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
URL = "ftp://ftp.eecs.umich.edu/people/daida/papers/ICEC97image.pdf",
notes = "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/",
}
@InProceedings{Bersano-Begey:1997:cedslo,
author = "Tommaso F. Bersano-Begey",
title = "Controlling Exploration, Diversity and Escaping Local
Optima in {GP}: Adapting Weights of Training Sets to
Model Resource Consumption",
booktitle = "Late Breaking Papers at the 1997 Genetic Programming
Conference",
year = "1997",
editor = "John R. Koza",
pages = "7--10",
address = "Stanford University, CA, USA",
publisher_address = "Stanford University, Stanford, California,
94305-3079, USA",
month = "13--16 " # jul,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-206995-8",
notes = "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",
}
@InProceedings{Bersano-Begey:1997:grffc,
author = "Tommaso F. Bersano-Begey and Jason M. Daida",
title = "A Discussion on Generality and Robustness and a
Framework for Fitness Set Construction in Genetic
Programming to Promote Robustness",
booktitle = "Late Breaking Papers at the 1997 Genetic Programming
Conference",
year = "1997",
editor = "John R. Koza",
pages = "11--18",
address = "Stanford University, CA, USA",
publisher_address = "Stanford University, Stanford, California,
94305-3079, USA",
month = "13--16 " # jul,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-206995-8",
notes = "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/",
}
@InProceedings{bersano-begey:1997:,
author = "Tommaso F. Bersano-Begey and Patrick G. Kenny and
Edmund H. Durfee",
title = "Multi-Agent Teamwork, Adaptive Learning and
Adversarial Planning in Robocup Using a {PRS}
Architecture",
booktitle = "IJCAI97",
year = "1997",
note = "accepted",
keywords = "genetic algorithms, genetic programming",
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.53.1962",
URL = "http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=BC06E9197308E7FDF6E8347CECE81DC1?doi=10.1.1.53.1962&rep=rep1&type=pdf",
size = "7 pages",
abstract = "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",
notes = "um-prs.pdf broken 5-sep-97
http://www.sonycsl.co.jp/person/kitano/RoboCup/ws97.html",
}
@InProceedings{Bersini:2000:GECCO,
author = "Hugues Bersini",
title = "Chemical Crossover",
pages = "825--832",
year = "2000",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2000)",
editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz
and Lee Spector and Ian Parmee and Hans-Georg Beyer",
address = "Las Vegas, Nevada, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "10-12 " # jul,
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-708-0",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/AA140.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/AA140.ps",
notes = "A joint meeting of the ninth International Conference
on Genetic Algorithms (ICGA-2000) and the fifth Annual
Genetic Programming Conference (GP-2000) Part of
\cite{whitley:2000:GECCO}",
}
@InProceedings{berstein:2004:mpefsgp,
title = "Multiobjective Parsimony Enforcement for Superior
Generalisation Performance",
author = "Yaniv Bernstein and Xiaodong Li and Vic Ciesielski and
Andy Song",
pages = "83--89",
booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary
Computation",
year = "2004",
publisher = "IEEE Press",
month = "20-23 " # jun,
address = "Portland, Oregon",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming,
Multiobjective evolutionary algorithms, Combinatorial
\& numerical optimization",
URL = "http://goanna.cs.rmit.edu.au/~ybernste/papers/Bernstein_CEC_2004.pdf",
size = "7 pages",
abstract = "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.",
notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and
the IEE.",
}
@InProceedings{Berthier:2010:LION,
title = "Consistency Modifications for Automatically Tuned
Monte-Carlo Tree Search",
author = "Vincent Berthier and Hassen Doghmen and Olivier
Teytaud",
booktitle = "Learning and Intelligent OptimizatioN, LION 4",
year = "2010",
editor = "Roberto Battiti",
address = "Venice",
month = jan # " 18-22",
keywords = "genetic algorithms, genetic programming, Game Go,
Mathematics/Optimization and Control, Monte-Carlo Tree
Search Consistency Ko-fights",
URL = "http://hal.archives-ouvertes.fr/docs/00/43/71/46/PDF/consistency.pdf",
URL = "HAL:http://hal.archives-ouvertes.fr/inria-00437146/en/",
bibsource = "OAI-PMH server at hal.archives-ouvertes.fr",
identifier = "HAL:inria-00437146, version 1",
language = "EN",
oai = "oai:hal.archives-ouvertes.fr:inria-00437146_v1",
abstract = "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.",
notes = "LION4
http://lion.disi.unitn.it/intelligent-optimization//LION4/program.php",
}
@InProceedings{Bertram:1997:ris,
author = "Robert R. Bertram and Jason M. Daida and John F.
Vesecky and Guy A. Meadows and Christian Wolf",
title = "Reconstructing Incomplete Signals Using Nonlinear
Interpolation and Genetic Algorithms",
booktitle = "Late Breaking Papers at the 1997 Genetic Programming
Conference",
year = "1997",
editor = "John R. Koza",
pages = "19--27",
address = "Stanford University, CA, USA",
publisher_address = "Stanford University, Stanford, California,
94305-3079, USA",
month = "13--16 " # jul,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-206995-8",
notes = "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 \cite{bertram:1998:risiGA}",
}
@InProceedings{bertram:1998:risiGA,
author = "Robert R. Bertram and Jason M. Daida and John F.
Vesecky and Guy A. Meadows and Christian Wolf",
title = "Reconstructing Incomplete Signals Using Nonlinear
Interpolation and Genetic Algorithms",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "447--454",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms",
URL = "http://citeseer.ist.psu.edu/244792.html",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/10392/ftp:zSzzSzftp.eecs.umich.eduzSzpeoplezSzdaidazSzpaperszSzsga98reconstruct.pdf/reconstructing-incomplete-signals-using.pdf",
size = "8 pages",
abstract = "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.",
notes = "SGA-98
see also \cite{Bertram:1997:ris}",
}
@InProceedings{1068303,
author = "Sireesha Besetti and Terence Soule",
title = "Function choice, resiliency and growth in genetic
programming",
booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on
Genetic and evolutionary computation",
year = "2005",
editor = "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",
volume = "2",
ISBN = "1-59593-010-8",
pages = "1771--1772",
address = "Washington DC, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1771.pdf",
doi = "doi:10.1145/1068009.1068303",
publisher = "ACM Press",
publisher_address = "New York, NY, 10286-1405, USA",
month = "25-29 " # jun,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, Poster,
function choice, growth, resilience",
notes = "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",
}
@InProceedings{best:1999:CMGSE,
author = "Michael L. Best",
title = "Coevolving Mutualists Guide Simulated Evolution",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "941",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "evolution strategies and evolutionary programming,
poster papers",
ISBN = "1-55860-611-4",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{bettenhausen:1995:biox,
author = "K. D. Bettenhausen and S. Gehlen and P. Marenbach and
H. Tolle",
title = "Bio{X}++ -- {N}ew results and conceptions concerning
the intelligent control of biotechnological processes",
booktitle = "6th International Conference on Computer Applications
in Biotechnology",
year = "1995",
editor = "A. Munack and K. Sch{\"u}gerl",
pages = "324--327",
organisation = "IFAC Publications",
publisher = "Elsevier Science",
email = "mali@rt.e-technik.tu-darmstadt.de",
keywords = "genetic algorithms, genetic programming, Expert
systems, neural networks, fuzzy systems, learning
control, fermentation, biotechnology",
URL = "http://www.rtr.tu-darmstadt.de/fileadmin/literature/rst_95_03.pdf",
size = "4 pages",
abstract = "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.",
notes = "14--17 May, Garmisch-Partenkirchen, Germany",
}
@InProceedings{bettenhausen:1995:sombbff,
author = "Kurt Dirk Bettenhausen and Peter Marenbach",
title = "Self-organizing modeling of biotechnological batch and
fed-batch fermentations",
booktitle = "EUROSIM'95",
year = "1995",
editor = "F. Breitenecker and I. Husinsky",
address = "Vienna, Austria",
publisher = "Elsevier",
email = "kurt.dirk.bettenhausen@rt.e-technik.tu-darmstadt.de
(Kurt Dirk Bettenhausen),
mali@rt.e-technik.tu-darmstadt.de",
keywords = "genetic algorithms, genetic programming, fermentation,
biotechnology",
URL = "http://www.rtr.tu-darmstadt.de/fileadmin/literature/rst_95_23.ps.gz",
size = "5 pages",
abstract = "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.",
notes = "11--15 September, Vienna, Austria",
}
@InProceedings{bettenhausen:1995:sombbffGP,
author = "K. D. Bettenhausen and P. Marenbach and Stephan Freyer
and Hans Rettenmaier and Ullrich Nieken",
title = "Self-organizing Structured modeling of a
Biotechnological Fed-batch fermentation by Means of
Genetic Programming",
booktitle = "First International Conference on Genetic Algorithms
in Engineering Systems: Innovations and Applications,
GALESIA",
year = "1995",
editor = "A. M. S. Zalzala",
volume = "414",
pages = "481--486",
address = "Sheffield, UK",
publisher_address = "London, UK",
month = "12-14 " # sep,
publisher = "IEE",
email = "mali@rt.e-technik.tu-darmstadt.de",
keywords = "genetic algorithms, genetic programming, symbolic
modelling, system identification, biotechnology,
predictive control",
ISBN = "0-85296-650-4",
URL = "http://www.rtr.tu-darmstadt.de/fileadmin/literature/rst_95_24.pdf",
size = "6 pages",
abstract = "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.",
notes = "Deals much more than bettenhausen:1995:ssmbff and
\cite{bettenhausen: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.
",
}
@InProceedings{beyer:1999:FNLEOGQFM,
author = "Hans-Georg Beyer and Dirk V. Arnold",
title = "Fitness Noise and Localization Errors of the Optimum
in General Quadratic Fitness Models",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "817--824",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "evolution strategies and evolutionary programming",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/beyer_GECCO99.ps.gz",
URL = "http://www.cs.dal.ca/~dirk/docs/GECCO99.ps.gz",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{beyer_et_al:DSP:2006:498,
author = "Hans-Georg Beyer and Thomas Jansen and Colin Reeves
and Michael D. Vose",
title = "04081 Abstracts Collection -- Theory of Evolutionary
Algorithms",
booktitle = "Theory of Evolutionary Algorithms",
year = "2004",
editor = "Hans-Georg Beyer and Thomas Jansen and Colin Reeves
and Michael D. Vose",
number = "04081",
series = "Dagstuhl Seminar Proceedings",
ISSN = "1862-4405",
publisher = "Internationales Begegnungs- und Forschungszentrum
(IBFI), Schloss Dagstuhl, Germany",
address = "Dagstuhl, Germany",
URL = "http://drops.dagstuhl.de/opus/volltexte/2006/498",
note = "$<$http://drops.dagstuhl.de/opus/volltexte/2006/498$>$
[date of citation: 2006-01-01]",
keywords = "genetic algorithms, genetic programming, Evolutionary
algorithms, co-evolution, run time analysis, landscape
analysis, Markov chains",
abstract = "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.",
notes = "See also \cite{langdon:2003:normal} and
\cite{mcphee:ots:gecco2004}",
}
@Article{beyer:2004:GPEM,
author = "Hans-Georg Beyer and Markus Olhofer and Bernhard
Sendhoff",
title = "On the Impact of Systematic Noise on the Evolutionary
Optimization Performance -- {A} Sphere Model Analysis",
journal = "Genetic Programming and Evolvable Machines",
year = "2004",
volume = "5",
number = "4",
pages = "327--360",
month = dec,
keywords = "ES, evolution strategies, noisy optimisation,
performance analysis, robust optimization",
ISSN = "1389-2576",
doi = "doi:10.1023/B:GENP.0000036020.79188.a0",
abstract = "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].",
notes = "Article ID: 5272968",
}
@Article{beyer:2005:GPEM,
author = "Hans-Georg Beyer and Dirk V. Arnold and Silja
Meyer-Nieberg",
title = "A New Approach for Predicting the Final Outcome of
Evolution Strategy Optimization Under Noise",
journal = "Genetic Programming and Evolvable Machines",
year = "2005",
volume = "6",
number = "1",
pages = "7--24",
month = mar,
keywords = "ES, evolution strategies, final fitness error, noisy
optimization, optimization quality, robust
optimization",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-005-7617-y",
abstract = "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.",
}
@Proceedings{GECCO2005,
title = "{GECCO 2005}: Proceedings of the 2005 conference on
Genetic and evolutionary computation",
year = "2005",
editor = "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",
address = "Washington DC, USA",
publisher_address = "New York, NY, 10286-1405, USA",
month = "25-29 " # jun,
organisation = "ACM SIGEVO (formerly ISGEC)",
publisher = "ACM Press",
keywords = "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",
ISBN = "1-59593-010-8",
URL = "http://portal.acm.org/citation.cfm?id=1068009&jmp=cit&coll=GUIDE&dl=GUIDE&CFID=48779769&CFTOKEN=55479664#supp",
abstract = "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{"}):.",
notes = "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",
}
@Article{Beyer:2006:GPEM,
author = "Hans-Georg Beyer",
title = "Special Issue: Best of {GECCO} 2005",
journal = "Genetic Programming and Evolvable Machines",
year = "2006",
volume = "7",
number = "2",
pages = "129--130",
month = aug,
keywords = "genetic algorithms, genetic programming",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-006-9002-x",
size = "2 pages",
notes = "Introduction to special issue",
}
@Article{Beyer:2007:GPEM,
author = "Hans-Georg Beyer and Silja Meyer-Nieberg",
title = "Self-adaptation of evolution strategies under noisy
fitness evaluations",
journal = "Genetic Programming and Evolvable Machines",
year = "2006",
volume = "7",
number = "4",
pages = "295--328",
month = dec,
keywords = "Evolution strategies, Self-adaptation, Noisy
optimisation, Noisy sphere model",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-006-9017-3",
size = "34 pages",
abstract = "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.",
}
@InCollection{bezdek:1999:EADC,
author = "Trevor Bezdek",
title = "Evolution and Analysis of {DNA} Classifiers",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1999",
year = "1999",
editor = "John R. Koza",
pages = "21--30",
address = "Stanford, California, 94305-3079 USA",
month = "15 " # mar,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "part of \cite{koza:1999:GAGPs}",
}
@Article{Bhalla:2009:GPEM,
author = "Navneet Bhalla",
title = "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",
journal = "Genetic Programming and Evolvable Machines",
year = "2009",
volume = "10",
number = "4",
pages = "473--475",
month = dec,
note = "Book Review",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-009-9088-z",
size = "3 pages",
}
@InProceedings{bhanu:2002:GECCO:workshop,
title = "Coevolutionary Construction of Features for
Transformation of Representation in Machine Learning",
author = "Bir Bhanu and Krzysztof Krawiec",
pages = "249--254",
booktitle = "{GECCO 2002}: Proceedings of the Bird of a Feather
Workshops, Genetic and Evolutionary Computation
Conference",
editor = "Alwyn M. Barry",
year = "2002",
month = "8 " # jul,
publisher = "AAAI",
address = "New York",
publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
URL = "http://www-idss.cs.put.poznan.pl/~krawiec/./pubs/gecco2002.pdf",
URL = "http://citeseer.ist.psu.edu/509773.html",
abstract = "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.",
notes = "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",
}
@InProceedings{bhanu:2002:gecco,
author = "Bir Bhanu and Yingqiang Lin",
title = "Learning Composite Operators For Object Detection",
booktitle = "GECCO 2002: Proceedings of the Genetic and
Evolutionary Computation Conference",
editor = "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",
year = "2002",
pages = "1003--1010",
address = "New York",
publisher_address = "San Francisco, CA 94104, USA",
month = "9-13 " # jul,
publisher = "Morgan Kaufmann Publishers",
keywords = "genetic algorithms, genetic programming, real world
applications, composite operators, genetic image
segmentation, object detection",
ISBN = "1-55860-878-8",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/RWA165_v2.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-20.pdf",
notes = "GECCO-2002. A joint meeting of the eleventh
International Conference on Genetic Algorithms
(ICGA-2002) and the seventh Annual Genetic Programming
Conference (GP-2002)",
}
@Article{bhanu:2004:ASC,
author = "Bir Bhanu and Yingqiang Lin",
title = "Object detection in multi-modal images using genetic
programming",
journal = "Applied Soft Computing",
year = "2004",
volume = "4",
number = "2",
pages = "175--201",
month = may,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.sciencedirect.com/science/article/B6W86-4BV444R-1/2/7540dd938c0b2f3059b1afb5382bd28a",
doi = "doi:10.1016/j.asoc.2004.01.004",
abstract = "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.",
}
@InProceedings{bhanu:2003:gecco,
author = "Krzysztof Krawiec and Bir Bhanu",
title = "Coevolution and Linear Genetic Programming for Visual
Learning",
booktitle = "Genetic and Evolutionary Computation -- GECCO-2003",
editor = "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",
year = "2003",
pages = "332--343",
address = "Chicago",
publisher_address = "Berlin",
month = "12-16 " # jul,
volume = "2723",
series = "LNCS",
ISBN = "3-540-40602-6",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, Coevolution",
abstract = "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.",
notes = "GECCO-2003. A joint meeting of the twelfth
International Conference on Genetic Algorithms
(ICGA-2003) and the eighth Annual Genetic Programming
Conference (GP-2003)",
}
@InProceedings{bhanu:fsu:gecco2004,
author = "Bir Bhanu and Jiangang Yu and Xuejun Tan and Yingqiang
Lin",
title = "Feature Synthesis Using Genetic Programming for Face
Expression Recognition",
booktitle = "Genetic and Evolutionary Computation -- GECCO-2004,
Part II",
year = "2004",
editor = "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",
series = "Lecture Notes in Computer Science",
pages = "896--907",
address = "Seattle, WA, USA",
publisher_address = "Heidelberg",
month = "26-30 " # jun,
organisation = "ISGEC",
publisher = "Springer-Verlag",
volume = "3103",
ISBN = "3-540-22343-6",
ISSN = "0302-9743",
URL = "http://link.springer.de/link/service/series/0558/bibs/3103/31030896.htm",
size = "12",
keywords = "genetic algorithms, genetic programming",
notes = "GECCO-2004 A joint meeting of the thirteenth
international conference on genetic algorithms
(ICGA-2004) and the ninth annual genetic programming
conference (GP-2004)",
}
@Article{Bhanu:2004:PRL,
author = "Bir Bhanu and Yingqiang Lin",
title = "Synthesizing feature agents using evolutionary
computation",
journal = "Pattern Recognition Letters",
year = "2004",
volume = "25",
pages = "1519--1531",
number = "13",
abstract = "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.",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6V15-4CRY8J6-2/2/d245bfcfeee2d509066321e19d84a0fd",
month = "1 " # oct,
note = "Pattern Recognition for Remote Sensing (PRRS 2002)",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1016/j.patrec.2004.06.005",
size = "13 pages",
notes = "SAR",
}
@Book{Bhanu:book,
author = "Bir Bhanu and Yingqiang Lin and Krzysztof Krawiec",
title = "Evolutionary Synthesis of Pattern Recognition
Systems",
year = "2005",
publisher = "Springer-Verlag",
address = "New York",
series = "Monographs in Computer Science",
keywords = "genetic algorithms, genetic programming, visual
learning, feature synthesis, Computer vision, Image
processing, Object detection, Pattern recognition",
ISBN = "0-387-21295-7",
URL = "http://www.springer.com/west/home/computer/imaging?SGWID=4-149-22-39144807-detailsPage=ppmmedia|aboutThisBook",
size = "296 pages",
}
@Article{Bhargavi:2010:IJCSIT,
title = "Soil Classification Using {GATREE}",
author = "P. Bhargavi and S. Jyothi",
journal = "International Journal of Computer Science \&
Information Technology",
year = "2010",
volume = "2",
number = "5",
pages = "184--191",
keywords = "genetic algorithms, genetic programming, data mining,
soil profile, soil database, classification",
ISSN = "09754660",
URL = "http://airccse.org/journal/jcsit/1010ijcsit14.pdf",
doi = "doi:10.5121/ijcsit.2010.2514",
publisher = "Academy \& Industry Research Collaboration Centre
(AIRCC)",
bibsource = "OAI-PMH server at www.doaj.org",
oai = "oai:doaj-articles:62c4c972981e7958ba9ff79981358355",
size = "8 pages",
abstract = "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.",
}
@InProceedings{Bhattacharya:2001:GPR,
author = "Maumita Bhattacharya and Baikunth Nath",
title = "Genetic Programming: {A} Review of Some Concerns",
volume = "2074",
pages = "1031--1040",
year = "2001",
booktitle = "Proceedings of International Conference Computational
Science Part~II - ICCS 2001",
editor = "V. N. Alexandrov and J. J. Dongarra and B. A. Juliano
and R. S. Renner and C. J. Kenneth Tan",
series = "Lecture Notes in Computer Science",
address = "San Francisco, CA, USA",
month = may # " 28-30",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, bloat",
CODEN = "LNCSD9",
ISSN = "0302-9743",
bibdate = "Sat Feb 2 13:04:30 MST 2002",
URL = "http://link.springer-ny.com/link/service/series/0558/bibs/2074/20741031.htm",
URL = "http://link.springer-ny.com/link/service/series/0558/papers/2074/20741031.pdf",
acknowledgement = ack-nhfb,
size = "10 pages",
abstract = "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.",
}
@InProceedings{bhattacharya:2001:HIS,
title = "A Linear Genetic Programming Approach for Modeling
Electricity Demand Prediction in Victoria",
author = "Maumita Bhattacharya and Ajith Abraham and Baikunth
Nath",
editor = "Ajith Abraham and Mario Koppen",
booktitle = "2001 International Workshop on Hybrid Intelligent
Systems",
series = "LNCS",
pages = "379--394",
publisher = "Springer-Verlag",
address = "Adelaide, Australia",
publisher_address = "Berlin",
month = "11-12 " # dec,
year = "2001",
email = "maumita.bhattacharya@infotech.monash.edu.au,
ajith.abraham@infotech.monash.edu.au,
b.nath@infotech.monash.edu.au",
keywords = "genetic algorithms, genetic programming, Linear
genetic programming, neuro-fuzzy, neural networks,
forecasting, electricity demand",
broken = "http://www-mugc.cc.monash.edu.au/~abrahamp/172.pdf",
URL = "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-7908-1480-6",
URL = "http://citeseer.ist.psu.edu/510872.html",
ISBN = "3-7908-1480-6",
abstract = "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.",
notes = "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",
}
@InProceedings{bhattacharyya:1998:rsGPlhf,
author = "Siddhartha Bhattacharyya and Olivier Pictet and Gilles
Zumbach",
title = "Representational Semantics for Genetic Programming
Based Learning in High-Frequency Financial Data",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "11--16",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-548-7",
notes = "GP-98",
}
@Article{bhattacharyya:1998:DS,
author = "Siddhartha Bhattacharyya and Parag C. Pendharkar",
title = "Inductive, Evolutionary, and Neural Computing
Techniques for Discrimination: {A} Comparative Study",
journal = "Decision Sciences",
year = "1998",
volume = "29",
number = "4",
pages = "871--899",
month = "Fall",
keywords = "genetic algorithms, genetic programming, Discriminant
Analysis, Inductive Learning, Machine Learning, and
Neural Networks",
ISSN = "00117315",
URL = "http://tigger.uic.edu/~sidb/papers/DiscCompPaper_DecSci.pdf",
size = "45 pages",
abstract = "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.",
notes = "http://www.decisionsciences.org/dsj/ (USPS 884860)
http://www.decisionsciences.org/dsj/Vol29_4/29_4_871.htm",
}
@InProceedings{347186,
author = "Siddhartha Bhattacharyya",
title = "Evolutionary algorithms in data mining:
multi-objective performance modeling for direct
marketing",
booktitle = "KDD '00: Proceedings of the sixth ACM SIGKDD
international conference on Knowledge discovery and
data mining",
year = "2000",
pages = "465--473",
address = "Boston, Massachusetts, United States",
publisher_address = "New York, NY, USA",
organisation = "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",
publisher = "ACM Press",
keywords = "genetic algorithms, genetic programming, Algorithms,
Design, Experimentation, Human Factors, Management,
Measurement, Performance, Theory, Pareto-optimal
models, data mining, database marketing, evolutionary
computation, multiple objectives",
ISBN = "1-58113-233-6",
URL = "http://tigger.uic.edu/~sidb/papers/MultiObj_KDD2000.pdf",
URL = "http://portal.acm.org/ft_gateway.cfm?id=347186&type=pdf&coll=GUIDE&dl=GUIDE&CFID=43813975&CFTOKEN=68162530",
doi = "doi:10.1145/347090.347186",
size = "9 pages",
abstract = "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.",
notes = "p470 {"}For the non-linear GP, results were found to
be similar to those observed for the linear GA.
{"}Elitism always provides improved performance{"}.",
}
@InCollection{bhattacharyya:2002:ECEF,
author = "Siddhartha Bhattacharyya and Kumar Mehta",
title = "Evolutionary Induction of Trading Models",
booktitle = "Evolutionary Computation in Economics and Finance",
publisher = "Physica Verlag",
year = "2002",
editor = "Shu-Heng Chen",
volume = "100",
series = "Studies in Fuzziness and Soft Computing",
chapter = "17",
pages = "311--332",
month = "2002",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-7908-1476-8",
URL = "http://tigger.uic.edu/~sidb/papers/EvolInductionOfTradingModels.pdf",
abstract = "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.",
notes = "http://btobsearch.barnesandnoble.com/booksearch/isbnInquiry.asp?sourceid=00395996645644787198&btob=Y&endeca=1&isbn=3790814768&itm=2",
size = "22 pages",
}
@Article{bhattacharyya:2002:trEC,
author = "Siddhartha Bhattacharyya and Olivier V. Pictet and
Gilles Zumbach",
title = "Knowledge-intensive genetic discovery in foreign
exchange markets",
journal = "IEEE Transactions on Evolutionary Computation",
year = "2002",
volume = "6",
number = "2",
pages = "169--181",
month = apr,
keywords = "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",
ISSN = "1089-778X",
URL = "http://tigger.uic.edu/~sidb/papers/KnowIntenGPForex__IEEE_EC.pdf",
doi = "doi:10.1109/4235.996016",
size = "13 pages",
abstract = "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",
notes = "CODEN: ITEVF5 INSPEC Accession Number:7256658",
}
@InProceedings{Bhowan:2009:cec,
author = "Urvesh Bhowan and Mark Johnston and Mengjie Zhang",
title = "Differentiating Between Individual Class Performance
in Genetic Programming Fitness for Classification with
Unbalanced Data",
booktitle = "2009 IEEE Congress on Evolutionary Computation",
year = "2009",
editor = "Andy Tyrrell",
pages = "2802--2809",
address = "Trondheim, Norway",
month = "18-21 " # may,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-2959-2",
file = "P289.pdf",
doi = "doi:10.1109/CEC.2009.4983294",
abstract = "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.",
keywords = "genetic algorithms, genetic programming",
notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and
the IET. IEEE Catalog Number: CFP09ICE-CDR",
}
@InProceedings{Bhowan:2009:IVCNZ,
title = "Genetic Programming for Image Classification with
Unbalanced Data",
author = "Urvesh Bhowan and Mengjie Zhang and Mark Johnston",
booktitle = "Proceeding of the 24th International Conference Image
and Vision Computing New Zealand, IVCNZ '09",
year = "2009",
month = "23-25 " # nov,
pages = "316--321",
doi = "doi:10.1109/IVCNZ.2009.5378388",
ISSN = "2151-2205",
address = "Wellington",
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-4697-1",
abstract = "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.",
notes = "Also known as \cite{5378388}",
}
@InProceedings{DBLP:conf/ausai/BhowanZJ09,
author = "Urvesh Bhowan and Mengjie Zhang and Mark Johnston",
title = "Multi-Objective Genetic Programming for Classification
with Unbalanced Data",
booktitle = "Proceedings of the 22nd Australasian Joint Conference
on Artificial Intelligence (AI'09)",
year = "2009",
editor = "Ann E. Nicholson and Xiaodong Li",
volume = "5866",
series = "Lecture Notes in Computer Science",
pages = "370--380",
bibsource = "DBLP, http://dblp.uni-trier.de",
address = "Melbourne, Australia",
month = dec # " 1-4",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-10438-1",
doi = "doi:10.1007/978-3-642-10439-8_38",
abstract = "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.",
}
@InProceedings{Bhowan:2010:EuroGP,
author = "Urvesh Bhowan and Mengjie Zhang and Mark Johnston",
title = "Genetic Programming for Classification with Unbalanced
Data",
booktitle = "Proceedings of the 13th European Conference on Genetic
Programming, EuroGP 2010",
year = "2010",
editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara
Silva and Stephen Dignum and A. Sima Uyar",
volume = "6021",
series = "LNCS",
pages = "1--13",
address = "Istanbul",
month = "7-9 " # apr,
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-12147-0",
doi = "doi:10.1007/978-3-642-12148-7_1",
abstract = "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.",
notes = "Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010
held in conjunction with EvoCOP2010 EvoBIO2010 and
EvoApplications2010",
}
@InProceedings{Bhowan:2010:gecco,
author = "Urvesh Bhowan and Mengjie Zhang and Mark Johnston",
title = "{AUC} analysis of the pareto-front using
multi-objective {GP} for classification with unbalanced
data",
booktitle = "GECCO '10: Proceedings of the 12th annual conference
on Genetic and evolutionary computation",
year = "2010",
editor = "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",
isbn13 = "978-1-4503-0072-8",
pages = "845--852",
keywords = "genetic algorithms, genetic programming",
month = "7-11 " # jul,
organisation = "SIGEVO",
address = "Portland, Oregon, USA",
doi = "doi:10.1145/1830483.1830639",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{1830639} GECCO-2010 A joint
meeting of the nineteenth international conference on
genetic algorithms (ICGA-2010) and the fifteenth annual
genetic programming conference (GP-2010)",
}
@InProceedings{conf/ausai/BhowanZJ10,
title = "A Comparison of Classification Strategies in Genetic
Programming with Unbalanced Data",
author = "Urvesh Bhowan and Mengjie Zhang and Mark Johnston",
booktitle = "Australasian Conference on Artificial Intelligence",
editor = "Jiuyong Li",
year = "2010",
volume = "6464",
series = "Lecture Notes in Computer Science",
pages = "243--252",
address = "Adelaide",
month = dec,
publisher = "Springer",
bibdate = "2010-11-30",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/ausai/ausai2010.html#BhowanZJ10",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-17431-5",
doi = "doi:10.1007/978-3-642-17432-2_25",
size = "10 pages",
abstract = "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.",
affiliation = "School of Engineering and Computer Science, Victoria
University of Wellington, New Zealand",
}
@InProceedings{Bhowan:2011:GECCO,
author = "Urvesh Bhowan and Mark Johnston and Mengjie Zhang",
title = "Evolving ensembles in multi-objective genetic
programming for classification with unbalanced data",
booktitle = "GECCO '11: Proceedings of the 13th annual conference
on Genetic and evolutionary computation",
year = "2011",
editor = "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",
isbn13 = "978-1-4503-0557-0",
pages = "1331--1338",
keywords = "genetic algorithms, genetic programming",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001576.2001756",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{2001756} GECCO-2011 A joint
meeting of the twentieth international conference on
genetic algorithms (ICGA-2011) and the sixteenth annual
genetic programming conference (GP-2011)",
}
@InProceedings{Bickel:1989:tsrGA,
author = "Authur S. Bickel and Riva Wenig Bickel",
title = "Tree Structured Rules in Genetic Algorithms",
booktitle = "Genetic Algorithms and their Applications: Proceedings
of the second International Conference on Genetic
Algorithms",
year = "1987",
editor = "John J. Grefenstette",
pages = "77--81",
address = "MIT, Cambridge, MA, USA",
publisher_address = "Hillsdale, NJ, USA",
month = "28-31 " # jul,
publisher = "Lawrence Erlbaum Associates",
keywords = "genetic algorithms, genetic programming",
size = "5 pages",
bibtex_url = "http://dl.acm.org/citation.cfm?id=42523&CFID=53044018&CFTOKEN=44075976",
abstract = "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",
}
@InProceedings{Bidaud:2002:romansy,
author = "Philippe Bidaud and Frederic Chapelle and G. Dumont",
title = "Evolutionary optimization of mechanical and control
design. Application to active endoscopes",
booktitle = "Theory and Practice of Robots and Manipulators",
organization = "CISM - IFToMM",
address = "Udine, Italy",
month = jul,
pages = "317--330",
editor = "Giovanni Bianchi and Jean-Claude Guinot and Cezary
Rzymkowski",
publisher = "Springer Verlag",
keywords = "genetic algorithms, genetic programming",
year = "2002",
number = "14",
series = "RoManSy",
publisher_address = "Wien/New York",
notes = "http://www.meil.pw.edu.pl/romansy2002/html/romansy14.htm",
}
@Article{Biesheuvel:2004:JCE,
author = "Cornelis J. Biesheuvel and Ivar Siccama and Diederick
E. Grobbee and Karel G. M. Moons",
title = "Genetic programming outperformed multivariable
logistic regression in diagnosing pulmonary embolism",
journal = "Journal of Clinical Epidemiology",
year = "2004",
volume = "57",
pages = "551--560",
number = "6",
abstract = "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.",
owner = "wlangdon",
URL = "http://igitur-archive.library.uu.nl/med/2006-0906-200235/grobbee_04_geneticprogrammingoutperformed.pdf",
URL = "http://www.sciencedirect.com/science/article/B6T84-4CTB5RT-3/2/325f5e3699d990701839201564eff8d3",
month = jun,
keywords = "genetic algorithms, genetic programming, Logistic
regression, Prediction, Diagnostic research,
Discrimination, Reliability",
doi = "doi:10.1016/j.jclinepi.2003.10.011",
notes = "PMID: 15246123 [PubMed - indexed for MEDLINE]",
}
@PhdThesis{biesheuvel:thesis,
author = "Cornelis Jan Biesheuvel",
title = "Diagnostic Research : improvements in design and
analysis",
school = "Universiteit Utrecht",
year = "2005",
address = "Holland",
ISBN = "90-393-2706-8",
keywords = "genetic algorithms, genetic programming, diagnosis,
methodology, prediction research",
URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/",
URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/full.pdf",
URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/title.pdf",
URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/contents.pdf",
URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/c1.pdf",
URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/c2.pdf",
URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/c3.pdf",
URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/c4.pdf",
URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/c5.pdf",
URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/c6.pdf",
URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/c7.pdf",
URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/c8.pdf",
URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/sum.pdf",
URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/sam.pdf",
URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/dank.pdf",
URL = "http://igitur-archive.library.uu.nl/dissertations/2005-0511-200047/cv.pdf",
size = "103 pages",
abstract = "In the era of evidence-based medicine, diagnostic
procedures also need to undergo critical evaluations.
In contrast to guidelines for randomised trials and
observational etiologic studies, principles and methods
for diagnostic evaluations are still incomplete. The
research described in this thesis was conducted to
further improve the methods for design and analysis of
diagnostic studies.
In the past, most diagnostic accuracy studies followed
a univariable or single test approach with the aim to
quantify the sensitivity, specificity or likelihood
ratio. However, single test studies and measures do not
reflect a test's added value. It is not the singular
association between a particular test result or
predictor and the diagnostic outcome that is
informative, but the test's value independent of
diagnostic information. Multivariable modelling is
necessary to estimate the value of a particular test
conditional on other test results. However, diagnostic
prediction rules are not the solution to everything.
They have certain drawbacks, such as overoptimistic
accuracy when applied to new patients. Recently,
methods have been described to overcome some of these
drawbacks. Typically, in diagnostic research one
selects a cohort of patients with an indication for the
diagnostic procedure at interest as defined by the
patients' suspicion of having the disease of interest.
The data are analysed cross-sectionally. When
appropriate analyses are applied, results from nested
case-control studies should be virtually identical to
results based on a full cohort analysis. We showed that
the nested case-control design offers investigators a
valid and efficient alternative for a full cohort
approach in diagnostic research. This may be
particularly important when the results of the test
under study are costly or difficult to collect.
It is suggested that randomised controlled trials
deliver the highest level of evidence to answer
research questions. The paradigm of a randomised study
design has also been applied to diagnostic research. We
described that a randomised study design is not always
necessary to evaluate the value of a diagnostic test to
change patient outcome. A test's effect on patient
outcome can be inferred and indeed considered as
quantified -using decision analysis- 1) if the test is
meant to include or exclude a disease for which an
established reference is available, 2) if a
cross-sectional accuracy study has shown the test's
ability to adequately detect the presence or absence of
that disease based on the reference, and finally 3) if
proper, randomised therapeutic studies have provided
evidence on efficacy of the optimal management of this
disease. In such instances diagnostic research does not
require an additional randomised comparison between two
(or more) 'test-treatment strategies' (one with and one
without the test under study) to establish the test's
effect on patient outcome. Accordingly, diagnostic
research -including the quantification of the effects
of diagnostic testing on patient outcome- may be
executed more efficiently.
Diagnostic research aims to quantify a test's added
contribution given other diagnostic information
available to the physician in determining the presence
or absence of a particular disease. Commonly,
diagnostic prediction rules use dichotomous logistic
regression analysis to predict the presence or absence
of a disease. We showed that genetic programming and
polytomous modelling are promising alternatives for the
conventional dichotomous logistic regression analysis
to develop diagnostic prediction rules. The main
advantage of genetic programming is the possibility to
create more flexible models with better discrimination.
This is especially important in large data sets in
which complex interactions between predictors and
outcomes may be present.",
abstract = "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.",
notes = "* 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.",
}
@InProceedings{1274004,
author = "Franck Binard and Amy Felty",
title = "An abstraction-based genetic programming system",
booktitle = "Late breaking paper at Genetic and Evolutionary
Computation Conference {(GECCO'2007)}",
year = "2007",
month = "7-11 " # jul,
editor = "Peter A. N. Bosman",
isbn13 = "978-1-59593-698-1",
pages = "2415--2422",
address = "London, United Kingdom",
keywords = "genetic algorithms, genetic programming, lambda
calculus, languages, polymorphism, types",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2415.pdf",
doi = "doi:10.1145/1274000.1274004",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Distributed on CD-ROM at GECCO-2007 ACM Order No.
910071",
}
@InProceedings{Binard:2008:gecco,
author = "Franck Binard and Amy Felty",
title = "Genetic programming with polymorphic types and
higher-order functions",
booktitle = "GECCO '08: Proceedings of the 10th annual conference
on Genetic and evolutionary computation",
year = "2008",
editor = "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",
isbn13 = "978-1-60558-130-9",
pages = "1187--1194",
address = "Atlanta, GA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1187.pdf",
doi = "doi:10.1145/1389095.1389330",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "12-16 " # jul,
keywords = "genetic algorithms, genetic programming, lambda
calculus, polymorphism, types",
notes = "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 \cite{1389330}",
}
@InProceedings{Bing:2010:ETCS,
author = "Wu Bing and Zhang Wen-qiong and Liang Jia-hong",
title = "A Genetic Multiple Kernel Relevance Vector Regression
Approach",
booktitle = "Second International Workshop on Education Technology
and Computer Science (ETCS), 2010",
year = "2010",
month = mar,
volume = "3",
pages = "52--55",
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/ETCS.2010.154",
notes = "Not a GP, fixed representation. Also known as
\cite{5460012}",
}
@Article{Birchenhall:1995:EJ,
author = "C. R. Birchenhall",
copyright = "Copyright 1995 Royal Economic Society",
ISSN = "00130133",
journal = "The Economic Journal",
number = "430",
owner = "wlangdon",
pages = "788--795",
title = "Genetic Algorithms, Classifier Systems and Genetic
Programming and their Use in the Models of Adaptive
Behaviour and Learning",
URL = "http://links.jstor.org/sici?sici=0013-0133%28199505%29105%3A430%3C788%3AGACSAG%3E2.0.CO%3B2-%23",
volume = "105",
year = "1995",
keywords = "genetic algorithms, genetic programming",
notes = "Reviewed? in The Economic Journal, vol 106 number 434,
1996 APPROX pages 271",
}
@InCollection{oai:CiteSeerPSU:397549,
title = "Schemas and Genetic Programming",
author = "Andreas Birk and Wolfgang J. Paul",
booktitle = "Prerational Intelligence: Adaptive Behavior and
Intelligent Systems Without Symbols and Logic {II}",
publisher = "Kluwer",
year = "2001",
editor = "Holk Cruse and Jeffrey Dean and Helge Ritter",
volume = "26",
series = "Studies in Cognitive Systems",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7923-6666-2",
URL = "http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-135-22-33673255-0,00.html",
URL = "http://www.faculty.iu-bremen.de/birk/publications/schemas_genetic_programming.pdf",
URL = "http://arti.vub.ac.be/~cyrano/PUBLICATIONS/schema_gp00.ps.gz",
URL = "http://citeseer.ist.psu.edu/397549.html",
citeseer-isreferencedby = "oai:CiteSeerPSU:106696;
oai:CiteSeerPSU:67434; oai:CiteSeerPSU:532836;
oai:CiteSeerPSU:86635; oai:CiteSeerPSU:54193;
oai:CiteSeerPSU:315750; oai:CiteSeerPSU:89833;
oai:CiteSeerPSU:66393; oai:CiteSeerPSU:226046;
oai:CiteSeerPSU:360779; oai:CiteSeerPSU:193774",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:397549",
rights = "unrestricted",
abstract = "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.",
notes = "schemas_genetic_programming.pdf crashes my browser",
size = "13 pages",
}
@InProceedings{Birtolo:2010:ICEIS,
author = "Cosimo Birtolo and Roberto Armenise and Luigi
Troiano",
title = "Supporting Menu Layout Design by Genetic Programming",
booktitle = "Proceedings of the 12th International Conference on
Enterprise Information Systems (ICEIS 2010)",
year = "2010",
editor = "Joaquim Filipe and Jos{\'e} Cordeiro",
address = "Funchal, Madeira, Portugal",
month = "8 - 12 " # jun,
keywords = "genetic algorithms, genetic programming: poster",
abstract = "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.",
notes = "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",
}
@InProceedings{bisat:1998:ussbctn,
author = "Mona T. Bisat and Charles W. Richter and Gerald B.
Sheble",
title = "Using Adaptive Agents to Study Bilateral Contracts and
Trade Networks",
booktitle = "Late Breaking Papers at the Genetic Programming 1998
Conference",
year = "1998",
editor = "John R. Koza",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "Stanford, CA, USA",
month = "22-25 " # jul,
publisher = "Stanford University Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "GP-98LB",
}
@Article{Bishop96,
author = "P. Bishop and R. Bloomfield",
title = "Conservative theory for long-term reliability-growth
prediction [of software]",
journal = "IEEE Transactions on Reliability",
volume = "45",
number = "4",
month = dec,
pages = "550--560",
notes = "Theoretical or Mathematical",
address = "Adelard, London, UK",
year = "1996",
ISSN = "0018-9529",
URL = "http://ieeexplore.ieee.org/iel1/24/12134/00556578.pdf?isNumber=12134&prod=JNL&arnumber=556578&arSt=550&ared=560&arAuthor=Bishop%2C+P.%3B+Bloomfield%2C+R.",
URL = "http://www.adelard.co.uk/resources/papers/pdf/issre96m.pdf",
abstract = "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.",
keywords = "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",
notes = "cf. \cite{brady:murphy}",
}
@InProceedings{conf/softcomp/BittencourtSLAO10,
title = "The Gene Expression Programming Applied to Demand
Forecast",
author = "Evandro Bittencourt and Sidney Schossland and Raul
Landmann and Denio {Murilo de Aguiar} and Adilson Gomes
{De Oliveira}",
bibdate = "2010-11-04",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/softcomp/soco2010.html#BittencourtSLAO10",
booktitle = "Soft Computing Models in Industrial and Environmental
Applications, 5th International Workshop ({SOCO} 2010),
Guimar{\~a}es, Portugal, June 2010",
publisher = "Springer",
year = "2010",
volume = "73",
editor = "Emilio Corchado and Paulo Novais and Cesar Analide and
Javier Sedano",
isbn13 = "978-3-642-13160-8",
pages = "197--200",
series = "Advances in Soft Computing",
keywords = "genetic algorithms, genetic programming, gene
expression programming",
URL = "http://dx.doi.org/10.1007/978-3-642-13161-5",
}
@InProceedings{Blasco:2010:ARES,
author = "Jorge Blasco and Agustin Orfila and Arturo Ribagorda",
title = "Improving Network Intrusion Detection by Means of
Domain-Aware Genetic Programming",
booktitle = "International Conference on Availability, Reliability,
and Security, ARES '10",
year = "2010",
month = feb,
pages = "327--332",
abstract = "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.",
keywords = "genetic algorithms, genetic programming, domain-aware
genetic programming, fitness function, intrusive
traffic, network intrusion detection, normal traffic,
security of data",
doi = "doi:10.1109/ARES.2010.53",
notes = "Also known as \cite{5438073}",
}
@InProceedings{bleuler:2001:mgprbus,
author = "Stefan Bleuler and Martin Brack and Lothar Thiele and
Eckart Zitzler",
title = "Multiobjective Genetic Programming: Reducing Bloat
Using {SPEA2}",
booktitle = "Proceedings of the 2001 Congress on Evolutionary
Computation CEC2001",
year = "2001",
pages = "536--543",
address = "COEX, World Trade Center, 159 Samseong-dong,
Gangnam-gu, Seoul, Korea",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "27-30 " # may,
organisation = "IEEE Neural Network Council (NNC), Evolutionary
Programming Society (EPS), Institution of Electrical
Engineers (IEE)",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, SPEA, SPEA2,
Pareto, external set",
ISBN = "0-7803-6658-1",
URL = "ftp://ftp.tik.ee.ethz.ch/pub/people/zitzler/BBTZ2001b.ps.gz",
URL = "http://citeseer.ist.psu.edu/443099.html",
size = "8 pages",
abstract = "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.",
notes = "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.",
}
@InCollection{Bleuler:2008:MPSN,
author = "Stefan Bleuler and Johannes Bader and Eckart Zitzler",
title = "Reducing Bloat in {GP} with Multiple Objectives",
booktitle = "Multiobjective Problem Solving from Nature: from
concepts to applications",
publisher = "Springer",
year = "2008",
editor = "Joshua Knowles and David Corne and Kalyanmoy Deb",
series = "Natural Computing",
chapter = "9",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-540-72963-1",
notes = "http://www.springer.com/west/home/computer/artificial?SGWID=4-147-22-173745027-0",
}
@InProceedings{BT94,
author = "Tobias Blickle and Lothar Thiele",
title = "Genetic Programming and Redundancy",
booktitle = "Genetic Algorithms within the Framework of
Evolutionary Computation (Workshop at KI-94,
Saarbr{\"u}cken)",
editor = "J. Hopf",
publisher = "Max-Planck-Institut f{\"u}r Informatik
(MPI-I-94-241)",
address = "
Im Stadtwald, Building 44, D-66123 Saarbr{\"u}cken,
Germany
",
pages = "33--38",
year = "1994",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.tik.ee.ethz.ch/~tec/publications/bt94/GPandRedundancy.ps.gz",
size = "6 pages",
notes = "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.
",
}
@TechReport{blickle:1995:css,
author = "Tobias Blickle and Lothar Thiele",
title = "A Comparison of Selection Schemes Used in Genetic
Algorithms",
institution = "TIK Institut fur Technische Informatik und
Kommunikationsnetze, Computer Engineering and Networks
Laboratory, ETH, Swiss Federal Institute of
Technology",
year = "1995",
type = "TIK-Report",
number = "11",
edition = "2",
address = "Gloriastrasse 35, 8092 Zurich, Switzerland",
month = dec,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.handshake.de/user/blickle/publications/TIK-Report11abstract.html",
URL = "http://www.handshake.de/user/blickle/publications/tik-report11_v2.ps",
abstract = "
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.",
notes = "
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
",
size = "65 pages",
}
@Article{blickle:1995:ea,
author = "Tobias Blickle",
title = "Optimieren nach dem Vorbild der Natur, Evolutionare
Algorithmen",
journal = "Bulletin SEV/VSE",
year = "1995",
volume = "86",
number = "25",
pages = "21--26",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.handshake.de/user/blickle/publications/EA.ps",
size = "6 pages",
notes = "Introduction to GA and GP in German",
}
@TechReport{blickle:1995:YAGPLIC,
author = "Tobias Blickle",
title = "{YAGPLIC} User Manual",
institution = "Computer Engineering and Communication Network Lab
(TIK), Swiss Federal Institute of Technology (ETH)",
year = "1995",
address = "Gloriastrasse 35, CH-8092, Zurich",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.tik.ee.ethz.ch/~blickle/YAGPLIC.html
broken",
notes = "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.",
}
@TechReport{blickle:1996:ecs,
author = "Tobias Blickle",
title = "Evolving Compact Solutions in Genetic Programming: {A}
Case Study",
institution = "TIK Institut fur Technische Informatik und
Kommunikationsnetze, Computer Engineering and Networks
Laboratory, ETH, Swiss Federal Institute of
Technology",
year = "1996",
type = "TIK-Report",
address = "Gloriastrasse 35, 8092 Zurich, Switzerland",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.handshake.de/user/blickle/publications/ppsn1.ps",
abstract = "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.",
notes = "Presented at PPSN 4
",
size = "10 pages",
}
@InProceedings{blickle96,
author = "Tobias Blickle",
title = "Evolving Compact Solutions in Genetic Programming: {A}
Case Study",
editor = "Hans-Michael Voigt and Werner Ebeling and Ingo
Rechenberg and Hans-Paul Schwefel",
booktitle = "Parallel Problem Solving From Nature IV. Proceedings
of the International Conference on Evolutionary
Computation",
year = "1996",
publisher = "Springer-Verlag",
volume = "1141",
series = "LNCS",
pages = "564--573",
address = "Berlin, Germany",
publisher_address = "Heidelberg, Germany",
month = "22-26 " # sep,
keywords = "genetic algorithms, genetic programming, bloat,
deleting crossover",
ISBN = "3-540-61723-X",
URL = "http://www.handshake.de/user/blickle/publications/ppsn1.ps",
URL = "http://citeseer.ist.psu.edu/blickle96evolving.html",
doi = "doi:10.1007/3-540-61723-X_1020",
size = "10 pages",
abstract = "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.",
notes = "http://lautaro.fb10.tu-berlin.de/ppsniv.html
PPSN4
same as \cite{blickle: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.",
affiliation = "Swiss Federal Institute of Technology (ETH) Computer
Engineering and Communication Networks Lab (TIK)
Gloriastrasse 35 8092 Zurich Switzerland Gloriastrasse
35 8092 Zurich Switzerland",
}
@PhdThesis{blickle:thesis,
author = "Tobias Blickle",
title = "Theory of Evolutionary Algorithms and Application to
System Synthesis",
school = "Swiss Federal Institute of Technology",
year = "1996",
address = "Zurich",
publisher = "vdf Verlag",
publisher_address = "CH-8092 Zurich",
month = nov,
keywords = "genetic algorithms, genetic programming",
ISBN = "3-7281-2433-8",
URL = "http://www.handshake.de/user/blickle/publications/diss.pdf",
size = "272 pages",
abstract = "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.",
notes = "Of special interest for this community might be
chapter 5 that deals with recombination and bloating in
GP YAGPLIC",
}
@Article{DBLP:journals/ec/BlickleT96,
author = "Tobias Blickle and Lothar Thiele",
title = "A Comparison of Selection Schemes used in Evolutionary
Algorithms",
journal = "Evolutionary Computation",
volume = "4",
number = "4",
year = "1996",
pages = "361--394",
bibsource = "DBLP, http://dblp.uni-trier.de",
month = "Winter",
keywords = "genetic algorithms, genetic programming, Selection,
evolutionary algorithms, diversity, selection
intensity, tournament selection, truncation selection,
linear ranking",
ISSN = "1063-6560",
URL = "http://www.handshake.de/user/blickle/publications/ECfinal.ps",
doi = "doi:10.1162/evco.1996.4.4.361",
size = "34 pages",
abstract = "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.",
notes = "Brief use of GP symbolic regression to find nice
formulae. Theoretical analysis.
NB see \cite{DBLP:journals/ec/Motoki02} for update on
loss of diversity under tournament selection",
}
@InProceedings{blume:2000:ocfromsgesGLEAM,
author = "Christian Blume",
title = "Optimized Collision Free Robot Move Statement
Generation by the Evolutionary Software {GLEAM}",
booktitle = "Real-World Applications of Evolutionary Computing",
year = "2000",
editor = "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",
volume = "1803",
series = "LNCS",
pages = "327--328",
address = "Edinburgh",
publisher_address = "Berlin",
month = "17 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, Industrial
Machining Robots",
ISBN = "3-540-67353-9",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1803&spage=327",
notes = "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",
}
@InCollection{bobrovnikoff:2000:SEISP,
author = "Dmitri Bobrovnikoff",
title = "SoccerBots: Evolving Intelligent Soccer Players",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2000",
year = "2000",
editor = "John R. Koza",
pages = "40--45",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "part of \cite{koza:2000:gagp}",
}
@InProceedings{boden:1996:tsobGA,
author = "Edward B. Boden and Gilford F. Martino",
title = "Testing Software using Order-Based Genetic
Algorithms",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "Genetic Algorithms",
pages = "461--466",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96 GA paper",
}
@InProceedings{boettcher:1999:EOMC,
author = "Stefan Boettcher and Allon G. Percus",
title = "Extremal Optimization: Methods derived from
Co-Evolution",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "825--832",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "evolution strategies and evolutionary programming",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/9904056.pdf",
URL = "http://xxx.lanl.gov/abs/math.OC/9904056",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{Boetticher:2006:IRI,
author = "G. D. Boetticher and K. Kaminsky",
title = "The Assessment and Application of Lineage Information
in Genetic Programs for Producing Better Models",
booktitle = "IEEE International Conference on Information Reuse and
Integration",
year = "2006",
pages = "141--146",
address = "Waikoloa Village, HI, USA",
month = sep,
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-9788-6",
doi = "doi:10.1109/IRI.2006.252403",
abstract = "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",
notes = "Houston Univ., TX",
}
@InProceedings{bohm:1996:eui,
author = "Walter Bohm and Andreas Geyer-Schulz",
title = "Exact Uniform Initialization for Genetic Programming",
booktitle = "Foundations of Genetic Algorithms IV",
year = "1996",
editor = "Richard K. Belew and Michael Vose",
pages = "379--407",
address = "University of San Diego, CA, USA",
publisher_address = "San Francisco, California, USA",
month = "3--5 " # aug,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-460-X",
notes = "FOGA4 k-bounded context-free languages May also use
key Boehm96 Demonstrated on XOR problem",
}
@InProceedings{bojarczuk:1999:DGP,
author = "Celia C. Bojarczuk and Heitor S. Lopes and Alex A.
Freitas",
title = "Discovering comprehensible classification rules by
using Genetic Programming: a case study in a medical
domain",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "953--958",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, data mining,
classification, medical applications",
ISBN = "1-55860-611-4",
URL = "http://www.cs.kent.ac.uk/people/staff/aaf/pub_papers.dir/gecco99.ps",
URL = "http://www.cs.kent.ac.uk/people/staff/aaf/my-publications-ukc.html",
URL = "http://citeseer.ist.psu.edu/340269.html",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-417.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-417.ps",
abstract = "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.",
notes = "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 \cite{bojarczuk:2000:kdcp}",
}
@Article{bojarczuk:2000:kdcp,
author = "Celia C. Bojarczuk and Heitor S. Lopes and Alex A.
Freitas",
title = "Genetic programming for knowledge discovery in
chest-pain diagnosis",
journal = "IEEE Engineering in Medicine and Biology Magazine",
year = "2000",
volume = "19",
number = "4",
pages = "38--44",
month = jul # "-" # aug,
keywords = "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",
ISSN = "0739-5175",
URL = "http://ieeexplore.ieee.org/iel5/51/18543/00853480.pdf",
URL = "http://www.cs.kent.ac.uk/people/staff/aaf/my-publications-ukc.html",
URL = "http://www.cs.kent.ac.uk/people/staff/aaf/pub_papers.dir/IEEE-EMB-2000.ps",
URL = "http://citeseer.ist.psu.edu/459907.html",
size = "7 pages",
abstract = "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.",
notes = "lilgp",
}
@InProceedings{bojarczuk:2001:idamap,
author = "Celia C. Bojarczuk and Heitor S. Lopes and Alex A.
Freitas",
title = "Data mining with constrained-syntax genetic
programming: applications to medical data sets",
booktitle = "Proceedings Intelligent Data Analysis in Medicine and
Pharmacology (IDAMAP-2001)",
year = "2001",
note = "a workshop at MedInfo-2001",
keywords = "genetic algorithms, genetic programming, data mining,
classification, medical applications,
Constrained-Syntax Genetic Programming",
URL = "http://www.ailab.si/idamap/idamap2001/papers/bojarczuk.pdf",
URL = "http://www.cs.kent.ac.uk/people/staff/aaf/my-publications-ukc.html",
URL = "http://www.cs.kent.ac.uk/people/staff/aaf/pub_papers.dir/IDAMAP-2001.ps",
URL = "http://citeseer.ist.psu.edu/459555.html",
abstract = "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.",
notes = "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).
",
}
@InProceedings{bojarczuk03,
author = "Celia C. Bojarczuk and Heitor S. Lopes and Alex A.
Freitas",
title = "An innovative application of a constrained-syntax
genetic programming system to the problem of predicting
survival of patients",
booktitle = "Genetic Programming, Proceedings of EuroGP'2003",
year = "2003",
editor = "Conor Ryan and Terence Soule and Maarten Keijzer and
Edward Tsang and Riccardo Poli and Ernesto Costa",
volume = "2610",
series = "LNCS",
pages = "11--21",
address = "Essex",
publisher_address = "Berlin",
month = "14-16 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, data mining,
classification, medical applications",
URL = "http://www.cs.kent.ac.uk/people/staff/aaf/my-publications-ukc.html",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=11",
ISBN = "3-540-00971-X",
abstract = "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",
notes = "EuroGP'2003 held in conjunction with EvoWorkshops
2003",
}
@Article{bojarczuk:2004:EMBM,
author = "Celia C. Bojarczuk and Heitor S. Lopes and Alex A.
Freitas and Edson L Michalkiewicz",
title = "A constrained-syntax genetic programming system for
discovering classification rules: application to
medical data sets",
journal = "Artificial Intelligence in Medicine",
year = "2004",
volume = "30",
number = "1",
pages = "27--48",
month = jan,
ISSN = "0933-3657",
keywords = "genetic algorithms, genetic programming, data mining,
classification, medical applications",
URL = "http://www.cs.kent.ac.uk/people/staff/aaf/my-publications-ukc.html",
URL = "http://www.cpgei.cefetpr.br/~hslopes/publicacoes/2004/aim2004.pdf",
URL = "http://www.sciencedirect.com/science/article/B6T4K-4B42BDH-1/2/77bc597c3188977bd9ffb40ba10802ac",
URL = "http://www.harcourt-international.com/journals/aiim/",
doi = "doi:10.1016/j.artmed.2003.06.001",
abstract = "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.",
}
@InProceedings{bolis:2001:EuroGP,
author = "Enzo Bolis and Christian Zerbi and Pierre Collet and
Jean Louchet and Evelyne Lutton",
title = "A {GP} Artificial Ant for image processing:
preliminary experiments with {EASEA}",
booktitle = "Genetic Programming, Proceedings of EuroGP'2001",
year = "2001",
editor = "Julian F. Miller and Marco Tomassini and Pier Luca
Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
William B. Langdon",
volume = "2038",
series = "LNCS",
pages = "246--255",
address = "Lake Como, Italy",
publisher_address = "Berlin",
month = "18-20 " # apr,
organisation = "EvoNET",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, Image
processing, Contour detection, EASEA, Animat",
ISBN = "3-540-41899-7",
URL = "http://minimum.inria.fr/evo-lab/Publications/EuroGPFinal.ps.gz",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=246",
size = "10 pages",
abstract = "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.",
notes = "EuroGP'2001, part of \cite{miller:2001:gp}",
}
@InProceedings{Bollegala:2011:GECCO,
author = "Danushka Bollegala and Nasimul Noman and Hitoshi Iba",
title = "{RankDE:} learning a ranking function for information
retrieval using differential evolution",
booktitle = "GECCO '11: Proceedings of the 13th annual conference
on Genetic and evolutionary computation",
year = "2011",
editor = "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",
isbn13 = "978-1-4503-0557-0",
pages = "1771--1778",
keywords = "genetic algorithms, genetic programming, Real world
applications",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001576.2001814",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{2001814} GECCO-2011 A joint
meeting of the twentieth international conference on
genetic algorithms (ICGA-2011) and the sixteenth annual
genetic programming conference (GP-2011)",
}
@InProceedings{bollini:1999:dpEAdp,
author = "Alessandro Bollini and Marco Piastra",
title = "Distributed and Persistent Evolutionary Algorithms: a
Design Pattern",
booktitle = "Genetic Programming, Proceedings of EuroGP'99",
year = "1999",
editor = "Riccardo Poli and Peter Nordin and William B. Langdon
and Terence C. Fogarty",
volume = "1598",
series = "LNCS",
pages = "173--183",
address = "Goteborg, Sweden",
publisher_address = "Berlin",
month = "26-27 " # may,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-65899-8",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1598&spage=173",
notes = "EuroGP'99, part of \cite{poli:1999:GP}
Java objectstore database",
}
@InProceedings{bollini:1999:A,
author = "Alessandro Bollini and Marco Piastra",
title = "A persistent blackboard for distributed evolutionary
computation",
booktitle = "Late Breaking Papers at the 1999 Genetic and
Evolutionary Computation Conference",
year = "1999",
editor = "Scott Brave and Annie S. Wu",
pages = "48--56",
address = "Orlando, Florida, USA",
month = "13 " # jul,
keywords = "Java",
notes = "GECCO-99LB",
}
@InProceedings{bonarini:1999:CRLAACFLCS,
author = "Andrea Bonarini",
title = "Comparing Reinforcement Learning Algorithms Applied to
Crisp and Fuzzy Learning Classifier Systems",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "52--59",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-876.pdf",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{conf/icnc/BonfimC05,
title = "FranksTree: {A} Genetic Programming Approach to Evolve
Derived Bracketed {L-systems}",
author = "Danilo Mattos Bonfim and Leandro Nunes {de Castro}",
year = "2005",
pages = "1275--1278",
editor = "Lipo Wang and Ke Chen and Yew-Soon Ong",
booktitle = "Advances in Natural Computation, First International
Conference, ICNC 2005, Proceedings, Part II",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3611",
address = "Changsha, China",
month = aug # " 27-29",
bibdate = "2005-07-29",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/icnc/icnc2005-1.html#BonfimC05",
keywords = "genetic algorithms, genetic programming, interactive
evolution",
ISBN = "3-540-28325-0",
doi = "doi:10.1007/11539087_168",
size = "4 pages",
abstract = "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.",
notes = "
Crossover based on identifying branches in pictures? No
mutation. population=6",
}
@InProceedings{bongard:1999:ECAL,
author = "Josh C. Bongard",
title = "Coevolutionary Dynamics of a Multi-population Genetic
Programming System",
booktitle = "Advances in Artificial Life",
year = "1999",
editor = "D. Floreano and J.-D. Nicoud and F. Mondada",
volume = "1674",
series = "LNAI",
pages = "154",
address = "Lausanne",
month = "13-17 " # sep,
publisher = "Springer Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-66452-1",
URL = "http://www.cs.uvm.edu/~jbongard/papers/s067.ps.gz",
URL = "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-66452-1",
URL = "http://citeseer.ist.psu.edu/319504.html",
notes = "ECAL-99",
}
@InProceedings{bongard:2000:legion,
author = "Josh C. Bongard",
title = "The Legion System: {A} Novel Approach to Evolving
Heterogeneity for Collective Problem Solving",
booktitle = "Genetic Programming, Proceedings of EuroGP'2000",
year = "2000",
editor = "Riccardo Poli and Wolfgang Banzhaf and William B.
Langdon and Julian F. Miller and Peter Nordin and
Terence C. Fogarty",
volume = "1802",
series = "LNCS",
pages = "16--28",
address = "Edinburgh",
publisher_address = "Berlin",
month = "15-16 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-67339-3",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=16",
abstract = "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.",
notes = "EuroGP'2000, part of \cite{poli:2000:GP}",
}
@Article{Bongard:2007:PNAS,
author = "Josh Bongard and Hod Lipson",
title = "Automated reverse engineering of nonlinear dynamical
systems",
journal = "PNAS, Proceedings of the National Academy of Sciences
of the United States of America",
year = "2007",
volume = "104",
number = "24",
pages = "9943--9948",
month = "12 " # jun,
keywords = "genetic algorithms, genetic programming, Physical
Sciences, Computer Sciences, coevolution, modelling,
symbolic identification",
doi = "doi:10.1073/pnas.0609476104",
size = "6 pages",
abstract = "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.",
notes = "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",
}
@Article{Bongard:2009:TEC,
author = "Josh C. Bongard",
title = "Accelerating Self-Modeling in Cooperative Robot
Teams",
journal = "IEEE Transactions on Evolutionary Computation",
year = "2009",
volume = "13",
number = "2",
pages = "321--332",
month = apr,
keywords = "genetic algorithms, genetic programming, Robots, Robot
sensing systems, Training data, Sensors, Data models,
Service robots, Computational modeling, self-modeling,
Collective robotics, evolutionary robotics",
doi = "doi:10.1109/TEVC.2008.927236",
abstract = "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.",
}
@InCollection{Bongard:2009:GPTP,
author = "Josh Bongard",
title = "A Functional Crossover Operator for Genetic
Programming",
booktitle = "Genetic Programming Theory and Practice {VII}",
year = "2009",
editor = "Rick L. Riolo and Una-May O'Reilly and Trent
McConaghy",
series = "Genetic and Evolutionary Computation",
address = "Ann Arbor",
month = "14-16 " # may,
publisher = "Springer",
chapter = "12",
pages = "195--210",
keywords = "genetic algorithms, genetic programming, homologous
crossover, crossover operators, system identification",
notes = "part of \cite{Riolo:2009:GPTP}",
}
@InProceedings{Bongard:2010:gecco,
author = "Josh C. Bongard",
title = "A probabilistic functional crossover operator for
genetic programming",
booktitle = "GECCO '10: Proceedings of the 12th annual conference
on Genetic and evolutionary computation",
year = "2010",
editor = "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",
isbn13 = "978-1-4503-0072-8",
pages = "925--932",
keywords = "genetic algorithms, genetic programming",
month = "7-11 " # jul,
organisation = "SIGEVO",
address = "Portland, Oregon, USA",
doi = "doi:10.1145/1830483.1830649",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{1830649} GECCO-2010 A joint
meeting of the nineteenth international conference on
genetic algorithms (ICGA-2010) and the fifteenth annual
genetic programming conference (GP-2010)",
}
@Article{Bongard:2012:ieeetec,
author = "Josh C. Bongard",
title = "Innocent Until Proven Guilty: Reducing Robot Shaping
from Polynomial to Linear Time",
journal = "IEEE Transactions on Evolutionary Computation",
year = "2011",
volume = "15",
number = "4",
pages = "571--585",
month = aug,
keywords = "genetic algorithms, genetic programming, Early
stopping, Evolutionary computation, Joints,
Manipulators, Neurons, Robot sensing systems,
evolutionary robotics",
ISSN = "1089-778X",
doi = "doi:10.1109/TEVC.2010.2096540",
abstract = "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.",
notes = "Also known as \cite{5703121}",
}
@InProceedings{bonham:1999:AIEEWCOGA,
author = "Christopher R. Bonham and Ian C. Parmee",
title = "An Investigation of Exploration and Exploitation
Within Cluster Oriented Genetic Algorithms ({COGA}s)",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1491--1497",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "real world applications",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-765.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-765.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{Bonilla:2011:LASCAS,
author = "Cesar Pedraza Bonilla and Carlos Ivan Camargo",
title = "Low Cost Platform for Evolvable-Based {Boolean}
Synthesis",
booktitle = "IEEE Second Latin American Symposium on Circuits and
Systems (LASCAS), 2011",
year = "2011",
month = feb,
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/LASCAS.2011.5750310",
notes = "Also known as \cite{5750310}",
}
@InProceedings{Bonte:2010:AIAI,
author = "Bert Bonte and Bart Wyns",
title = "Automatically Designing Robot Controllers and Sensor
Morphology with Genetic Programming",
booktitle = "6th IFIP Advances in Information and Communication
Technology AIAI 2010",
year = "2010",
editor = "Harris Papadopoulos and Andreas Andreou and Max
Bramer",
volume = "339",
series = "IFIP Advances in Information and Communication
Technology",
pages = "86--93",
address = "Larnaca, Cyprus",
month = oct # " 6-7",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1007/978-3-642-16239-8_14",
abstract = "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.",
affiliation = "Department of Electrical Energy, Systems and
Automation, Ghent University, Technologiepark 913, 9052
Zwijnaarde, Belgium",
notes = "http://www.cs.ucy.ac.cy/aiai2010/",
}
@InCollection{booker:2000:EC1,
author = "Lashon B. Booker and David B. Fogel and Darrell
Whitley and Peter J. Angeline and A. E. Eiben",
title = "Recombination",
booktitle = "Evolutionary Computation 1 Basic Algorithms and
Operators",
publisher = "Institute of Physics Publishing",
year = "2000",
editor = "Thomas Baeck and David B. Fogel and Zbigniew
Michalewicz",
chapter = "33",
pages = "256--307",
address = "Bristol",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7503-0664-5",
notes = "http://www.crcpress.com/shopping_cart/products/product_detail.asp?sku=IP274
section 33.5 parse trees p286--289",
size = "52 pages",
}
@InProceedings{booth:2004:eurogp,
author = "Richard F. Booth and Alexandre V. Borovik",
title = "Coevolution of Algorithms and Deterministic Solutions
of Equations in Free Groups",
booktitle = "Genetic Programming 7th European Conference, EuroGP
2004, Proceedings",
year = "2004",
editor = "Maarten Keijzer and Una-May O'Reilly and Simon M.
Lucas and Ernesto Costa and Terence Soule",
volume = "3003",
series = "LNCS",
pages = "11--22",
address = "Coimbra, Portugal",
publisher_address = "Berlin",
month = "5-7 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-21346-5",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=11",
abstract = "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.",
notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in
conjunction with EvoCOP2004 and EvoWorkshops2004",
}
@InProceedings{Borges:2010:gecco,
author = "Cruz E. Borges and Cesar L. Alonso and Jose L.
Montana",
title = "Model selection in genetic programming",
booktitle = "GECCO '10: Proceedings of the 12th annual conference
on Genetic and evolutionary computation",
year = "2010",
editor = "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",
isbn13 = "978-1-4503-0072-8",
pages = "985--986",
keywords = "genetic algorithms, genetic programming, Poster",
month = "7-11 " # jul,
organisation = "SIGEVO",
address = "Portland, Oregon, USA",
doi = "doi:10.1145/1830483.1830662",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{1830662} GECCO-2010 A joint
meeting of the nineteenth international conference on
genetic algorithms (ICGA-2010) and the fifteenth annual
genetic programming conference (GP-2010)",
}
@InProceedings{Borges:2010:ICEC,
author = "Cruz Enrique Borges and Cesar L. Alonso and Jose Luis
Montana",
title = "Coevolutionary Architectures with Straight Line
Programs for solving the Symbolic Regression Problem",
booktitle = "Proceedings of the International Conference on
Evolutionary Computation (ICEC 2010)",
year = "2010",
editor = "Agostinho Rosa",
pages = "Paper Nr: 37",
address = "Valencia, Spain",
month = "24-26 " # oct,
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "http://www.icec.ijcci.org/ICEC2010/home.asp
http://www.ecta.ijcci.org/Abstracts/2010/ICEC_2010_Abstracts.htm",
}
@InProceedings{Boric:2007:cec,
author = "Neven Boric and Pablo A. Estevez",
title = "Genetic Programming-Based Clustering Using an
Information Theoretic Fitness Measure",
booktitle = "2007 IEEE Congress on Evolutionary Computation",
year = "2007",
editor = "Dipti Srinivasan and Lipo Wang",
pages = "31--38",
address = "Singapore",
month = "25-28 " # sep,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "1-4244-1340-0",
file = "1285.pdf",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and
the IET.
IEEE Catalog Number: 07TH8963C",
}
@Article{Borrelli:2006:PhysicaA,
author = "A. Borrelli and I. {De Falco} and A. {Della Cioppa}
and M. Nicodemi and G. Trautteura",
title = "Performance of genetic programming to extract the
trend in noisy data series",
journal = "Physica A: Statistical and Theoretical Physics",
year = "2006",
volume = "370",
number = "1",
pages = "104--108",
month = "1 " # oct,
note = "Econophysics Colloquium - Proceedings of the
International Conference {"}Econophysics
Colloquium{"}",
keywords = "genetic algorithms, genetic programming,
Multiobjective genetic programming, Stochastic time
series",
doi = "doi:10.1016/j.physa.2006.04.025",
abstract = "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.",
}
@InProceedings{boryczka:2002:gecco,
author = "Mariusz Boryczka and Zbigniew J. Czech",
title = "Solving Approximation Problems By Ant Colony
Programming",
booktitle = "GECCO 2002: Proceedings of the Genetic and
Evolutionary Computation Conference",
editor = "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",
year = "2002",
pages = "133",
address = "New York",
publisher_address = "San Francisco, CA 94104, USA",
month = "9-13 " # jul,
publisher = "Morgan Kaufmann Publishers",
keywords = "genetic algorithms, genetic programming, artificial
life, adaptive behavior, agents, ant colony
optimization, poster paper, ant colony programming,
approximation problems, automatic programming",
ISBN = "1-55860-878-8",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/aaaa288.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/aaaa288.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-02.pdf",
notes = "GECCO-2002. A joint meeting of the eleventh
International Conference on Genetic Algorithms
(ICGA-2002) and the seventh Annual Genetic Programming
Conference (GP-2002)",
}
@InProceedings{boryczka:2002:gecco:lbp,
title = "Solving Approximation Problems by Ant Colony
Programming",
author = "Mariusz Boryczka and Zbigniew J. Czech",
booktitle = "Late Breaking Papers at the Genetic and Evolutionary
Computation Conference ({GECCO-2002})",
editor = "Erick Cant{\'u}-Paz",
year = "2002",
month = jul,
pages = "39--46",
address = "New York, NY",
publisher = "AAAI",
publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
keywords = "genetic algorithms, genetic programming, automatic
programming, ant colony programming, approximation
problems",
URL = "http://www-zo.iinf.polsl.gliwice.pl/pub/zjc/bc02.ps.Z",
size = "8 pages",
abstract = "A method of automatic programming, called genetic
programming, assumes that the desired program is found
by using a genetic algorithm....",
notes = "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",
}
@Article{Bose20091,
author = "Indranil Bose and Xi Chen",
title = "Quantitative models for direct marketing: {A} review
from systems perspective",
journal = "European Journal of Operational Research",
volume = "195",
number = "1",
pages = "1--16",
year = "2009",
ISSN = "0377-2217",
doi = "doi:10.1016/j.ejor.2008.04.006",
URL = "http://www.sciencedirect.com/science/article/B6VCT-4S7SV3H-3/2/39d97985eecf3aa2b863955e4227cbb0",
keywords = "genetic algorithms, genetic programming, Marketing,
Data mining, Customer profiling, Customer targeting,
Statistical modelling, Performance evaluation",
abstract = "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.",
notes = "Survey",
}
@TechReport{SAND2005-0014,
author = "Mark Boslough and Michael Peters and Arthurine
Pierson",
title = "Graduated Embodiment for Sophisticated Agent Evolution
and Optimization",
institution = "Sandia National Laboratories",
year = "2005",
number = "SAND2005-0014",
address = "P.O. Box 5800, Albuquerque, NM 87185-0318, USA",
month = jan,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.sandia.gov/web1433/pubsagent/Graduated_Embodiment.pdf",
abstract = "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.",
notes = "Unlimited Release
Mark Boslough Michael Peters Evolutionary Computing &
Agent Based Modeling Department
Arthurine Pierson Intelligent Systems Principles
Department",
size = "53 pages",
}
@InProceedings{bosman:1999:LIPIDEA,
author = "Peter A. N. Bosman and Dirk Thierens",
title = "Linkage Information Processing In Distribution
Estimation Algorithms",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "60--67",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-812.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-812.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@TechReport{UUCS2004047,
author = "Peter A. N. Bosman and Edwin D. {de Jong}",
year = "2004",
title = "Grammar Transformations in an {EDA} for Genetic
Programming",
number = "UU-CS-2004-047",
institution = "Department of Information and Computing Sciences,
Utrecht University",
urlpdf = "{http://www.cs.uu.nl/research/techreps/repo/CS-2004/2004-047.pdf}",
pubcat = "techreport",
address = "The Netherlands",
keywords = "genetic algorithms, genetic programming, EDA,
grammar",
URL = "http://www.cs.uu.nl/research/techreps/repo/CS-2004/2004-047.pdf",
URL = "http://www.cs.uu.nl/research/techreps/UU-CS-2004-047.html",
abstract = "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.",
notes = "Royal Tree. See also \cite{bosman:2004:obu:panbos}",
size = "13 pages",
}
@InProceedings{bosman:2004:obu:panbos,
author = "Peter A. N. Bosman and Edwin D. {de Jong}",
title = "Grammar Transformations in an {EDA} for Genetic
Programming",
editor = "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",
booktitle = "GECCO 2004 Workshop Proceedings",
year = "2004",
month = "26-30 " # jun,
address = "Seattle, Washington, USA",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/WOBU001.pdf",
notes = "See also \cite{UUCS2004047} GECCO-2004WKS Distributed
on CD-ROM at GECCO-2004",
}
@InProceedings{Bosman:PPSN:2004,
author = "Peter A. N. Bosman and Edwin D. {de Jong}",
title = "Learning Probabilistic Tree Grammars for Genetic
Programming",
booktitle = "Parallel Problem Solving from Nature - PPSN VIII",
year = "2004",
editor = "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\v{n}o Ata
Kab\'an and Hans-Paul Schwefel",
volume = "3242",
pages = "192--201",
series = "LNCS",
address = "Birmingham, UK",
publisher_address = "Berlin",
month = "18-22 " # sep,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, EDA",
ISBN = "3-540-23092-0",
URL = "http://www.cs.uu.nl/~dejong/publications/edagpppsn.pdf",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3242&spage=192",
doi = "doi:10.1007/b100601",
size = "10 pages",
abstract = "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.",
}
@MastersThesis{bot:1999:masters,
author = "Martijn Bot",
title = "Application of Genetic Programming to the Induction of
Linear Programming Trees",
school = "Vrije Universiteit",
year = "1999",
address = "Amsterdam, The Netherlands",
month = "1 " # jul,
keywords = "genetic algorithms, genetic programming, data mining",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/martijn/verslag.ps.gz",
URL = "http://citeseer.ist.psu.edu/243957.html",
size = "48 pages",
notes = "See also \cite{bot:1999:GPilct},
\cite{bot:2000:GPilct}",
}
@InProceedings{bot:1999:GPilct,
author = "Martijn Bot and William B. Langdon",
title = "Application of Genetic Programming to Induction of
Linear Classification Trees",
booktitle = "Proceedings of the Eleventh Belgium/Netherlands
Conference on Artificial Intelligence (BNAIC'99)",
year = "1999",
editor = "Eric Postma and Marc Gyssens",
pages = "107--114",
address = "Kasteel Vaeshartelt, Maastricht, Holland",
month = "3-4 " # nov,
organisation = "BNVKI, Dutch and the Belgian AI Association",
keywords = "genetic algorithms, genetic programming, data mining",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/martijn/BNAIC99.bot.18aug99.ps.gz",
size = "8 pages",
notes = "http://www.cs.unimaas.nl/~bnvki/",
}
@InProceedings{bot:2000:GPilct,
author = "Martijn C. J. Bot and William B. Langdon",
title = "Application of Genetic Programming to Induction of
Linear Classification Trees",
booktitle = "Genetic Programming, Proceedings of EuroGP'2000",
year = "2000",
editor = "Riccardo Poli and Wolfgang Banzhaf and William B.
Langdon and Julian F. Miller and Peter Nordin and
Terence C. Fogarty",
volume = "1802",
series = "LNCS",
pages = "247--258",
address = "Edinburgh",
publisher_address = "Berlin",
month = "15-16 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-67339-3",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/martijn/bot.eurogp2000.19jan.ps.gz",
URL = "http://citeseer.ist.psu.edu/318695.html",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=247",
doi = "doi:10.1007/978-3-540-46239-2_18",
abstract = "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.",
notes = "See also \cite{bot:1999:GPilct} EuroGP'2000, part of
\cite{poli:2000:GP}",
}
@InProceedings{Bot:2000:GECCO,
author = "Martijn C. J. Bot",
title = "Improving Induction of Linear Classification Trees
with Genetic Programming",
pages = "403--410",
year = "2000",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2000)",
editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz
and Lee Spector and Ian Parmee and Hans-Georg Beyer",
address = "Las Vegas, Nevada, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "10-12 " # jul,
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-708-0",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/GP185.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/martijn/bot.gecco2000.19jan.ps.gz",
URL = "http://citeseer.ist.psu.edu/316984.html",
abstract = "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.",
notes = "A joint meeting of the ninth International Conference
on Genetic Algorithms (ICGA-2000) and the fifth Annual
Genetic Programming Conference (GP-2000) Part of
\cite{whitley:2000:GECCO}",
}
@InProceedings{bot:2001:EuroGP,
author = "Martijn C. J. Bot",
title = "Feature Extraction for the k-Nearest Neighbour
Classifier with Genetic Programming",
booktitle = "Genetic Programming, Proceedings of EuroGP'2001",
year = "2001",
editor = "Julian F. Miller and Marco Tomassini and Pier Luca
Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
William B. Langdon",
volume = "2038",
series = "LNCS",
pages = "256--267",
address = "Lake Como, Italy",
publisher_address = "Berlin",
month = "18-20 " # apr,
organisation = "EvoNET",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, Feature
Extraction, Machine Learning",
ISBN = "3-540-41899-7",
URL = "http://link.springer.de/link/service/series/0558/papers/2038/20380256.pdf",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=256",
size = "12 pages",
abstract = "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%.",
notes = "EuroGP'2001, part of \cite{miller:2001:gp}",
}
@InProceedings{bot:2001:fencgp,
author = "Martijn C. J. Bot",
title = "Feature Extraction for the k-Nearest Neighbour
Classifier with Genetic Programming",
booktitle = "Graduate Student Workshop",
year = "2001",
editor = "Conor Ryan",
pages = "397--400",
address = "San Francisco, California, USA",
month = "7 " # jul,
keywords = "genetic algorithms, genetic programming",
notes = "GECCO-2001WKS Part of heckendorn:2001:GECCOWKS",
}
@InCollection{Botros:2004:EMTP,
author = "Michael Botros",
title = "Evolving Controllers for Miniature Robots",
year = "2004",
booktitle = "Evolvable Machines: Theory \& Practice",
pages = "73--100",
volume = "161",
series = "Studies in Fuzziness and Soft Computing",
chapter = "4",
editor = "Nadia Nedjah and Luiza {de Macedo Mourelle}",
publisher = "Springer",
address = "Berlin",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-22905-1",
URL = "http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980449-0,00.html",
notes = "Springer says published in 2005 but available Nov
2004",
}
@InCollection{botros:2006:GSP,
author = "Michael Botros",
title = "Evolving Complex Robotic Behaviors Using Genetic
Programming",
year = "2006",
booktitle = "Genetic Systems Programming: Theory and Experiences",
pages = "175--194",
volume = "13",
series = "Studies in Computational Intelligence",
editor = "Nadia Nedjah and Ajith Abraham and Luiza {de Macedo
Mourelle}",
publisher = "Springer",
address = "Germany",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-29849-5",
notes = "http://www.springer.com/sgw/cda/frontpage/0,11855,5-146-22-92733168-0,00.html",
}
@InProceedings{Botzheim:2004:ishrCI,
title = "Model Identification by Bacterial Optimization",
author = "J. Botzheim and L. T. Koczy",
booktitle = "Proceedings of the 5th International Symposium of
Hungarian Researchers on Computational Intelligence",
year = "2004",
pages = "91--102",
address = "Budapest, Hungary",
month = nov,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.bmf.hu/conferences/mtn/botzheim.pdf",
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.135.7233",
bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
contributor = "CiteSeerX",
language = "en",
oai = "oai:CiteSeerXPSU:10.1.1.135.7233",
abstract = "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.",
}
@Article{BotzheimCabritaKoczyRuano07,
author = "Janos Botzheim and Cristiano Cabrita and Laszlo T.
Koczy and Antonio E. Ruano",
title = "Genetic and Bacterial Programming for {B}-Spline
Neural Networks Design",
journal = "Journal of Advanced Computational Intelligence and
Intelligent Informatics",
year = "2007",
volume = "11",
number = "2",
pages = "220--231",
month = feb,
keywords = "genetic algorithms, genetic programming",
ISSN = "1343-0130",
URL = "http://www.fujipress.jp/finder/xslt.php?mode=present&inputfile=JACII001100020012.xml",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/botzheim/BotzheimCabritaKoczyRuano07.pdf",
abstract = "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.",
}
@PhdThesis{Botzheim:thesis,
author = "Janos Botzheim",
title = "Intelligens szamitastechnikai modellek identifiacioja
evolucios es gradiens alapu tanulo algoritmusokkal",
school = "Budapest University of Technology and Economics,
Faculty of Electrical Engineering and Informatics",
type = "{Ph.D.} thesis",
year = "2007",
address = "Budapest",
month = "11 " # nov,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.sze.hu/~botzheim/hid/disszertacio.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/botzheim/thesisbooklet.pdf",
size = "124 pages",
abstract = "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.",
notes = "In Hungarian. 24 page english summary",
}
@InProceedings{Boumaza:2001:EvoWorks,
author = "Amine M. Boumaza and Jean Louchet",
title = "Dynamic Flies: Using Real-Time Parisian Evolution in
Robotics",
booktitle = "Applications of Evolutionary Computing",
year = "2001",
editor = "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",
volume = "2037",
series = "LNCS",
pages = "288--297",
address = "Lake Como, Italy",
publisher_address = "Berlin",
month = "18 " # apr,
organisation = "EvoNET",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, fly algorithm, robot",
ISBN = "3-540-41920-9",
URL = "http://minimum.inria.fr/evo-lab/Publications/evoiasp2001_Louchet_Boumaza.ps.gz",
abstract = "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.",
notes = "EvoWorkshops2001",
}
@InProceedings{Boumaza:evowks03,
author = "Amine M. Boumaza and Jean Louchet",
title = "Mobile Robot Sensor Fusion Using Flies",
booktitle = "Applications of Evolutionary Computing,
EvoWorkshops2003: Evo{BIO}, Evo{COP}, Evo{IASP},
Evo{MUSART}, Evo{ROB}, Evo{STIM}",
year = "2003",
editor = "G{\"u}nther 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",
volume = "2611",
series = "LNCS",
pages = "357--367",
address = "University of Essex, England, UK",
publisher_address = "Berlin",
month = "14-16 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, evolutionary
computation, applications",
notes = "EvoWorkshops2003",
}
@InProceedings{Bourmistrova:2007:cec,
author = "A. Bourmistrova and S. Khantsis",
title = "Control System Design Optimisation via Genetic
Programming",
booktitle = "2007 IEEE Congress on Evolutionary Computation",
year = "2007",
editor = "Dipti Srinivasan and Lipo Wang",
pages = "1993--2000",
address = "Singapore",
month = "25-28 " # sep,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "1-4244-1340-0",
file = "1691.pdf",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and
the IET.
IEEE Catalog Number: 07TH8963C",
}
@InCollection{Bourmistrova:2009:AV,
author = "Anna Bourmistrova and Sergey Khantsis",
title = "Flight Control System Design Optimisation via Genetic
Programming",
booktitle = "Aerial Vehicles",
publisher = "InTech",
year = "2009",
editor = "Thanh Mung Lam",
chapter = "7",
keywords = "genetic algorithms, genetic programming, mobile
robotics",
isbn13 = "978-953-7619-41-1",
URL = "http://www.intechopen.com/download/pdf/pdfs_id/5969",
bibsource = "OAI-PMH server at www.intechopen.com",
language = "eng",
oai = "oai:intechopen.com:5969",
URL = "http://www.intechopen.com/articles/show/title/flight_control_system_design_optimisation_via_genetic_programming",
abstract = "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.",
size = "34 pages",
}
@InCollection{Bourmistrova:2010:naEC,
title = "Genetic Programming in Application to Flight Control
System Design Optimisation",
author = "Anna Bourmistrova and Sergey Khantsis",
booktitle = "New Achievements in Evolutionary Computation",
publisher = "InTech",
year = "2010",
editor = "Peter Korosec",
chapter = "10",
month = feb,
keywords = "genetic algorithms, genetic programming, UAV",
isbn13 = "978-953-307-053-7",
language = "eng",
oai = "oai:intechopen.com:8542",
URL = "http://www.intechopen.com/articles/show/title/genetic-programming-in-application-to-flight-control-system-design-optimisation",
URL = "http://www.intechopen.com/download/pdf/pdfs_id/8542",
notes = "the first seminal book to introduce GP as a solid and
practical technique is John Koza's Genetic Programming,
dated 1992. RMIT",
size = "34 pages",
}
@InCollection{bozarth:2000:PCVGP,
author = "Bradley J. Bozarth",
title = "Programmatic Compression of Video using Genetic
Programming",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2000",
year = "2000",
editor = "John R. Koza",
pages = "46--53",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "part of \cite{koza:2000:gagp}",
}
@InProceedings{Bozorgtabar:2010:IST,
author = "Behzad Bozorgtabar and Farzad Noorian and Gholam Ali
Rezai Rad",
title = "Comparison of different {PCA} based Face Recognition
algorithms using Genetic Programming",
booktitle = "5th International Symposium on Telecommunications (IST
2010)",
year = "2010",
month = dec,
pages = "801--805",
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/ISTEL.2010.5734132",
notes = "Also known as \cite{5734132}",
}
@InProceedings{Bozorgtabar:2011:GCC,
author = "Behzad Bozorgtabar and Farzad Noorian and Rezai Rad
{Gholam Ali}",
title = "A Genetic Programming approach to face recognition",
booktitle = "IEEE GCC Conference and Exhibition (GCC), 2011",
year = "2011",
pages = "194--197",
address = "Dubai, United Arab Emirates",
month = feb # " 19-22",
publisher = "IEEE",
size = "4 pages",
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/IEEEGCC.2011.5752477",
notes = "Iran University of Science and Technology Also known
as \cite{5752477}",
}
@InProceedings{brabazon:2001:AAANZ,
author = "Tony Brabazon and M. O'Neill and C. Ryan and J. J.
Collins",
title = "Uncovering Technical Trading Rules Using Evolutionary
Automatic Programming",
booktitle = "Proceedings of 2001 AAANZ Conference (Accounting
Association of Australia and NZ)",
year = "2001",
address = "Auckland, New Zealand",
month = "1-3 " # jul,
keywords = "genetic algorithms, genetic programming, grammatical
evolution, financial prediction",
}
@InProceedings{brabazon:2002:EuroGP,
title = "Evolving classifiers to model the relationship between
strategy and corporate performance using grammatical
evolution",
author = "Anthony Brabazon and Michael O'Neill and Conor Ryan
and Robin Matthews",
editor = "James A. Foster and Evelyne Lutton and Julian Miller
and Conor Ryan and Andrea G. B. Tettamanzi",
booktitle = "Genetic Programming, Proceedings of the 5th European
Conference, EuroGP 2002",
volume = "2278",
series = "LNCS",
pages = "103--112",
address = "Kinsale, Ireland",
publisher_address = "Berlin",
month = "3-5 " # apr,
publisher = "Springer-Verlag",
year = "2002",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
ISBN = "3-540-43378-3",
abstract = "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.",
notes = "EuroGP'2002, part of \cite{lutton:2002:GP}",
}
@InProceedings{brabazon:2002:gecco,
author = "Anthony Brabazon and Michael O'Neill and Robin
Matthews and Conor Ryan",
title = "Grammatical Evolution And Corporate Failure
Prediction",
booktitle = "GECCO 2002: Proceedings of the Genetic and
Evolutionary Computation Conference",
editor = "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",
year = "2002",
pages = "1011--1018",
address = "New York",
publisher_address = "San Francisco, CA 94104, USA",
month = "9-13 " # jul,
publisher = "Morgan Kaufmann Publishers",
keywords = "genetic algorithms, genetic programming, real world
applications, corporate failure prediction, genotype to
phenotype mapping, grammars, grammatical evolution",
ISBN = "1-55860-878-8",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/RWA145.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/RWA145.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-20.pdf",
notes = "GECCO-2002. A joint meeting of the eleventh
International Conference on Genetic Algorithms
(ICGA-2002) and the seventh Annual Genetic Programming
Conference (GP-2002)",
}
@InProceedings{brabazon:2002:gecco:workshop,
title = "Trading Foreign Exchange Markets Using Evolutionary
Automatic Programming",
author = "Tony Brabazon and Michael O'Neill",
pages = "133--136",
booktitle = "{GECCO 2002}: Proceedings of the Bird of a Feather
Workshops, Genetic and Evolutionary Computation
Conference",
editor = "Alwyn M. Barry",
year = "2002",
month = "8 " # jul,
publisher = "AAAI",
address = "New York",
publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
URL = "http://www.grammatical-evolution.org/gews2002/brabazon.ps",
notes = "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",
}
@InProceedings{Brabazon:2003:ICAI,
author = "Anthony Brabazon and Michael O'Neill",
title = "A Grammar Model for Foreign-Exchange Trading",
booktitle = "Proceedings of the International Conference on
Artificial Intelligence",
year = "2003",
editor = "H. R. Arabnia et al.",
volume = "II",
pages = "492--498",
month = "23-26 " # jun,
publisher = "CSREA Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-932415-13-0",
abstract = "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.",
}
@InProceedings{Brabazon:2004:BYB,
author = "Anthony Brabazon and Robin Matthews and Michael
O'Neill",
title = "Grammars, Representations, Mental Maps and Corporate
Strategy",
booktitle = "Business Research Yearbook: Global Business
Perspectives. Proceedings of the Fifteenth Annual
International Conference of the International Academy
of Business Disciplines",
year = "2004",
editor = "C. Gardner and J. Biberman and A. Alkhafaji",
volume = "11",
pages = "1054--1058",
address = "San Antonio, USA",
publisher_address = "Saline, Michigan, USA",
month = mar # " 24-27",
printer = "McNaughton and Gunn",
keywords = "genetic algorithms, genetic programming,grammatical
evolution",
notes = "http://academic.scranton.edu/faculty/BIBERMANG1/pres.htm",
}
@InProceedings{brabazon:evows04,
author = "Anthony Brabazon and Michael O'Neill",
title = "Bond-Issuer Credit Rating with Grammatical Evolution",
booktitle = "Applications of Evolutionary Computing,
EvoWorkshops2004: {EvoBIO}, {EvoCOMNET}, {EvoHOT},
{EvoIASP}, {EvoMUSART}, {EvoSTOC}",
year = "2004",
month = "5-7 " # apr,
editor = "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",
series = "LNCS",
volume = "3005",
address = "Coimbra, Portugal",
publisher = "Springer Verlag",
publisher_address = "Berlin",
pages = "270--279",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, evolutionary computation",
ISBN = "3-540-21378-3",
abstract = "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.",
notes = "EvoWorkshops2004",
}
@Article{brabazon:2005:GMTSP,
author = "Anthony Brabazon and Katrina Meagher and Edward Carty
and Michael O'Neill and Peter Keenan",
title = "Grammar-mediated time-series prediction",
journal = "Journal of Intelligent Systems",
year = "2004",
volume = "14",
number = "2--3",
pages = "123--143",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, time-series",
}
@Article{Brabazon:2005:JIS,
author = "A. Brabazon and K. Meagher and E. Carty and M. O'Neill
and P. Keenan",
title = "Grammar-Mediated Time-Series Prediction",
journal = "Journal of Intelligent Systems",
year = "2005",
volume = "14",
number = "2-3",
pages = "123--143",
note = "Special Issue",
publisher = "Freund \& Pettman Publishers",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
ISSN = "0334-1860",
URL = "http://www.freundpublishing.com/Journal_Intelligent_Systems/Intellileaf14_2_3.htm",
}
@InProceedings{brabazon:2005:CRWpiGE,
author = "Anthony Brabazon and Michael O'Neill",
title = "Credit Rating with pi Grammatical Evolution",
booktitle = "Proceedings of Computer Methods and Systems
Conference",
year = "2005",
editor = "R. Tadeusiewicz and A. Ligeza and M. Szymkat",
volume = "1",
pages = "253--260",
address = "Krakow, Poland",
publisher_address = "Krakow",
month = "14-16 " # nov,
publisher = "Oprogramowanie Naukowo-Techniczne Tadeusiewicz",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
ISBN = "83-916420-3-8",
abstract = "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.",
}
@Book{Brabazon:2006:BIAS,
author = "Anthony Brabazon and Michael O'Neill",
title = "Biologically Inspired Algorithms for Financial
Modelling",
publisher = "Springer",
year = "2006",
series = "Natural Computing Series",
keywords = "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)",
ISBN = "3-540-26252-0",
notes = "reviewed by \cite{Kaboudan:2006:GPEM} also Brad G.
Kyer, The Book Review Column 40(4), 2009, p11-17,
William Gasarch,
http://www.cs.umd.edu/~gasarch/bookrev/",
size = "275 pages",
}
@Article{Brabazon:2006:I,
author = "Anthony Brabazon and Michael O'Neill",
title = "Credit Classification Using Grammatical Evolution",
journal = "Informatica",
year = "2006",
volume = "30",
number = "3",
pages = "325--335",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, Povzetek: Metoda gramaticne evolucije je
uporabljena za klasificiranje kreditov.",
ISSN = "0350-5596",
URL = "http://ai.ijs.si/informatica/PDF/30-3/07_Brabazon_Credit%20Classification%20Using.pdf",
size = "11 pages",
abstract = "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.",
}
@InCollection{Brabazon:2008:K-DC,
author = "Anthony Brabazon and Michael O'Neill",
title = "Bond Rating with piGrammatical Evolution",
booktitle = "Knowledge Engineering and Intelligent Computations",
publisher = "Springer",
year = "2008",
editor = "C. Cotta and S. Reich and R. Schaefer and A. Ligeza",
volume = "102",
series = "Studies in Computational Intelligence",
chapter = "2",
pages = "17--30",
keywords = "genetic algorithms, genetic programming, Grammatical
Evolution",
isbn13 = "978-3-540-77474-7",
doi = "doi:10.1007/978-3-540-77475-4_2",
abstract = "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.",
}
@Book{Brabazon:2008:edbook,
editor = "Anthony Brabazon and Michael O'Neill",
title = "Natural Computing in Computational Finance",
publisher = "Springer",
year = "2008",
volume = "100",
series = "Studies in Computational Intelligence",
month = apr,
keywords = "genetic algorithms, genetic programming, computational
finance, evolution strategies, differential evolution,
bacterial foraging, quantum-inspired evolutionary
algorithms",
isbn13 = "9783540774761",
URL = "http://www.springer.com/engineering/book/978-3-540-77476-1",
abstract = "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.",
size = "approx 300 pages",
}
@Article{Brabazon:2008:IEEECIM,
author = "Anthony Brabazon and Michael O'Neill and Ian Dempsey",
title = "An Introduction to Evolutionary Computation in
Finance",
journal = "IEEE Computational Intelligence Magazine",
year = "2008",
volume = "3",
number = "4",
pages = "42--55",
month = nov,
URL = "http://ieeexplore.ieee.org/xpl/tocresult.jsp?isYear=2008&isnumber=4625777&Submit32=Go+To+Issue",
doi = "doi:10.1109/MCI.2008.929841",
ISSN = "1556-603X",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, finance, evolutionary computation, financial
data processing computational intelligence
methodologies, evolutionary computation approach,
finance",
abstract = "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.",
notes = "Also known as \cite{4625793}",
}
@Book{Brabazon:2009:book,
editor = "Anthony Brabazon and Michael O'Neill",
title = "Natural Computing in Computational Finance (Volume
2)",
publisher = "Springer",
year = "2009",
volume = "185",
series = "Studies in Computational Intelligence",
month = mar,
keywords = "genetic algorithms, genetic programming, computational
Finance, Computational Intelligence",
isbn13 = "978-3-540-95973-1",
URL = "http://www.springer.com/engineering/book/978-3-540-95973-1",
abstract = "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",
size = "approx 260 pages",
}
@Book{brabazon_oneill_maringer:2010:book,
editor = "A. Brabazon and M. O'Neill and D. G. Maringer",
title = "Natural Computing in Computational Finance (Volume
3)",
publisher = "Springer",
year = "2010",
volume = "293",
series = "Studies in Computational Intelligence",
keywords = "genetic algorithms, genetic programming, natural
computing, computational finance, computational
intelligence",
isbn13 = "978-3-642-13949-9",
URL = "http://www.springer.com/engineering/book/978-3-642-13949-9",
abstract = "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
\cite{Brabazon:2008:edbook} and II
\cite{Brabazon:2009:book}",
size = "241 pages",
}
@Unpublished{abrabazon_moneill:ppsn2010,
author = "A. Brabazon and M. O'Neill",
howpublished = "PPSN 2010 11th International Conference on Parallel
Problem Solving From Nature",
address = "Krakow, Poland",
month = "11-15 " # sep,
note = "Tutorial",
title = "Natural Computing and Finance",
URL = "ncra.ucd.ie/papers/PPSN_tutorial_2010_published.pdf",
year = "2010",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, finance",
size = "69 slides",
}
@InProceedings{BradburyJ10,
author = "Jeremy S. Bradbury and Kevin Jalbert",
title = "Automatic Repair of Concurrency Bugs",
booktitle = "Proceedings of the 2nd International Symposium on
Search Based Software Engineering (SSBSE '10)",
year = "2010",
editor = "Massimiliano {Di Penta} and Simon Poulding and Lionel
Briand and John Clark",
address = "Benevento, Italy",
month = "7-9 " # sep,
note = "Fast abstract",
keywords = "genetic algorithms, genetic programming, SBSE,
concurrency, mutation :poster?",
URL = "http://www.ssbse.org/2010/fastabstracts/ssbse2010_fastabstract_04.pdf",
size = "2 pages",
abstract = "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.",
notes = "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",
}
@InCollection{braden:2002:AAPSPGA,
author = "Katie Braden",
title = "A simple Approach to Protein Structure Prediction
using Genetic Algorithms",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2002",
year = "2002",
editor = "John R. Koza",
pages = "36--44",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms",
URL = "http://www.genetic-programming.org/sp2002/Braden.pdf",
notes = "part of \cite{koza:2002:gagp}",
}
@InProceedings{bradley:2010:evofin,
author = "Robert Gregory Bradley and Anthony Brabazon and
Michael O'Neill",
title = "Evolving Trading Rule-Based Policies",
booktitle = "EvoFIN",
year = "2010",
editor = "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",
volume = "6025",
series = "LNCS",
pages = "251--260",
address = "Istanbul",
month = "7-9 " # apr,
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
isbn13 = "978-3-642-12241-5",
doi = "doi:10.1007/978-3-642-12242-2_26",
abstract = "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.",
notes = "EvoFIN'2010 held in conjunction with EuroGP'2010
EvoCOP2010 EvoBIO2010",
}
@InProceedings{bradley_etal:cec2010,
author = "Robert Bradley and Anthony Brabazon and Michael
O'Neill",
title = "Objective Function Design in a Grammatical
Evolutionary Trading System",
booktitle = "2010 IEEE World Congress on Computational
Intelligence",
pages = "3487--3494",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-6910-9",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
doi = "doi:10.1109/CEC.2010.5586020",
abstract = "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.",
notes = "WCCI 2010. Also known as \cite{5586020}",
}
@TechReport{brady:murphy,
author = "Robert M. Brady and Ross J. Anderson and Robin C.
Ball",
title = "Murphy's law, the fitness of evolving species, and the
limits of software reliability",
institution = "Computer Laboratory, Cambridge",
year = "1996?",
email = "rja14@cl.cam.ac.uk",
URL = "http://www.ftp.cl.cam.ac.uk/ftp/users/rja14/babtr.pdf",
abstract = "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.",
size = "11 pages",
notes = "cf \cite{Bishop96}
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{"}.",
}
@Book{Braitenberg:1984,
author = "Valentino Braitenberg",
year = "1984",
keywords = "NEURAL MOBILE SIMULATION EVOLUTION MOTOR-SCHEMA
REACTIVE MODULAR",
institution = "Europe?",
title = "Vehicles",
publisher = "MIT Press, Cambridge MA, 1984",
annote = "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]",
ISBN = "0-262-52112-1",
notes = "amazon says 1986",
}
@InProceedings{Brajlih:2005:PMI,
author = "Tomaz Brajlih and Igor Drstvensek and Miha Kovacic and
Joze Balic",
title = "Compensation of the size of the finished part for the
PolyJet rapid prototyping procedure",
booktitle = "Proceedings of the International Conference Polymers
\& Moulds Innovations PMI 2005",
year = "2005",
address = "Gent, Belgium",
month = apr # " 20-23",
keywords = "genetic algorithms, genetic programming, hitra
izdelava prototipov, PolyJet postopek, izravnalni
faktor, prototipi, rapid prototyping, polyjet
procedure, compensation factor",
abstract = "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.",
notes = "
http://cobiss.izum.si/scripts/cobiss?command=DISPLAY&base=COBIB&RID=9636118",
}
@InProceedings{Brajlih:2006:DAAAM,
author = "T. Brajlih and I. Drstvensek and B. Valentan and J.
Balic",
title = "Improving the Accuracy of Rapid Prototyping Procedures
by Genetic Programming",
booktitle = "Proceedings of the 5TH International conference of
DAAAM Baltic -- Industrial Engineering",
year = "2006",
editor = "R. Kyeener",
pages = "113--116",
address = "Tallinn, Estonia",
month = "20-22 " # apr,
organisation = "BALTECH Consortium, Estonian Academy of Sciences,
Federation of Estonian Engineering Industries,
Association of Estonian Mechanical Engineers, Leonardo
National Agency of Estonia, INNOMET",
publisher = "DAAAM",
keywords = "genetic algorithms, genetic programming",
URL = "http://innomet.ttu.ee/daaam06/proceedings/Production%20Engineering/24brajilih.pdf",
size = "4 pages",
abstract = "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.",
}
@Article{Brajlih:2006:AMME,
author = "Tomaz Brajlih and Igor Drstvensek and Miha Kovacic and
Joze Balic",
title = "Optimizing scale factors of the PolyJet rapid
prototyping procedure by genetic programming",
journal = "Journal of achievements in materials and manufacturing
engineering",
year = "2006",
volume = "16",
number = "1-2",
pages = "101--106",
month = may # "-" # jun,
note = "Special Issue of CAM3S'2005",
keywords = "genetic algorithms, genetic programming, rapid
prototyping, PolyJet",
issn_ = "Y505-3994 invalid checksum",
URL = "http://157.158.19.167/papers_cams05/167.pdf",
URL = "http://www.journalamme.org/papers_cams05/167.pdf",
size = "6 pages",
abstract = "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.",
notes = "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]",
}
@TechReport{oai:CiteSeerPSU:323834,
title = "{SYSGP} -- {A} {C}++ library of different {GP}
variants",
author = "Markus Brameier and Wolfgang Kantschik and Peter
Dittrich and Wolfgang Banzhaf",
institution = "Collaborative Research Center 531, University of
Dortmund",
year = "1998",
type = "Technical Report",
number = "CI-98/48",
address = "Germany",
keywords = "genetic algorithms, genetic programming",
URL = "https://eldorado.uni-dortmund.de/bitstream/2003/5345/2/ci4898_doc.pdf",
URL = "http://citeseer.ist.psu.edu/323834.html",
citeseer-isreferencedby = "oai:CiteSeerPSU:39667",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:323834",
rights = "unrestricted",
abstract = "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.",
size = "13 pages",
}
@InProceedings{brameier:1999:PMCGP,
author = "Markus Brameier and Frank Hoffmann and Peter Nordin
and Wolfgang Banzhaf and Frank Francone",
title = "Parallel Machine Code Genetic Programming",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1228",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, poster
papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-439.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-439.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@TechReport{oai:CiteSeerPSU:488546,
title = "Effective Linear Genetic Programming",
author = "Markus Brameier and Wolfgang Banzhaf",
citeseer-isreferencedby = "oai:CiteSeerPSU:75403",
citeseer-references = "oai:CiteSeerPSU:46478; oai:CiteSeerPSU:271953;
oai:CiteSeerPSU:349518",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:488546",
rights = "unrestricted",
institution = "Department of Computer Science, University of
Dortmund",
year = "2001",
number = "Reihe CI 108/01, SFB 531",
address = "44221 Dortmund, Germany",
keywords = "genetic algorithms, genetic programming",
URL = "http://sfbci.uni-dortmund.de/index.php?option=com_wrapper&Itemid=180&lang=en",
URL = "http://sfbci.uni-dortmund.de/Publications/Reference/Downloads/BB09052001.pdf",
URL = "http://citeseer.ist.psu.edu/488546.html",
abstract = "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.",
size = "pages",
}
@TechReport{oai:CiteSeerPSU:324837,
author = "Markus Brameier and Wolfgang Banzhaf",
title = "A Comparison of Genetic Programming and Neural
Networks in Medical Data Analysis",
institution = "Dortmund University",
year = "1998",
type = "Reihe",
number = "CI 43/98, SFB 531",
address = "Germany",
keywords = "genetic algorithms, genetic programming",
URL = "https://eldorado.uni-dortmund.de/dspace/bitstream/2003/5344/2/ci4398_doc.pdf",
URL = "http://citeseer.ist.psu.edu/324837.html",
citeseer-isreferencedby = "oai:CiteSeerPSU:39821",
citeseer-references = "oai:CiteSeerPSU:212034;
oai:CiteSeerPSU:186821",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:324837",
rights = "unrestricted",
abstract = "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",
size = "pages",
}
@Article{Brameier:2001:TEC,
author = "Markus Brameier and Wolfgang Banzhaf",
title = "A Comparison of Linear Genetic Programming and Neural
Networks in Medical Data Mining",
journal = "IEEE Transactions on Evolutionary Computation",
year = "2001",
volume = "5",
number = "1",
pages = "17--26",
month = feb,
keywords = "genetic algorithms, genetic programming, Data mining,
evolutionary computation, neural networks",
URL = "http://web.cs.mun.ca/~banzhaf/papers/ieee_taec.pdf",
size = "10 pages",
abstract = "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.",
notes = "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.",
}
@Article{brameier:2001:GPEM,
author = "Markus Brameier and Wolfgang Banzhaf",
title = "Evolving Teams of Predictors with Linear Genetic
Programming",
journal = "Genetic Programming and Evolvable Machines",
year = "2001",
volume = "2",
number = "4",
pages = "381--407",
month = dec,
keywords = "genetic algorithms, genetic programming, evolution of
teams, combination of multiple predictors, linear
genetic programming",
ISSN = "1389-2576",
URL = "http://web.cs.mun.ca/~banzhaf/papers/teams.pdf",
URL = "http://citeseer.ist.psu.edu/508652.html",
URL = "http://citeseer.ist.psu.edu/411995.html",
doi = "doi:10.1023/A:1012978805372",
size = "26 pages",
abstract = "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.",
notes = "Article ID: 386363",
}
@TechReport{oai:CiteSeerPSU:552561,
title = "Explicit Control of Diversity and Effective Variation
Distance in Linear Genetic Programming",
author = "Markus Brameier and Wolfgang Banzhaf",
year = "2002",
month = feb # "~25",
citeseer-isreferencedby = "oai:CiteSeerPSU:92442;
oai:CiteSeerPSU:192628",
citeseer-references = "oai:CiteSeerPSU:266665; oai:CiteSeerPSU:271953;
oai:CiteSeerPSU:270103; oai:CiteSeerPSU:61421;
oai:CiteSeerPSU:440305; oai:CiteSeerPSU:32228;
oai:CiteSeerPSU:212034; oai:CiteSeerPSU:61877",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
language = "ENG",
oai = "oai:eldorado:0x0004162d",
oai = "oai:CiteSeerPSU:552561",
rights = "unrestricted",
URL = "http://eldorado.uni-dortmund.de/0x81d98002_0x0004162d",
URL = "http://eldorado.uni-dortmund.de:8080/bitstream/2003/5419/1/123.pdf",
URL = "http://citeseer.ist.psu.edu/552561.html",
institution = "Dortmund University",
keywords = "genetic algorithms, genetic programming",
abstract = "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",
notes = "see also \cite{brameier:2002:EuroGP} 123.pdf crashes
SUSE 10.0 KDE Konqueror 3.4.2b, Nov 2006",
size = "25 pages",
}
@InProceedings{brameier:2002:EuroGP,
title = "Explicit Control of Diversity and Effective Variation
Distance in Linear Genetic Programming",
author = "Markus Brameier and Wolfgang Banzhaf",
editor = "James A. Foster and Evelyne Lutton and Julian Miller
and Conor Ryan and Andrea G. B. Tettamanzi",
booktitle = "Genetic Programming, Proceedings of the 5th European
Conference, EuroGP 2002",
publisher = "Springer-Verlag",
volume = "2278",
series = "LNCS",
pages = "37--49",
address = "Kinsale, Ireland",
publisher_address = "Berlin",
month = "3-5 " # apr,
year = "2002",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-43378-3",
URL = "http://www.cs.mun.ca/~banzhaf/papers/eurogp02_dist.pdf",
URL = "http://link.springer-ny.com/link/service/series/0558/bibs/2278/22780037.htm",
URL = "http://link.springer-ny.com/link/service/series/0558/papers/2278/22780037.pdf",
abstract = "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.",
notes = "EuroGP'2002, part of \cite{lutton:2002:GP} Best
paper
See also \cite{oai:CiteSeerPSU:552561}",
}
@InProceedings{brameier03,
author = "Markus Brameier and Wolfgang Banzhaf",
title = "Neutral Variations Cause Bloat in Linear {GP}",
booktitle = "Genetic Programming, Proceedings of EuroGP'2003",
year = "2003",
editor = "Conor Ryan and Terence Soule and Maarten Keijzer and
Edward Tsang and Riccardo Poli and Ernesto Costa",
volume = "2610",
series = "LNCS",
pages = "286--296",
address = "Essex",
publisher_address = "Berlin",
month = "14-16 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-00971-X",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=286",
abstract = "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.",
notes = "EuroGP'2003 held in conjunction with EvoWorkshops
2003
Section 2.3 PerlGP 'In PerlGP \cite{maccallum03},
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.'
",
}
@PhdThesis{B2005OLGP,
title = "On Linear Genetic Programming",
author = "Markus Brameier",
month = feb,
year = "2004",
school = "Fachbereich Informatik, Universit{\"a}t Dortmund",
address = "Germany",
keywords = "genetic algorithms, genetic programming, Evolutionary
algorithms, Machine learning",
URL = "https://eldorado.uni-dortmund.de/bitstream/2003/20098/2/Brameierunt.pdf",
URL = "https://eldorado.uni-dortmund.de/bitstream/2003/20098/1/Brameier.ps",
size = "272 pages",
abstract = "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.",
notes = "Day of Submission: 2003-05-28, Committee: Wolfgang
Banzhaf and Martin Riedmiller and Peter Nordin.
\onlineAvailableT{https://eldorado.uni-dortmund.de/handle/2003/20098}{http://hdl.handle.net/2003/20098}{2007-08-17}",
}
@Article{oai:biomedcentral.com:1471-2105-7-16,
author = "Markus Brameier and Josien Haan and Andrea Krings and
Robert M MacCallum",
title = "Automatic discovery of cross-family sequence features
associated with protein function",
publisher = "BioMed Central Ltd.",
year = "2006",
month = jan # "~12",
journal = "BMC bioinformatics [electronic resource]",
volume = "7",
number = "16",
keywords = "genetic algorithms, genetic programming",
ISSN = "1471-2105",
bibsource = "OAI-PMH server at www.biomedcentral.com",
language = "en",
oai = "oai:biomedcentral.com:1471-2105-7-16",
rights = "Copyright 2006 Brameier et al; licensee BioMed Central
Ltd.",
URL = "http://www.biomedcentral.com/content/pdf/1471-2105-7-16.pdf",
URL = "http://www.biomedcentral.com/1471-2105/7/16",
doi = "doi:10.1186/1471-2105-7-16",
size = "16 pages",
abstract = "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.",
notes = "PMID: 16409628",
}
@Book{Brameier:2006:book,
author = "Markus Brameier and Wolfgang Banzhaf",
title = "Linear Genetic Programming",
publisher = "Springer",
year = "2007",
number = "XVI",
series = "Genetic and Evolutionary Computation",
keywords = "genetic algorithms, genetic programming",
ISSN = "1932-0167",
ISBN = "0-387-31029-0",
URL = "http://www.springer.com/west/home/default?SGWID=4-40356-22-173660820-0",
abstract = "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.",
size = "Approx. 320 pages",
}
@Article{NucPred-bioinformatics2007,
author = "Markus Brameier and Andrea Krings and Robert M.
MacCallum",
title = "{NucPred} Predicting nuclear localization of
proteins",
journal = "Bioinformatics",
year = "2007",
volume = "23",
number = "9",
pages = "1159--1160",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1093/bioinformatics/btm066",
size = "2 pages",
abstract = "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.",
notes = "PMID: 17332022 [PubMed - indexed for MEDLINE]",
}
@Article{Brameier:2007:BMCbinf,
author = "Markus Brameier and Carsten Wiuf",
title = "Ab initio identification of human {microRNAs} based on
structure motifs",
journal = "BMC Bioinformatics",
year = "2007",
volume = "8",
pages = "478",
month = "18 " # dec,
keywords = "genetic algorithms, genetic programming, linear
genetic programming",
URL = "http://www.biomedcentral.com/content/pdf/1471-2105-8-478.pdf",
doi = "doi:10.1186/1471-2105-8-478",
size = "11 pages",
abstract = "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.",
notes = "PMID: 18088431 [PubMed - indexed for MEDLINE]",
}
@InProceedings{branke:1999:RGDSSEA,
author = "Jurgen Branke and Massimo Cutaia and Heinrich Dold",
title = "Reducing Genetic Drift in Steady State Evolutionary
Algorithms",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "68--74",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@Article{Branke:2006:ASC,
author = "Jurgen Branke and Pablo Funes and Frederik Thiele",
title = "Evolutionary design of en-route caching strategies",
journal = "Applied Soft Computing",
year = "2006",
volume = "7",
number = "3",
pages = "890--898",
month = jun,
keywords = "genetic algorithms, genetic programming, En-route
caching, Robustness",
doi = "doi:10.1016/j.asoc.2006.04.003",
abstract = "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.",
}
@Proceedings{Branke:2010:GECCO,
title = "{GECCO} '10: Proceedings of the 12th annual conference
on Genetic and evolutionary computation",
year = "2010",
editor = "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",
address = "Portland, OR, USA",
publisher_address = "New York, NY, USA",
month = jul # " 07-11",
organisation = "ACM SIGEVO",
publisher = "ACM",
keywords = "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",
isbn13 = "978-1-4503-0072-8",
URL = "http://portal.acm.org/citation.cfm?id=1830483&coll=DL&dl=ACM&CFID=12039329&CFTOKEN=58660565",
abstract = "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.",
notes = "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.",
}
@InProceedings{Brar:2007:WCE,
author = "Gursewak S Brar and Yadwinder S Brar and Yaduvir
Singh",
title = "A Fuzzy Entropy Algorithm For Data Extrapolation In
Multi-Compressor System",
booktitle = "Proceedings of the World Congress on Engineering, WCE
2007",
year = "2007",
volume = "I",
pages = "105--110",
address = "London",
month = jul # " 2-4",
keywords = "genetic algorithms, genetic programming, fuzzy
entropy, incomplete data, classification, knowledge
discovery, multi-compressor system",
isbn13 = "978-988-98671-5-7",
URL = "http://www.iaeng.org/publication/WCE2007/WCE2007_pp105-110.pdf",
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.2111",
bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
contributor = "CiteSeerX",
language = "en",
oai = "oai:CiteSeerXPSU:10.1.1.149.2111",
abstract = "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",
notes = "pdf broken?",
}
@InCollection{brave:1994:recursive,
author = "Scott Brave",
title = "Evolution of Planning: Using recursive techniques in
Genetic Planning",
booktitle = "Artificial Life at Stanford 1994",
year = "1994",
editor = "John R. Koza",
pages = "1--10",
address = "Stanford, California, 94305-3079 USA",
month = jun,
organisation = "Stanford University",
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-182105-2",
notes = "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",
}
@InProceedings{brave:1994:recursiveGW,
author = "Scott Brave",
title = "Using Genetic Programming to Evolve Recursive Programs
for Tree Search",
booktitle = "Fourth Golden West Conference on Intelligent Systems",
year = "1995",
editor = "Sushil J. Louis",
pages = "60--65",
publisher_address = "San Francisco, California, USA",
month = "12-14 " # jun,
publisher = "International Society for Computers and their
Applications - ISCA",
email = "isca@ipass.net",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-880843-12-9",
notes = "GWICS ISCA-GW-95 http://www.isca-hq.org/proc-lst.htm",
}
@InProceedings{brave:1994:mmGW,
author = "Scott Brave",
title = "Using Genetic Programming to Evolve Mental Models",
booktitle = "Fourth Golden West Conference on Intelligent Systems",
year = "1995",
editor = "Sushil J. Louis",
pages = "91--96",
publisher_address = "San Francisco, California, USA",
month = "12-14 " # jun,
publisher = "International Society for Computers and their
Applications - ISCA",
email = "isca@ipass.net",
keywords = "genetic algorithms, genetic programming, memory",
ISBN = "1-880843-12-9",
notes = "GWICS ISCA-GW-95 http://www.isca-hq.org/proc-lst.htm",
}
@InCollection{brave:1996:aigp2,
author = "Scott Brave",
title = "Evolving Recursive Programs for Tree Search",
booktitle = "Advances in Genetic Programming 2",
publisher = "MIT Press",
year = "1996",
editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}",
pages = "203--220",
chapter = "10",
address = "Cambridge, MA, USA",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-262-01158-1",
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.3.3005",
URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.3.3005&rep=rep1&type=pdf",
URL = "http://cisnet.mit.edu/Advances-in-Genetic-Programming/220",
abstract = "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.",
notes = "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",
size = "17 pages",
}
@InProceedings{brave:1996:dface,
author = "Scott Brave",
title = "Evolving Deterministic Finite Automata Using Cellular
Encoding",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "39--44",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
size = "6 pages",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/1745/http:zSzzSzbrave.www.media.mit.eduzSzpeoplezSzbravezSzpublicationszSzautomata.pdf/brave96evolving.pdf",
URL = "http://citeseer.ist.psu.edu/brave96evolving.html",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
abstract = "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...",
notes = "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.{"}",
}
@InProceedings{brave:1996:emmmGP,
author = "Scott Brave",
title = "The Evolution of Memory and Mental Models Using
Genetic Programming",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming, memory",
pages = "261--266",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/1745/http:zSzzSzbrave.www.media.mit.eduzSzpeoplezSzbravezSzpublicationszSzmodels.pdf/brave96evolution.pdf",
size = "6 pages",
abstract = "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...",
notes = "GP-96. cf. \cite{brave:1994:mmGW}
",
}
@Proceedings{brave:1999:gecco99lb,
editor = "Scott Brave and Annie S. Wu",
title = "Late Breaking Papers at the 1999 Genetic and
Evolutionary Computation Conference",
year = "1999",
address = "Orlando, Florida, USA",
month = "13 " # jul,
keywords = "genetic algorithms, genetic programming, Evolutionary
Programming, fuzzy rules",
size = "311 pages",
notes = "GECCO-99LB",
}
@InProceedings{DBLP:conf/ideal/BrazierRW04,
author = "Karl J. Brazier and Graeme Richards and Wenjia Wang",
title = "Implicit Fitness Sharing Speciation and Emergent
Diversity in Tree Classifier Ensembles.",
booktitle = "Intelligent Data Engineering and Automated Learning -
IDEAL 2004, 5th International Conference, Proceedings",
year = "2004",
pages = "333--338",
editor = "Zheng Rong Yang and Richard M. Everson and Hujun Yin",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3177",
ISBN = "3-540-22881-0",
bibsource = "DBLP, http://dblp.uni-trier.de",
address = "Exeter, UK",
month = aug # " 25-27",
organisation = "IEEE",
keywords = "genetic algorithms, genetic programming, gene
expression programming",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3177&spage=333",
doi = "doi:10.1007/b99975",
abstract = "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.",
notes = "http://www.dcs.ex.ac.uk/ideal04/
a) Cleveland heart data b) Thyroid data c) Pima Indians
diabetes data d) E. coli data
",
}
@InProceedings{Bredeche:2009:EA,
author = "N. Bredeche and E. Haasdijk and A. E. Eiben",
title = "On-Line, On-Board Evolution of Robot Controllers",
booktitle = "9th International Conference, Evolution Artificielle,
EA 2009",
year = "2009",
editor = "Pierre Collet and Nicolas Monmarche and Pierrick
Legrand and Marc Schoenauer and Evelyne Lutton",
volume = "5975",
series = "Lecture Notes in Computer Science",
pages = "110--121",
address = "Strasbourg, France",
month = oct # " 26-28",
publisher = "Springer",
note = "Revised Selected Papers",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-14155-3",
URL = "http://www.cs.vu.nl/~gusz/papers/2009-bredeche09ea_final2-LNCS.pdf",
doi = "doi:10.1007/978-3-642-14156-0",
size = "12 pages",
abstract = "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.",
notes = "EA'09 Published 2010",
}
@InProceedings{breeden:1999:UJE,
author = "Joseph L. Breeden and Todd W. Allen",
title = "Using an optimization toolkit for Java to evolve
market strategies for European seeds",
booktitle = "Late Breaking Papers at the 1999 Genetic and
Evolutionary Computation Conference",
year = "1999",
editor = "Scott Brave and Annie S. Wu",
pages = "57--64",
address = "Orlando, Florida, USA",
month = "13 " # jul,
keywords = "Genetic Algorithms",
notes = "GECCO-99LB",
}
@InProceedings{RibeiroZV07a,
author = "Jose Carlos Ribeiro and Mario Zenha-Rela and Francisco
{Fernandez de Vega}",
title = "An Evolutionary Approach for Performing Structural
Unit-Testing on Third-Party Object-Oriented Java
Software",
booktitle = "Proceedings of International Workshop on Nature
Inspired Cooperative Strategies for Optimization (NICSO
'07)",
year = "2007",
pages = "379--388",
editor = "Natalio Krasnogor and Giuseppe Nicosia and Mario
Pavone and David Pelta",
volume = "129",
series = "Studies in Computational Intelligence",
address = "Acireale, Italy",
month = "8-10 " # nov,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, SBSE",
bibsource = "http://www.sebase.org/sbse/publications/repository.html",
isbn13 = "978-3-540-78986-4",
URL = "http://jcbribeiro.googlepages.com/NICSO2007-053.pdf",
doi = "doi:10.1007/978-3-540-78987-1_34",
abstract = "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.",
}
@InProceedings{Bregieiro-Ribeiro:2008:JAEM,
author = "Jose Carlos {Bregieiro Ribeiro} and Mario Alberto
Zenha-Rela and Francisco {Fernandez de Vega}",
title = "eCrash: a framework for performing evolutionary
testing on third-party Java components",
booktitle = "I Jornadas sobre Algoritmos Evolutivos y
Metaheuristicas (JAEM 2007)",
year = "2007",
editor = "Enrique Alba and Francisco Herrera",
pages = "137--144",
address = "Zaragoza, Spain",
month = "11-14 " # sep,
keywords = "genetic algorithms, genetic programming, SBSE, STGP",
isbn13 = "978-84-9732-593-6",
URL = "http://jcbribeiro.googlepages.com/jribeiro_jaem07.pdf",
size = "8 pages",
abstract = "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.",
notes = "http://neo.lcc.uma.es/jaem07/ With CEDI 2007",
}
@InProceedings{Bregieiro-Ribeiro:2008:AST,
author = "Jose Carlos {Bregieiro Ribeiro} and Mario Alberto
Zenha-Rela and Francisco {Fernandez de Vega}",
title = "A strategy for evaluating feasible and unfeasible test
cases for the evolutionary testing of object-oriented
software",
booktitle = "AST '08: Proceedings of the 3rd international workshop
on Automation of software test",
year = "2008",
pages = "85--92",
address = "Leipzig, Germany",
publisher_address = "New York, NY, USA",
publisher = "ACM",
keywords = "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",
isbn13 = "978-1-60558-030-2",
URL = "http://jcbribeiro.googlepages.com/ast12-ribeiro.pdf",
doi = "doi:10.1145/1370042.1370061",
size = "8 pages",
abstract = "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.",
notes = "also known as \cite{1370061}",
}
@InProceedings{Bregieiro-Ribeiro:2008:gecco,
author = "Jose Carlos {Bregieiro Ribeiro} and Mario Alberto
Zenha-Rela and Francisco {Fernandez de Vega}",
title = "Strongly-typed genetic programming and purity
analysis: input domain reduction for evolutionary
testing problems",
booktitle = "GECCO '08: Proceedings of the 10th annual conference
on Genetic and evolutionary computation",
year = "2008",
editor = "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",
isbn13 = "978-1-60558-130-9",
pages = "1783--1784",
address = "Atlanta, GA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1783.pdf",
doi = "doi:10.1145/1389095.1389439",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "12-16 " # jul,
keywords = "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",
abstract = "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",
notes = "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 \cite{1389439}",
}
@InProceedings{Bregieiro-Ribeiro:2008:geccocomp,
author = "Jose Carlos {Bregieiro Ribeiro}",
title = "Search-based test case generation for object-oriented
java software using strongly-typed genetic
programming",
year = "2008",
editor = "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",
isbn13 = "978-1-60558-131-6",
booktitle = "GECCO-2008 Graduate Student Workshops",
pages = "1819--1822",
address = "Atlanta, GA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1819.pdf",
doi = "doi:10.1145/1388969.1388979",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "12-16 " # jul,
keywords = "genetic algorithms, genetic programming,
dynect-orientation, evolutionary testing, search-based
test case generation, strongly-Typed genetic
programming",
notes = "Distributed on CD-ROM at GECCO-2008
ACM Order Number 910081. Also known as \cite{1388979}",
}
@Article{BregieiroRibeiro2009,
author = "Jose Carlos {Bregieiro Ribeiro} and Mario Alberto
Zenha-Rela and Francisco {Fernandez de Vega}",
title = "Test Case Evaluation and Input Domain Reduction
Strategies for the Evolutionary Testing of
Object-Oriented Software",
journal = "Information and Software Technology",
year = "2009",
volume = "51",
number = "11",
pages = "1534--1548",
month = nov,
keywords = "genetic algorithms, genetic programming, Evolutionary
Testing, Search-Based Software Engineering, Test Case
Evaluation, Input Domain Reduction",
ISSN = "0950-5849",
doi = "doi:10.1016/j.infsof.2009.06.009",
URL = "http://www.sciencedirect.com/science/article/B6V0B-4WP47MR-2/2/798c73c2b9c5e1e9389b8a3491eac4f2",
size = "15 pages",
abstract = "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.{"}",
notes = "Third IEEE International Workshop on Automation of
Software Test (AST 2008); Eighth International
Conference on Quality Software (QSIC 2008)",
}
@InProceedings{DBLP:conf/gecco/RibeiroRV09,
author = "Jose Carlos {Bregieiro Ribeiro} and Mario {Zenha Rela}
and Francisco {Fernandez de Vega}",
title = "An adaptive strategy for improving the performance of
genetic programming-based approaches to evolutionary
testing",
booktitle = "GECCO '09: Proceedings of the 11th Annual conference
on Genetic and evolutionary computation",
year = "2009",
editor = "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",
pages = "1949--1950",
address = "Montreal",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "8-12 " # jul,
organisation = "SigEvo",
keywords = "genetic algorithms, genetic programming, Poster",
isbn13 = "978-1-60558-325-9",
bibsource = "DBLP, http://dblp.uni-trier.de",
doi = "doi:10.1145/1569901.1570253",
abstract = "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.",
notes = "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.",
}
@Article{Ribeiro20091534,
author = "Jose Carlos {Bregieiro Ribeiro} and Mario Alberto
Zenha-Rela and Francisco {Fernandez de Vega}",
title = "Test Case Evaluation and Input Domain Reduction
strategies for the Evolutionary Testing of
Object-Oriented software",
journal = "Information and Software Technology",
volume = "51",
number = "11",
pages = "1534--1548",
year = "2009",
note = "Third IEEE International Workshop on Automation of
Software Test (AST 2008); Eighth International
Conference on Quality Software (QSIC 2008)",
ISSN = "0950-5849",
doi = "doi:10.1016/j.infsof.2009.06.009",
URL = "http://www.sciencedirect.com/science/article/B6V0B-4WP47MR-2/2/798c73c2b9c5e1e9389b8a3491eac4f2",
keywords = "genetic algorithms, genetic programming, SBSE,
Evolutionary Testing, Search-Based Software
Engineering, Test Case Evaluation, Input Domain
Reduction",
abstract = "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.",
}
@InProceedings{Ribeiro:2010:EuroGP,
author = "Jose Carlos {Bregieiro Ribeiro} and Mario Alberto
Zenha-Rela and Francisco {Fernandez de Vega}",
title = "Enabling Object Reuse on Genetic Programming-based
Approaches to Object-Oriented Evolutionary Testing",
booktitle = "Proceedings of the 13th European Conference on Genetic
Programming, EuroGP 2010",
year = "2010",
editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara
Silva and Stephen Dignum and A. Sima Uyar",
volume = "6021",
series = "LNCS",
pages = "220--231",
address = "Istanbul",
month = "7-9 " # apr,
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, SBSE",
isbn13 = "978-3-642-12147-0",
doi = "doi:10.1007/978-3-642-12148-7_19",
abstract = "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.",
notes = "AT-nodes P-nodes \cite{lopez:2004:eurogp}. Java
Red-Black tree and vector classes. pop=25. Part of
\cite{Esparcia-Alcazar:2010:GP} EuroGP'2010 held in
conjunction with EvoCOP2010 EvoBIO2010 and
EvoApplications2010",
}
@InProceedings{Bremner:2010:ICES,
author = "Paul Bremner and Mohammad Samie and Gabriel Dragffy
and Tony Pipe and James Alfred Walker and Andy M.
Tyrrell",
title = "Evolving Digital Circuits Using Complex Building
Blocks",
booktitle = "Proceedings of the 9th International Conference
Evolvable Systems: From Biology to Hardware, ICES
2010",
year = "2010",
editor = "Gianluca Tempesti and Andy M. Tyrrell and Julian F.
Miller",
series = "Lecture Notes in Computer Science",
volume = "6274",
pages = "37--48",
address = "York",
month = sep # " 6-8",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-15322-8",
doi = "doi:10.1007/978-3-642-15323-5_4",
size = "12 pages",
abstract = "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.",
affiliation = "Bristol Robotics Laboratory, University of the West of
England, Bristol, BS16 1QY",
}
@InProceedings{Bremner:2011:EuroGP,
author = "Paul Bremner and Mohammad Samie and Anthony G. Pipe
and Gabriel Dragffy and Yang Liu",
title = "Evolving Cell Array Configurations Using {CGP}",
booktitle = "Proceedings of the 14th European Conference on Genetic
Programming, EuroGP 2011",
year = "2011",
month = "27-29 " # apr,
editor = "Sara Silva and James A. Foster and Miguel Nicolau and
Mario Giacobini and Penousal Machado",
series = "LNCS",
volume = "6621",
publisher = "Springer Verlag",
address = "Turin, Italy",
pages = "73--84",
organisation = "EvoStar",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-20406-7",
doi = "doi:10.1007/978-3-642-20407-4_7",
abstract = "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.",
notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in
conjunction with EvoCOP2011 EvoBIO2011 and
EvoApplications2011",
}
@InProceedings{Bremner:2011:MOoCCE,
title = "Multi-Objective Optimisation of Cell-Array Circuit
Evolution",
author = "Paul Bremner and Mohammad Samie and Anthony Pipe and
Andy Tyrrell",
pages = "440--446",
booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary
Computation",
year = "2011",
editor = "Alice E. Smith",
month = "5-8 " # jun,
address = "New Orleans, USA",
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming, cartesian
genetic programming, Multiobjective optimization,
Evolvable hardware and software",
abstract = "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.",
notes = "CEC2011 sponsored by the IEEE Computational
Intelligence Society, and previously sponsored by the
EPS and the IET.",
}
@InCollection{breunig:1995:LIPRGP,
author = "Markus M. Breunig",
title = "Location Independent Pattern Recognition using Genetic
Programming",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1995",
year = "1995",
editor = "John R. Koza",
pages = "29--38",
address = "Stanford, California, 94305-3079 USA",
month = "11 " # dec,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming, ADF",
ISBN = "0-18-195720-5",
URL = "http://www.dbs.informatik.uni-muenchen.de/~breunig/HomepageResearch/Papers/PatternRecog.pdf",
size = "10 pages",
abstract = "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.",
notes = "part of \cite{koza:1995:gagp}",
}
@InProceedings{Brezocnik:1997:DAAAM,
author = "Miran Brezocnik and Joze Balic",
title = "System for discovering and optimizung mathematical
models using genetic programming and genetic
algorithms",
booktitle = "Proceedings of the 8th International DAAAM Symposium",
year = "1997",
editor = "Branko Katalinic",
pages = "37--38",
month = "23-25 " # oct,
publisher = "DAAAM International",
email = "mbrezocnik@uni-mb.si",
ISBN = "3-901509-04-6",
address = "Dubrovnik, Croatia",
publisher_address = "Vienna",
keywords = "genetic algorithms, genetic programming, adaptive
systems, evolutionary computation",
abstract = "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.",
notes = "http://www.daaam.com/daaam/Past_Activities/DAAAM_International_Activities_1990-2005.pdf",
}
@InProceedings{Brezocnik:1997:ICDMMI,
author = "Miran Brezocnik and Joze Balic",
title = "Comparison of genetic programming with genetic
algorithm",
booktitle = "3rd International Conference Design to Manufacture in
Modern Industry: Design to manufacture in modern
industry",
year = "1997",
editor = "Anton Jezernik and Bojan Dolsak",
pages = "150--156",
month = sep,
publisher = "University of Maribor, Faculty of Mechanical
Engineering",
publisher_address = "Slovenia",
email = "mbrezocnik@uni-mb.si",
ISBN = "86-435-0192-1",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
}
@InProceedings{Brezocnik:1998:IAD,
author = "Miran Brezocnik and Joze Balic",
title = "A genetic programming approach for modelling of
self-organizing assembly systems",
booktitle = "Intelligent assembly and disassembly - IAD'98: A
proceedings volume from the IFAC Workshop",
year = "1998",
editor = "Peter Kopacek and Dragica Noe",
pages = "47--52",
address = "Bled, Slovenia",
publisher_address = "Oxford, UK",
month = "21-23 " # may,
organisation = "IFAC",
publisher = "Pergamon",
keywords = "genetic algorithms, genetic programming,
self-organising systems, intelligent manufacturing
systems, assembly, simulation",
ISBN = "0-08-043042-2",
URL = "http://www1.elsevier.com/homepage/saf/ifac/site/proceed_1998.htm",
URL = "http://books.elsevier.com/elsevier/?isbn=0080430422",
abstract = "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.",
}
@PhdThesis{Brezocnik:thesis,
author = "Miran Brezocnik",
title = "{MODELING} {OF} {TECHNOLOGICAL} {SYSTEMS} {BY} {THE}
{USE} {OF} {GENETIC} {METHODS}",
school = "University of Maribor, Faculty of Mechanical
Engineering",
year = "1998",
type = "phdthesis",
address = "Smetanova ulica 17, SI-2000 Maribor, Slovenia",
email = "mbrezocnik@uni-mb.si",
keywords = "genetic algorithms, genetic programming, intelligent
manufacturing systems, technological system, forming,
assembly, robots, self-organisation, genetic methods,
modelling, optimisation",
size = "129 pages",
abstract = "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.",
}
@Article{Brezocnik:2000:JTP,
author = "Miran Brezocnik and Joze Balic and Leo Gusel",
title = "Artificial intelligence approach to determination of
flow curve",
journal = "Journal for technology of plasticity",
year = "2000",
volume = "25",
number = "1-2",
pages = "1--7",
keywords = "genetic algorithms, genetic programming, forming, flow
curve, artificial intelligence",
ISSN = "0350-2368",
abstract = "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.",
notes = "http://www.scindeks.nbs.bg.ac.yu/arhiva.php?issn=0350-2368&je=en",
}
@Book{Brezocnik:book,
author = "Miran Brezocnik",
title_en = "Using genetic programming in intelligent manufacturing
systems",
title = "Uporaba genetskega programiranja v inteligentnih
proizvodnih sistemih",
publisher = "University of Maribor, Faculty of mechanical
engineering",
year = "2000",
address = "Maribor, Slovenia",
email = "mbrezocnik@uni-mb.si",
keywords = "genetic algorithms, genetic programming,
manufacturing, intelligent manufacturing systems,
modelling, assembly, metal forming, autonomous robot,
evolutionary algorithms",
ISBN = "86-435-0306-1",
URL = "http://maja.uni-mb.si/slo/Knjige/2000-03-mon/index.htm",
size = "160 pages",
note = "In Slovenian",
}
@Article{Brezocnik:2001:MPT,
author = "Miran Brezocnik and Joze Balic and Zlatko Kampus",
title = "Modeling of forming efficiency using genetic
programming",
journal = "Journal of Materials Processing Technology",
volume = "109",
pages = "20--29",
year = "2001",
number = "1-2",
month = "1 " # feb,
email = "joze.balic@uni-mb.si",
keywords = "genetic algorithms, genetic programming,
Metal-forming, Yield stress, Forming efficiency,
Modeling, Adaptation, Artificial intelligence",
ISSN = "0924-0136",
URL = "http://www.sciencedirect.com/science/article/B6TGJ-423HM9M-5/1/bcc93a13fbb04521236d3a8e16f8850b",
doi = "doi:10.1016/S0924-0136(00)00783-4",
abstract = "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]",
notes = "Journal of Materials Processing Technology
http://www.elsevier.com/wps/find/journaldescription.cws_home/505656/description#description",
}
@Article{Brezocnik:2001:RCIM,
author = "Miran Brezocnik and Joze Balic",
title = "A genetic-based approach to simulation of
self-organizing assembly",
journal = "Robotics and Computer-Integrated Manufacturing",
volume = "17",
pages = "113--120",
year = "2001",
number = "1-2",
month = feb,
keywords = "genetic algorithms, genetic programming, Intelligent
manufacturing systems, Self-organizing assembly,
Evolution",
ISSN = "0736-5845",
doi = "doi:10.1016/S0736-5845(00)00044-2",
URL = "http://www.sciencedirect.com/science/article/B6V4P-42DP1Y1-J/1/175033beb3ddb787b75c22253e5534c2",
abstract = "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.",
notes = "Robotics and Computer-Integrated Manufacturing
http://www.elsevier.com/wps/find/journaldescription.cws_home/704/description#description",
}
@InProceedings{Brezocnik:2001:RIM,
author = "Miran Brezocnik and Miha Kovacic",
title = "Survey of the evolutionary computation and its
application in manufacturing systems",
booktitle = "3rd International Conference on Revitalization and
Modernization of Production RIM 2001",
year = "2001",
editor = "Milan Jurkovic and Isak Karabegovic",
pages = "501--508",
address = "University of Bihac, Bihacu, Bosnia and Herzegovina",
month = sep,
organisation = "Bihac, Tehnieki fakultet",
keywords = "genetic algorithms, genetic programming",
ISBN = "9958-624-10-9",
}
@Article{Brezocnik:2002:JIM,
author = "Miran Brezocnik and Joze Balic and Karl Kuzman",
title = "Genetic programming approach to determining of metal
materials properties",
journal = "Journal of Intelligent Manufacturing",
year = "2002",
volume = "13",
number = "1",
pages = "5--17",
month = feb,
email = "joze.balic@uni-mb.si",
keywords = "genetic algorithms, genetic programming, materials
properties, metal forming, modeling,
self-organisation",
ISSN = "0956-5515",
URL = "http://www.springerlink.com/openurl.asp?genre=article&eissn=1572-8145&volume=13&issue=1&spage=5",
doi = "doi:10.1023/A:1013693828052",
abstract = "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.",
notes = "Journal of Intelligent Manufacturing
http://www.springeronline.com/sgw/cda/frontpage/0,11855,4-40528-70-35668245-0,00.html",
}
@InProceedings{Brezocnik:2002:AMME,
author = "Miran Brezocnik and Miha Kovacic",
title = "Prediction of surface roughness with genetic
programming",
booktitle = "Proceedings of the 11th International Scientific
Conference Achievements in Mechanical and Materials
Engineering, AMME'2002",
year = "2002",
editor = "Leszek A. Dobrzanski",
pages = "23--26",
keywords = "genetic algorithms, genetic programming",
ISBN = "83-914458-7-9",
notes = "http://www.wamme.org/index.php?id=37&PHPSESSID=8b9ce9355f0dbdaebee40f5d6ddec320
See also \cite{Brezocnik:2004:JMPT}",
}
@InCollection{Brezocnik:2002:DAAAM,
author = "Miran Brezocnik",
title = "On intelligent learning systems for next-generation
manufacturing",
booktitle = "DAAAM International Scientific Book 2002",
chapter = "6",
pages = "39--48",
publisher = "DAAAM International",
year = "2002",
volume = "1",
address = "Vienna",
month = oct,
editor = "Branko Katalinic",
email = "mbrezocnik@uni-mb.si",
keywords = "genetic algorithms, genetic programming, manufacturing
systems, artificial intelligence, learning,
evolutionary computation, emergence",
ISBN = "3-901509-30-5",
URL = "http://www.daaam.com/",
abstract = "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.",
notes = "http://www.daaam.com/daaam/Publications/Publications.htm",
}
@InProceedings{Brezocnik:TMT2002,
author = "Miran Brezocnik and Miha Kovacic",
title = "Integrated evolutionary computation environment for
optimizing and modeling of manufacturing processes",
booktitle = "6th International Research/Expert Conference {"}Trends
in the development of Machinery and Associated
Technology{"}",
year = "2002",
editor = "Safet Brdarevia and Sabahudin Ekinovia and Ramon
{Compamys Pascual} and Joan {Calvet Vivancos}",
pages = "TMT02--073",
address = "Neum, Bosnia and Herzegovina",
month = "18-22 " # sep,
organisation = "FACULTY OF MECHANICAL ENGINEERING IN ZENICA,
UNIVERSITY OF SARAJEVO, BOSNIA AND
HERZEGOVINA.
UNIVERSITAT POLITECNICA DE CATALUNYA BARCELONA, DEP.
D'ENGINYERIA MECANICA (SPAIN)",
keywords = "genetic algorithms, genetic programming, Poster",
ISBN = "9958-617-11-0",
notes = "http://www.mf.unze.ba/tmt2002/",
}
@Article{Brezocnik:2003:RCIM,
author = "Miran Brezocnik and Joze Balic and Zmago Brezocnik",
title = "Emergence of intelligence in next-generation
manufacturing systems",
journal = "Robotics and Computer-Integrated Manufacturing",
year = "2003",
volume = "19",
pages = "55--63",
number = "1-2",
abstract = "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.",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6V4P-47XW4VG-1/2/f88aada395a16da3031d89d272dae207",
month = feb # "-" # apr,
keywords = "genetic algorithms, genetic programming, Intelligent
manufacturing systems, Emergence, Learning",
doi = "doi:10.1016/S0736-5845(02)00062-5",
}
@InCollection{Brezocnik:2003:DAAAM,
author = "Miran Brezocnik and Miha Kovacic",
title = "Modelling of intelligent mobility for next-generation
manufacturing systems",
volume = "2",
booktitle = "DAAAM International Scientific Book 2003",
publisher = "DAAAM International Vienna",
year = "2003",
editor = "B. Katalinic",
pages = "95--102",
address = "Vienna",
month = jul,
keywords = "genetic algorithms, genetic programming",
ISBN = "3-901509-30-5",
abstract = "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.",
notes = "publication@daaam.com
http://www.daaam.com/daaam/Sc_Book/DAAAM_International_Scientific_Book_2006.htm",
}
@InProceedings{Brezocnik:2003:tmt,
author = "Miran Brezocnik and Miha Kovacic and Mirko Ficko",
title = "Genetic-based approach to predict surface roughness in
end milling",
booktitle = "7th International Research/Expert Conference {"}Trends
in the Development Machinery and Associated
Technology{"}",
year = "2003",
pages = "529--532",
address = "Barcelona, Spain",
month = "15-16 " # sep,
organisation = "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)",
keywords = "genetic algorithms, genetic programming",
ISBN = "9958-617-18-8",
notes = "http://www.mf.unze.ba/tmt2003/papers.htm",
}
@Article{Brezocnik:2004:AJME,
author = "Miran Brezocnik and Miha Kovacic and Mirko Ficko",
title = "Intelligent systems for next-generation
manufacturing",
journal = "Academic Journal of Manufacturing Engineering",
year = "2004",
volume = "2",
number = "1",
pages = "34--37",
keywords = "genetic algorithms, genetic programming",
ISSN = "1583-7904",
notes = "http://www.eng.utt.ro/auif/rev/issue/no-05/no-05.html",
}
@Article{Brezocnik:2004:IJAMT,
author = "Miran Brezocnik and Leo Gusel",
title = "Predicting stress distribution in cold-formed material
with genetic programming",
journal = "International journal of advanced manufacturing
technology",
year = "2004",
volume = "23",
number = "7-8",
pages = "467--474",
email = "mbrezocnik@uni-mb.si",
keywords = "genetic algorithms, genetic programming, metal
forming, stress distribution, modelling",
ISSN = "0268-3768",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0268-3768&volume=23&issue=7&spage=467",
doi = "doi:10.1007/s00170-003-1649-3",
abstract = "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.",
}
@InProceedings{Brezocnik:2004:TMT,
author = "Miran Brezocnik and Mirko Ficko and Miha Kovacic",
title = "Genetic based approach to predict surface roughness",
booktitle = "8th International Research/Expert Conference Trends in
the Development Machinery and Associated Technology",
year = "2004",
pages = "91--94",
address = "Neum, Bosnia and Herzegovina",
month = "15-19 " # sep,
keywords = "genetic algorithms, genetic programming, celno
frezanje, povrsinska hrapavost, napoved hrapavosti,
genetsko programiranje, end milling, surface roughness,
prediction of surface roughness",
ISBN = "9958-617-21-8",
abstract = "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.",
notes = "
http://cobiss.izum.si/scripts/cobiss?command=DISPLAY&base=COBIB&RID=9009686",
}
@Article{Brezocnik:2004:JMPT,
author = "M. Brezocnik and M. Kovacic and M. Ficko",
title = "Prediction of surface roughness with genetic
programming",
journal = "Journal of Materials Processing Technology",
year = "2004",
volume = "157-158",
pages = "28--36",
month = "20 " # dec # " 2004",
keywords = "genetic algorithms, genetic programming, Manufacturing
systems, Surface roughness; Milling, Evolutionary
algorithms",
ISSN = "0924-0136",
doi = "doi:10.1016/j.jmatprotec.2004.09.004",
abstract = "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.",
notes = "Originally in AMME 2000-2002 conference
\cite{Brezocnik: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)",
}
@InProceedings{Mechatronics2004_Abstract_026,
author = "Miha Kovacic and Miran Brezocnik and Joze Balic",
title = "Genetic Programming Approach for Autonomous Vehicles",
booktitle = "Mechatronics 2004 9th Mechatronics Forum International
Conference",
year = "2004",
address = "METU, Ankara, Turkey",
month = "30 " # aug # "-1 " # sep,
organisation = "Atilim University",
keywords = "genetic algorithms, genetic programming",
URL = "http://mechatronics.atilim.edu.tr/mechatronics2004/papers/Mechatronics2004_Abstract_026.pdf",
size = "1 page",
abstract = "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.",
notes = "University of Maribor, Faculty of Mechanical
Engineering, Maribor, Slovenia",
}
@Article{brezocnik_2004_AJME,
author = "Miran Brezocnik and Miha Kovacic and Joze Balic and
Bogdan Sovilj",
title = "Programming {CNC} measuring machines by genetic
algorithms",
journal = "Academic Journal of Manufacturing Engineering",
year = "2004",
volume = "2",
number = "4",
pages = "15--20",
keywords = "genetic algorithms, genetic programming, optimisation,
coordinate measuring machines, computer aided quality
control, evolutionary computation",
ISSN = "1583-7904",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/brezocnik_2004_AJME.pdf",
size = "6 pages",
abstract = "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",
notes = "http://www.eng.utt.ro/auif/
http://www.eng.utt.ro/auif/rev/issue/no-08/no-08.html#C2",
}
@Article{Brezocnik:2003:MMP,
author = "Miran Brezocnik and Miha Kovacic",
title = "Integrated genetic programming and genetic algorithm
approach to predict surface roughness",
journal = "Materials and Manufacturing Processes",
year = "2003",
volume = "18",
number = "3",
pages = "475--491",
month = may,
keywords = "genetic algorithms, genetic programming, Manufacturing
systems, Surface roughness, Milling",
doi = "doi:10.1081/AMP-120022023",
abstract = "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.",
}
@InProceedings{Brezocnik:2005:RIM,
author = "Miran Brezocnik and Bostjan Vaupotic and Janez Fridrih
and Ivo Pahole",
title = "Cost estimation for punch dies by genetic
programming",
booktitle = "RIM 2005 / 5th International scientific conference on
Production engineering",
year = "2005",
editor = "Milan Jurkovic and Vlatko Dolecek",
pages = "167--172",
month = "14-17 " # sep,
publisher = "Faculty of Technical Engineering, Bihac, Bosnia and
Hercegovina",
email = "mbrezocnik@uni-mb.si",
keywords = "genetic algorithms, genetic programming, punch dies,
cost estimation",
ISBN = "9958-9262-0-2",
abstract = "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.",
}
@Article{brezocnik:2005:MMP,
author = "Miran Brezocnik and Miha Kovacic and Leo Gusel",
title = "Comparison Between Genetic Algorithm and Genetic
Programming Approach for Modeling the Stress
Distribution",
journal = "Materials and Manufacturing Processes",
year = "2005",
volume = "20",
number = "3",
pages = "497--508",
month = may,
keywords = "genetic algorithms, genetic programming, Metal
forming, Stress distribution, System modelling",
ISSN = "1042-6914",
URL = "http://journalsonline.tandf.co.uk/openurl.asp?genre=article&issn=1042-6914&volume=20&issue=3&spage=497",
doi = "doi:10.1081/AMP-200053541",
abstract = "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.",
notes = "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",
}
@Article{Brezocnik:2006:AMME,
author = "Miran Brezocnik and Miha Kovacic and Matej Psenicnik",
title = "Prediction of steel machinability by genetic
programming",
journal = "Journal of achievements in materials and manufacturing
engineering",
year = "2006",
volume = "16",
number = "1-2",
pages = "107--113",
month = may # "-" # jun,
note = "Special Issue of CAM3S'2005",
keywords = "genetic algorithms, genetic programming, Steel
machinability, Extra machinability, Modelling",
issn_ = "Y505-3994 invalid checksum",
URL = "http://157.158.19.167/papers_cams05/1123.pdf",
size = "7 pages",
abstract = "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.",
notes = "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",
}
@Article{Briand:2006:GPEM,
author = "Lionel C. Briand and Yvan Labiche and Marwa Shousha",
title = "Using genetic algorithms for early schedulability
analysis and stress testing in real-time systems",
journal = "Genetic Programming and Evolvable Machines",
year = "2006",
volume = "7",
number = "2",
pages = "145--170",
month = aug,
note = "Special Issue: Best of GECCO 2005",
keywords = "genetic algorithms, Software verification and
validation, Schedulability theory",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-006-9003-9",
abstract = "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.",
}
@InProceedings{Briggs:2006:ASPGP,
title = "Functional genetic programming with combinators",
author = "Forrest Briggs and Melissa O'Neill",
booktitle = "Proceedings of the Third Asian-Pacific workshop on
Genetic Programming",
year = "2006",
editor = "The Long Pham and Hai Khoi Le and Xuan Hoai Nguyen",
pages = "110--127",
ISSN = "18590209",
address = "Military Technical Academy, Hanoi, VietNam",
keywords = "genetic algorithms, genetic programming",
URL = "http://sc.snu.ac.kr/courses/2006/fall/pg/aai/GP/forrest/fsb-meo-combs.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/aspgp06/fsb-meo-combs.pdf",
size = "18 pages",
abstract = "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].",
notes = "http://www.aspgp.org",
}
@Article{Briggs:2008:IJKBIES,
author = "Forrest Briggs and Melissa O'Neill",
title = "Functional genetic programming and exhaustive program
search with combinator expressions",
journal = "International Journal of Knowledge-Based and
Intelligent Engineering Systems",
year = "2008",
volume = "12",
number = "1",
pages = "47--68",
keywords = "genetic algorithms, genetic programming",
ISSN = "1327-2314",
publisher = "IOS Press",
URL = "http://iospress.metapress.com/content/u6l4j13p67w66370/",
abstract = "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.",
notes = "KES",
}
@InProceedings{briney+karpinski:2003:gecco:workshop,
title = "An Interdisciplinary Investigation of the Evolution
and Maintenance of Conditional Strategies in Chthamalus
anisopoma, using Genetic Programming and a Quantitative
Genetic Model",
author = "Kristin Briney and Tod Karpinski",
pages = "258--261",
booktitle = "{GECCO 2003}: Proceedings of the Bird of a Feather
Workshops, Genetic and Evolutionary Computation
Conference",
editor = "Alwyn M. Barry",
year = "2003",
month = "11 " # jul,
publisher = "AAAI",
address = "Chigaco",
publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
notes = "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",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{brizuela:1999:ADSGAJSSP,
author = "Carlos A. Brizuela and Nobuo Sannomiya",
title = "A Diversity Study in Genetic Algorithms for Job Shop
Scheduling Problems",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "75--82",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-333.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-333.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InCollection{brock:1994:ers,
author = "Oliver Brock",
title = "Evolving Reusable Subroutines for Genetic
Programming",
booktitle = "Artificial Life at Stanford 1994",
year = "1994",
editor = "John R. Koza",
pages = "11--19",
address = "Stanford, California, 94305-3079 USA",
month = jun,
organisation = "Stanford University",
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-182105-2",
URL = "http://robotics.stanford.edu/users/oli/PAPERS/a-life.ps",
URL = "http://citeseer.ist.psu.edu/156902.html",
abstract = "Although automatically defined functions (ADFs) are
able to significantly reduce the computational effort
required in genetic programming, reasonably di{\AE}cult
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.",
notes = "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.",
}
@InProceedings{Brooks92RR9,
title = "Artificial Life and Real Robots",
year = "1992",
pages = "3--10",
author = "Rodney A. Brooks",
booktitle = "Toward a Practice of Autonomous Systems: Proceedings
of the First European Conference on Artificial Life",
editor = "Francisco J. Varela and Paul Bourgine",
address = "Cambridge, MA, USA",
publisher = "MIT Press",
keywords = "genetic algorithms, genetic programming",
URL = "http://people.csail.mit.edu/brooks/papers/real-robots.pdf",
size = "9 pages",
abstract = "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.",
}
@TechReport{broughton:1998:e3DwlsGPwww,
author = "T. Broughton and P. Coates and H. Jackson",
title = "Exploring 3{D} design worlds using Lindenmayer systems
and Genetic Programming",
institution = "University of East London",
year = "1998",
keywords = "genetic algorithms, genetic programming",
URL = "http://homepages.uel.ac.uk/0483p/chapter12.html",
notes = "www info only",
}
@InCollection{broughton:1999:e3DwlsGPwww,
author = "T. Broughton and P. S. Coates and H. Jackson",
title = "Exploring Three-dimensional design worlds using
Lindenmeyer Systems and Genetic Programming",
booktitle = "Evolutionary Design Using Computers",
publisher = "Academic press",
year = "1999",
editor = "Peter Bentley",
chapter = "14",
pages = "323--341",
address = "London, UK",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-12-089070-4",
URL = "http://www.cs.ucl.ac.uk/staff/P.Bentley/evdes.html",
abstract = "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, \cite{coates: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",
notes = "
",
}
@Article{brown:1997:GPsoccer,
author = "Janelle Brown",
title = "{AI}, Teamwork is Goal of Robot Soccer Tourney",
journal = "Wired News",
year = "1997",
volume = "5",
number = "10",
month = "3:04pm PDT 26 " # aug,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.wired.com/culture/lifestyle/news/1997/08/6388",
size = "1 page",
abstract = "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.",
notes = "Report on RoboCup robot competition (held at IJCAI
1997 in Nagoya, Japan) http://www.robocup.org/RoboCup/
see also \cite{luke:1997:csstcGP} and
http://www.cs.umd.edu/users/seanl/soccerbots/",
}
@InProceedings{Brown:2010:ANNIE,
author = "Joseph A. Brown and Daniel Ashlock",
title = "Using Evolvable Regressors to Partition Data",
booktitle = "ANNIE 2010, Intelligent Engineering Systems through
Artificial Neural Networks",
year = "2010",
editor = "Cihan H. Dagli",
volume = "20",
pages = "187--194",
address = "St. Louis, Mo, USA",
month = nov # " 1-3",
organisation = "Smart Engineering Systems Laboratory, Systems
Engineering Graduate Programs, Missouri University of
Science and Technology, 600 W. 14th St., Rolla, MO
65409 USA",
publisher = "ASME",
keywords = "genetic algorithms, genetic programming",
isbn13 = "9780791859599",
URL = "http://www.uoguelph.ca/~jbrown16/EvolRegress.pdf",
URL = "http://asmedl.aip.org/ebooks/asme/asme_press/859599/859599_paper24",
doi = "doi:10.1115/1.859599.paper24",
abstract = "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.",
notes = "ASME Order Number: 859599",
}
@Article{Brown:2010:JCP,
author = "W. Michael Brown and Aidan P. Thompson and Peter A.
Schultz",
title = "Efficient hybrid evolutionary optimization of
interatomic potential models",
journal = "Journal of Chemical Physics",
year = "2010",
volume = "132",
number = "2",
pages = "024108",
keywords = "genetic algorithms, genetic programming, potential
energy functions, search problems",
ISSN = "1089-7690",
doi = "doi:10.1063/1.3294562",
size = "13 pages",
abstract = "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.",
notes = "34.20.Cf
Department of Multiscale Dynamic Material Modeling,
Sandia National Laboratories, Albuquerque, New Mexico
87185-1322, USA",
}
@InProceedings{browncribbs:1996:nand,
author = "H. {Brown Cribbs III} and Robert E. Smith",
title = "Classifier System Renaissance: New Analogies, New
Directions",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "Classifier Systems, Genetic Algorithms",
pages = "547--552",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96 Classifier paper",
}
@PhdThesis{CameronBrowne:thesis,
author = "Cameron Browne",
title = "Automatic Generation and Evaluation of Recombination
Games",
school = "Faculty of Information Technology, Queensland
University of Technology",
year = "2008",
address = "Australia",
month = feb,
keywords = "genetic algorithms, genetic programming,
Combinatorial, Games, Design, Aesthetics, Evolutionary,
Search, Yavalath",
URL = "http://www.cameronius.com/cv/publications/thesis-2.47.zip",
size = "251 pages",
abstract = "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.",
}
@Book{CameronBrowne:book,
author = "Cameron Browne",
title = "Evolutionary Game Design",
publisher = "Springer",
year = "2011",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4471-2178-7",
URL = "http://www.springer.com/computer/ai/book/978-1-4471-2178-7",
doi = "doi:10.1007/978-1-4471-2179-4",
abstract = "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",
notes = "Softcover",
size = "122 pages",
}
@Misc{browne:1996:bsc,
author = "David Browne",
title = "Vision-Based Obstacle Avoidance: {A} Coevolutionary
Approach",
school = "Department of Software Development, Monash
University",
year = "1996",
type = "Bachelor of Computing with Honours",
address = "Australia",
month = oct,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.csse.monash.edu.au/hons/projects/1996/David.Browne/",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/browne/browne_thesis.ps.gz",
size = "147 pages",
abstract = "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.",
}
@Article{Browne:2010:ACISC,
author = "Nigel P. A. Browne and Marcus V. {dos Santos}",
title = "Adaptive Representations for Improving Evolvability,
Parameter Control, and Parallelization of Gene
Expression Programming",
journal = "Applied Computational Intelligence and Soft
Computing",
year = "2010",
volume = "2010",
pages = "Article ID 409045",
keywords = "genetic algorithms, genetic programming, gene
expression programming",
URL = "http://downloads.hindawi.com/journals/acisc/2010/409045.pdf",
doi = "doi:10.1155/2010/409045",
size = "19 pages",
abstract = "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.",
}
@InProceedings{bruce:1996:agOOpGP,
author = "Wilker Shane Bruce",
title = "Automatic Generation of Object-Oriented Programs Using
Genetic Programming",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming, memory",
pages = "267--272",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/12859/http:zSzzSzwww.scis.nova.eduzSz~brucewszSzPUBLICATIONSzSzgp96.pdf/bruce96automatic.pdf",
URL = "http://citeseer.ist.psu.edu/bruce96automatic.html",
size = "6 pages",
abstract = "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...",
notes = "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
\cite{bruce:thesis}.",
}
@PhdThesis{bruce:thesis,
author = "Wilker Shane Bruce",
title = "The Application of Genetic Programming to the
Automatic Generation of Object-Oriented Programs",
school = "School of Computer and Information Sciences, Nova
Southeastern University",
year = "1995",
address = "3100 SW 9th Avenue, Fort Lauderdale, Florida 33315,
USA",
month = Dec,
keywords = "genetic algorithms, genetic programming, memory",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/bruce.thesis.ps.gz",
size = "664 pages",
}
@InProceedings{bruce:1997:lprsbGPADF,
author = "Wilker Shane Bruce",
title = "The Lawnmower Problem Revisited: Stack-Based Genetic
Programming and Automatically Defined Functions",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "52--57",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
broken = "http://www.scis.nova.edu/~brucews/PUBLICATIONS/gp-97.ps",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/12859/http:zSzzSzwww.scis.nova.eduzSz~brucewszSzPUBLICATIONSzSzgp97.pdf/bruce97lawnmower.pdf",
URL = "http://citeseer.ist.psu.edu/bruce97lawnmower.html",
size = "6 pages",
abstract = "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...",
notes = "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{"}.",
}
@InProceedings{brucherseifer:2001:EuroGP,
author = "Eva Brucherseifer and Peter Bechtel and Stephan Freyer
and Peter Marenbach",
title = "An Indirect Block-Oriented Representation for Genetic
Programming",
booktitle = "Genetic Programming, Proceedings of EuroGP'2001",
year = "2001",
editor = "Julian F. Miller and Marco Tomassini and Pier Luca
Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
William B. Langdon",
volume = "2038",
series = "LNCS",
pages = "268--279",
address = "Lake Como, Italy",
publisher_address = "Berlin",
month = "18-20 " # apr,
organisation = "EvoNET",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming,
Block-oriented representation, Biotechnology, Process
modelling, Controller design, Causality",
ISBN = "3-540-41899-7",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=268",
size = "12 pages",
abstract = "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.",
notes = "EuroGP'2001, part of \cite{miller:2001:gp}",
}
@Article{bruhn:2002:ECJ,
author = "Peter Bruhn and Andreas Geyer-Schulz",
title = "Genetic Programming over Context-Free Languages with
Linear Constraints for the Knapsack Problem: First
Results",
journal = "Evolutionary Computation",
year = "2002",
volume = "10",
number = "1",
pages = "51--74",
month = "Spring",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, grammar-based genetic, programming,
combinatorial, optimization, context-free grammars,
with linear constraints, knapsack problems",
URL = "http://www.ingentaconnect.com/content/mitpress/evco/2002/00000010/00000001/art00004",
doi = "doi:10.1162/106365602317301772",
abstract = "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.",
}
@InProceedings{Brumby:1999:SPIE,
author = "S. P. Brumby and J. Theiler and S. J. Perkins and N.
R. Harvey and J. J. Szymanski and J. J. Bloch and M.
Mitchell",
title = "Investigation of image feature extraction by a genetic
algorithm",
booktitle = "Applications and Science of Neural Networks, Fuzzy
Systems, and Evolutionary Computation II, Proceedings
of SPIE",
year = "1999",
editor = "Bruno Bosacchi and David B. Fogel and James C.
Bezdek",
volume = "3812",
pages = "24--31",
month = "19-20 " # jul,
keywords = "genetic algorithms, genetic programming",
notes = "http://www.spie.org/web/meetings/programs/sd99/confs/3812.html
Los Alamos National Lab; Santa Fe Institute [3812-03]",
}
@InProceedings{Brumby:2000:SPIE,
author = "S. P. Brumby and N. R. Harvey and S. Perkins and R. B.
Porter and J. J. Szymanski and J. Theiler and J. J.
Bloch",
title = "A genetic algorithm for combining new and existing
image processing tools for multispectral imagery",
booktitle = "Algorithms for Multispectral, Hyperspectral, and
Ultraspectral Imagery VI. Proceedings of SPIE",
year = "2000",
editor = "Sylvia S. Shen and Michael R. Descour",
volume = "4049",
pages = "480--490",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{Brumby:2001:SPIE,
author = "S. P. Brumby and J. J. Bloch and N. R. Harvey and J.
Theiler and S. Perkins and A. C. Young and J. J.
Szymanski",
title = "Evolving forest fire burn severity classification
algorithms for multi-spectral imagery",
booktitle = "In Algorithms for Multispectral, Hyperspectral, and
Ultraspectral Imagery VII, Proceedings of SPIE",
year = "2001",
editor = "Sylvia S. Shen and Michael R. Descour",
volume = "4381",
pages = "236--245",
keywords = "genetic algorithms, genetic programming, Multispectral
imagery, Supervised classification, Forest fire,
Wildfire, GENIE, Aladdin",
URL = "http://public.lanl.gov/perkins/webdocs/brumby.aerosense01.pdf",
size = "10 pages",
abstract = "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.",
notes = "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'",
}
@InProceedings{Brumby:2001:FUSION,
author = "Steven P. Brumby and James Theiler and Simon Perkins
and Neal R. Harvey and John J. Szymanski",
title = "Genetic programming approach to extracting features
from remotely sensed imagery",
booktitle = "FUSION 2001: Fourth International Conference on Image
Fusion",
year = "2001",
address = "Montreal, Quebec, Canada",
month = "7-10 " # aug,
email = "brumby@lanl.gov",
keywords = "genetic algorithms, genetic programming, Evolutionary
Computation, Image Processing, Remote Sensing,
Multispectral Imagery, Panchromatic imagery",
URL = "http://public.lanl.gov/perkins/webdocs/brumbyFUSION2001.pdf",
size = "8 pages",
abstract = "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.",
notes = "oai:CiteSeerPSU:567526 seems to be wrong",
}
@InProceedings{oai:CiteSeerPSU:445835,
author = "Steven P. Brumby and James Theiler and Jeffrey J.
Bloch and Neal R. Harvey and Simon Perkins and John J.
Szymanski and A. Cody Young",
title = "Evolving land cover classification algorithms for
multispectral and multitemporal imagery",
booktitle = "Proc. SPIE Imaging Spectrometry VII",
year = "2002",
editor = "Michael R. Descour and Sylvia S. Shen",
volume = "4480",
keywords = "genetic algorithms, genetic programming, Feature
Extraction, Supervised classification, K-means
clustering, Multi-spectral imagery, Land cover,
Wildfire",
URL = "http://public.lanl.gov/jt/Papers/brumby_SPIE4480-14.pdf",
URL = "http://citeseer.ist.psu.edu/445835.html",
abstract = "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.",
notes = "Los Alamos National Lab.",
}
@Article{Bryant:2001:ETAI,
author = "C. H. Bryant and S. H. Muggleton and S. G. Oliver and
D. B. Kell and P. G. K. Reiser and R. D. King",
title = "Combining Inductive Logic Programming, Active Learning
and Robotics to Discover the Function of Genes",
journal = "Electronic Transactions in Artificial Intelligence",
year = "2001",
volume = "6",
number = "12",
keywords = "ILP",
URL = "http://www.stancomb.co.uk/~prr/Papers/bryant-ETAI.ps",
URL = "http://www.stancomb.co.uk/~prr/Papers/bryant-ETAI.pdf",
notes = "online only?",
}
@Article{1676819,
author = "Randal E. Bryant",
title = "Graph-Based Algorithms for Boolean Function
Manipulation",
journal = "IEEE Transactions on Computers",
year = "1986",
volume = "C-35",
number = "8",
pages = "677--691",
month = aug,
keywords = "DEC VAX, Boolean functions, binary decision diagrams,
logic design verification, symbolic manipulation",
ISSN = "0018-9340",
doi = "doi:10.1109/TC.1986.1676819",
size = "15 pages",
abstract = "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.",
notes = "NOT GP, exhaustive depth first search?",
}
@Article{buason:2005:GPEM,
author = "Gunnar Buason and Nicklas Bergfeldt and Tom Ziemke",
title = "Brains, Bodies, and Beyond: Competitive Co-Evolution
of Robot Controllers, Morphologies and Environments",
journal = "Genetic Programming and Evolvable Machines",
year = "2005",
volume = "6",
number = "1",
pages = "25--51",
month = mar,
keywords = "genetic algorithms, neuronal robot controller, CCE,
khepera, YAKS simulator",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-005-7618-x",
abstract = "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.",
notes = "20/100 rule",
}
@InProceedings{eurogp06:BuchsbaumVossner,
author = "Thomas Buchsbaum and Siegfried V{\"o}ssner",
title = "Information-Dependent Switching of Identification
Criteria in a Genetic Programming System for System
Identification",
editor = "Pierre Collet and Marco Tomassini and Marc Ebner and
Steven Gustafson and Anik\'o Ek\'art",
booktitle = "Proceedings of the 9th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3905",
year = "2006",
address = "Budapest, Hungary",
month = "10 - 12 " # apr,
organisation = "EvoNet",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-33143-3",
pages = "300--309",
URL = "http://link.springer.de/link/service/series/0558/papers/3905/39050300.pdf",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in
conjunction with EvoCOP2006 and EvoWorkshops2006",
}
@InProceedings{Buchsbaum:2007:cec,
author = "Thomas Buchsbaum",
title = "Toward a Winning {GP} Strategy for Continuous
Nonlinear Dynamical System Identification",
booktitle = "2007 IEEE Congress on Evolutionary Computation",
year = "2007",
editor = "Dipti Srinivasan and Lipo Wang",
pages = "1269--1275",
address = "Singapore",
month = "25-28 " # sep,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "1-4244-1340-0",
file = "1490.pdf",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and
the IET.
IEEE Catalog Number: 07TH8963C",
}
@InCollection{Buckley:2010:Chiong,
author = "Muneer Buckley and Zbigniew Michalewicz and Ralf
Zurbruegg",
title = "An Application of Genetic Programming to Forecasting
Foreign Exchange Rates",
booktitle = "Nature-Inspired Informatics for Intelligent
Applications and Knowledge Discovery: Implications in
Business, Science, and Engineering",
publisher = "IGI Global",
year = "2010",
editor = "Raymond Chiong",
chapter = "2",
pages = "26--48",
keywords = "genetic algorithms, genetic programming",
isbn13 = "1605667056",
URL = "http://hdl.handle.net/2440/54525",
doi = "doi:10.4018/978-1-60566-705-8",
bibsource = "OAI-PMH server at digital.library.adelaide.edu.au",
oai = "oai:digital.library.adelaide.edu.au:2440/54525",
notes = "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)",
}
@InCollection{Buckley:2009:niiiakd,
title = "An Application of Genetic Programming to Forecasting
Foreign Exchange Rates",
author = "Muneer Buckley and Zbigniew Michalewicz and Ralf
Zurbruegg",
publisher = "IGI Global",
year = "2009",
booktitle = "Nature-Inspired Informatics for Intelligent
Applications and Knowledge Discovery: Implications in
Business, Science, and Engineering",
editor = "Raymond Chiong",
chapter = "2",
pages = "26--48",
keywords = "genetic algorithms, genetic programming",
isbn13 = "1605667056",
URL = "http://www.igi-global.com/bookstore/chapter.aspx?titleid=36310",
URL = "http://hdl.handle.net/2440/54525",
oai = "oai:digital.library.adelaide.edu.au:2440/54525",
abstract = "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.",
}
@InProceedings{bui:286,
author = "Tai D. Bui and Alan A. Smith",
editor = "Don Phelps and Gerald Sehlke",
title = "Water Resource Engineers and Environmental
Hydraulics",
publisher = "ASCE",
year = "2001",
volume = "111",
pages = "286--286",
booktitle = "World Water Congress 2001",
address = "Orlando, Florida, USA",
month = "20-24 " # may,
keywords = "genetic algorithms, genetic programming",
URL = "http://link.aip.org/link/?ASC/111/286/1",
doi = "doi:10.1061/40569(2001)286",
abstract = "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.",
notes = "number = 40569 Conference Proceeding Paper",
}
@InCollection{Bui:1997:s8p,
author = "Thai Bui",
title = "Solving the 8-Puzzle with Genetic Programming",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1997",
publisher = "Stanford Bookstore",
year = "1997",
editor = "John R. Koza",
pages = "11--17",
address = "Stanford, California, 94305-3079 USA",
month = "17 " # mar,
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-205981-2",
notes = "part of \cite{koza:1997:GAGPs}",
}
@InProceedings{Buk:2009:ICANNGA,
author = "Zdenek Buk and Jan Koutni and Miroslav Snorek",
title = "{NEAT} in Hyper{NEAT} Substituted with Genetic
Programming",
year = "2009",
booktitle = "9th International Conference on Adaptive and Natural
Computing Algorithms, ICANNGA 2009",
editor = "Mikko Kolehmainen and Pekka Toivanen and Bartlomiej
Beliczynski",
series = "Lecture Notes in Computer Science",
volume = "5495",
pages = "243--252",
address = "Kuopio, Finland",
month = "23-25 " # apr,
publisher = "Springer",
note = "Revised selected papers",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-04920-0",
doi = "doi:10.1007/978-3-642-04921-7_25",
abstract = "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.",
notes = "ICANNGA 2009",
}
@InProceedings{Bukhtoyarov:2010:cec,
author = "Vladimir V. Bukhtoyarov and Olga E. Semenkina",
title = "Comprehensive evolutionary approach for neural network
ensemble automatic design",
booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-6910-9",
abstract = "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.",
doi = "doi:10.1109/CEC.2010.5586516",
notes = "WCCI 2010. Also known as \cite{5586516}",
}
@InProceedings{bull:1996:SandS,
author = "Lawrence Bull and Terence C. Fogarty",
title = "Evolutionary Computing in Multi-Agent Environments:
Speciation and Symbiogenesis",
editor = "Hans-Michael Voigt and Werner Ebeling and Ingo
Rechenberg and Hans-Paul Schwefel",
booktitle = "Parallel Problem Solving From Nature IV. Proceedings
of the International Conference on Evolutionary
Computation",
year = "1996",
publisher = "Springer-Verlag",
volume = "1141",
series = "LNCS",
pages = "12--21",
address = "Berlin, Germany",
publisher_address = "Heidelberg, Germany",
month = "22-26 " # sep,
keywords = "genetic algorithms",
ISBN = "3-540-61723-X",
doi = "doi:10.1007/3-540-61723-X_965",
size = "10 pages",
abstract = "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.",
notes = "http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4
Wall climbing quadruped robot simulation",
}
@InProceedings{Bull:1997:ecmaee,
author = "Larry Bull and Owen Holland",
title = "Evolutionary Computing in Multi-Agent Environments:
Eusociality",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "Genetic Algorithms",
pages = "347--352",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
notes = "GP-97",
}
@InProceedings{bull:1999:OZSCDM,
author = "Larry Bull",
title = "On using {ZCS} in a Simulated Continuous
Double-Auction Market",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "83--90",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-806.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-806.pdf",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{Bull:2009:eurogp,
author = "Larry Bull and Richard Preen",
title = "On Dynamical Genetic Programming: Random {Boolean}
Networks in Learning Classifier Systems",
booktitle = "Proceedings of the 12th European Conference on Genetic
Programming, EuroGP 2009",
year = "2009",
editor = "Leonardo Vanneschi and Steven Gustafson and Alberto
Moraglio and Ivanoe {De Falco} and Marc Ebner",
volume = "5481",
series = "LNCS",
pages = "37--48",
address = "Tuebingen",
month = apr # " 15-17",
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-01180-1",
doi = "doi:10.1007/978-3-642-01181-8_4",
notes = "Part of \cite{conf/eurogp/2009} EuroGP'2009 held in
conjunction with EvoCOP2009, EvoBIO2009 and
EvoWorkshops2009",
}
@Article{Bull:2009:IJPEDS,
author = "Larry Bull",
title = "On dynamical genetic programming: simple {Boolean}
networks in learning classifier systems",
journal = "International Journal of Parallel, Emergent and
Distributed Systems",
year = "2009",
volume = "24",
number = "5",
pages = "421--442",
month = oct,
publisher = "Taylor \& Francis",
keywords = "genetic algorithms, genetic programming, discrete,
dynamical systems, evolution, multiplexer, unorganised
machines",
ISSN = "1744-5760",
doi = "doi:10.1080/17445760802660387",
abstract = "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.",
notes = "a Department of Computer Science, University of the
West of England, Bristol, UK Formerly Parallel
Algorithms and Applications",
}
@Article{bullard:1998:MD,
author = "James B. Bullard and John Duffy",
title = "learning and the Stability of Cycles",
journal = "Macroeconomic Dynamics",
year = "1998",
volume = "2",
number = "1",
pages = "22--48",
month = mar,
keywords = "genetic algorithms, Learning, Multiple Equilibria,
Coordination",
size = "27 pages",
abstract = "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.",
notes = "Also available as working paper 1995-006B
http://research.stlouisfed.org/wp/1995/95-006.pdf",
}
@Article{bullard:1998:JEDC,
author = "James Bullard and John Duffy",
title = "A model of learning and emulation with artificial
adaptive agents",
journal = "Journal of Economic Dynamics and Control",
year = "1998",
volume = "22",
number = "2",
pages = "179--207",
month = feb,
keywords = "genetic algorithms, Learning, Coordination,
Overlapping generations",
doi = "doi:10.1016/S0165-1889(97)00072-9",
abstract = "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.",
notes = "JEL classification codes: D83; C63
Also available as working paper 1994-014C
http://research.stlouisfed.org/wp/1994/94-014.pdf",
}
@Article{buontempo:2005:CIM,
author = "Frances V. Buontempo and Xue Zhong Wang and Mulaisho
Mwense and Nigel Horan and Anita Young and Daniel
Osborn",
title = "Genetic Programming for the Induction of Decision
Trees to Model Ecotoxicity Data",
journal = "Journal of Chemical Information and Modeling",
year = "2005",
volume = "45",
pages = "904--912",
note = "ASAP article. Web Release Date: May 12, 2005",
keywords = "genetic algorithms, genetic programming, decision
trees, model ecotoxicity, EPTree, C5.0 See5, recursive
partitioning, S-Plus, SIMCA-P 8.0, QSAR",
doi = "doi:10.1021/ci049652n",
size = "9 pages",
abstract = "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.",
notes = "
http://pubs.acs.org/journals/jcisd8/index.html
S1549-9596(04)09652-4 ACS Publications Division
cites EPtree \cite{delisle: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.",
}
@InProceedings{wssec-rb-final,
author = "Robert Burbidge",
title = "A Contribution to the Foundations of {AI}: Genetic
Programming and Support Vector Machines",
booktitle = "Workshop and Summer School on Evolutionary Computing
Lecture Series by Pioneers",
year = "2008",
editor = "T. M. McGinnity",
address = "University of Ulster",
month = "18-20 " # aug,
organisation = "School of Computing and Intelligent Systems,
University of Ulster",
keywords = "genetic algorithms, genetic programming, SVM",
URL = "http://users.aber.ac.uk/rvb/wssec-rb-final.pdf",
size = "4 pages",
abstract = "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.",
notes = "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.",
}
@InProceedings{Burbidge:2009:TAROS,
author = "Robert Burbidge and Joanne H. Walker and Myra S.
Wilson",
title = "A Grammar for Evolution of a Robot Controller",
booktitle = "TAROS 2009 Towards Autonomous Robotic Systems",
year = "2009",
editor = "Theocharis Kyriacou and Ulrich Nehmzow and Chris
Melhuish and Mark Witkowski",
series = "Intelligent Systems Research Centre Technical Report
Series",
pages = "182--189",
address = "University of Ulster, Londonderry, United Kingdom",
month = aug # " 31 - " # sep # " 2",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, robot control",
URL = "http://isrc.ulster.ac.uk/images/stories/publications/report-series/TAROS_2009.pdf",
size = "8 pages",
abstract = "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.",
notes = "http://www.infm.ulst.ac.uk/~ulrich/Taros09/",
}
@InProceedings{Burbidge:2009:IROS,
author = "Robert Burbidge and Joanne H. Walker and Myra S.
Wilson",
title = "Grammatical evolution of a robot controller",
booktitle = "IEEE/RSJ International Conference on Intelligent
Robots and Systems, IROS 2009",
year = "2009",
month = "11-15 " # oct,
address = "St. Louis, USA",
pages = "357--362",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, Khepera robot, artificial intelligence,
autonomous mobile robot, evolutionary algorithm,
evolutionary technique, onboard controller, robot
controller, grammars, mobile robots",
doi = "doi:10.1109/IROS.2009.5354411",
abstract = "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.",
notes = "Also known as \cite{5354411}",
}
@Article{Burgess:2001:IST,
author = "Colin J. Burgess and Martin Lefley",
title = "Can genetic programming improve software effort
estimation? {A} comparative evaluation",
year = "2001",
journal = "Information and Software Technology",
volume = "43",
number = "14",
pages = "863--873",
abstract = "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.",
keywords = "genetic algorithms, genetic programming, Case-based
reasoning, Machine learning, Neural networks, Software
effort estimation",
URL = "http://www.sciencedirect.com/science/article/B6V0B-44D4196-7/1/20f45986fc0a4827ad09169178379d73",
doi = "doi:10.1016/S0950-5849(01)00192-6",
abstract-url = "http://www.cs.bris.ac.uk/Publications/pub_info.jsp?id=1000586",
}
@InCollection{2000240,
author = "C. J. Burgess and M. Lefley",
title = "Can Genetic Programming improve Software Effort
Estimation? {A} Comparative Evaluation",
booktitle = "Machine Learning Applications In Software Engineering:
Series on Software Engineering and Knowledge
Engineering",
editor = "Du Zhang and Jeffrey J. P. Tsai",
volume = "16",
ISBN = "981-256-094-7",
publisher = "World Scientific Publishing Co.",
pages = "95--105",
month = may,
year = "2005",
abstract = "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.",
abstract-url = "http://www.cs.bris.ac.uk/Publications/pub_info.jsp?id=2000240",
pubtype = "7",
keywords = "genetic algorithms, genetic programming, Artificial
Intelligence, Machine Learning, SBSE",
notes = "This paper is not on-line. Contact the author",
}
@TechReport{burgess:1999:faasdeGP,
author = "Glenn Burgess",
title = "Finding Approximate Analytic Solutions To Differential
Equations Using Genetic Programming",
institution = "Surveillance Systems Division, Defence Science and
Technology Organisation, Australia",
month = Feb,
year = "1999",
type = "Technical Report",
number = "DSTO-TR-0838",
address = "Salisbury, SA, 5108, Austrlia",
notes = "Based on author's 1997 Dept. Phys. Honours Thesis,
Flinders University of South Australia",
keywords = "genetic algorithms, genetic programming, differential
equations",
URL = "http://203.36.224.190/cgi-bin/dsto/extract.pl?DSTO-TR-0838",
URL = "http://www.dsto.defence.gov.au/corporate/reports/DSTO-TR-0838.pdf",
abstract = "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.",
size = "73 pages",
}
@InProceedings{Burgin:2010:cec,
author = "Mark Burgin and Eugene Eberbach",
title = "Bounded and periodic evolutionary machines",
booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-6910-9",
abstract = "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.",
doi = "doi:10.1109/CEC.2010.5586271",
notes = "WCCI 2010. Also known as \cite{5586271}",
}
@InCollection{burjorjee:1999:GAGGS,
author = "Keki M. Burjorjee",
title = "Genetic Algorithms Go to Grade School",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1999",
year = "1999",
editor = "John R. Koza",
pages = "31--40",
address = "Stanford, California, 94305-3079 USA",
month = "15 " # mar,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "part of \cite{koza:1999:GAGPs}",
}
@Article{burk:1998:pmgGA,
author = "Donald S. Burke and Kenneth A. {De Jong} and John J.
Grefenstette and Connie Loggia Ramsey and Annie S. Wu",
title = "Putting More Genetics into Genetic Algorithms",
journal = "Evolutionary Computation",
year = "1998",
volume = "6",
number = "4",
pages = "387--410",
month = "Winter",
keywords = "genetic algorithms, Models of viral evolution,
variable-length representation, length penalty
functions, genome length adaptation, noncoding regions,
duplicative genes",
URL = "http://www.mitpressjournals.org/doi/pdfplus/10.1162/evco.1998.6.4.387",
doi = "doi:10.1162/evco.1998.6.4.387",
size = "25 pages",
abstract = "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.",
notes = "Evolutionary Computation (Journal)
Special Issue: Variable-Length Representation and
Noncoding Segments for Evolutionary Algorithms Edited
by Annie S. Wu and Wolfgang Banzhaf",
}
@Misc{burk:1998:pmgGAx,
author = "Donald S. Burke and Kenneth A. {De Jong} and John J.
Grefenstette and Connie Loggia Ramsey and Annie S. Wu",
title = "Putting More Genetics into Genetic Algorithms",
howpublished = "preprint of \cite{burk:1998:pmgGA}",
year = "1998",
month = "19 " # oct,
keywords = "genetic algorithms, Models of viral evolution,
variable-length representation, length penalty
functions, genome length adaptation, noncoding regions,
duplicative genes",
URL = "http://www.ib3.gmu.edu/gref/papers/burke-ec98.ps",
}
@InProceedings{burke:2002:gecco,
author = "Edmund Burke and Steven Gustafson and Graham Kendall",
title = "A Survey And Analysis Of Diversity Measures In Genetic
Programming",
booktitle = "GECCO 2002: Proceedings of the Genetic and
Evolutionary Computation Conference",
editor = "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",
year = "2002",
pages = "716--723",
address = "New York",
publisher_address = "San Francisco, CA 94104, USA",
month = "9-13 " # jul,
publisher = "Morgan Kaufmann Publishers",
keywords = "genetic algorithms, genetic programming, diversity,
population diversity, population dynamics",
ISBN = "1-55860-878-8",
URL = "http://www.cs.nott.ac.uk/~smg/research/publications/gecco-diversity-2002.ps",
URL = "http://www.cs.nott.ac.uk/~smg/research/publications/gecco-diversity-2002.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/GP125.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/GP125.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf",
notes = "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",
}
@InProceedings{burke:ppsn2002:pp341,
author = "Edmund Burke and Steven Gustafson and Graham Kendall
and Natalio Krasnogor",
title = "Advanced Population Diversity Measures in Genetic
Programming",
booktitle = "Parallel Problem Solving from Nature - PPSN VII",
address = "Granada, Spain",
month = "7-11 " # sep,
pages = "341--350",
year = "2002",
editor = "Juan J. Merelo-Guervos and Panagiotis Adamidis and
Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and
Hans-Paul Schwefel",
number = "2439",
series = "Lecture Notes in Computer Science, LNCS",
publisher = "Springer-Verlag",
URL = "http://www.gustafsonresearch.com/research/publications/ppsn-2002.pdf",
URL = "http://www.cs.nott.ac.uk/~smg/research/publications/ppsn-2002.ps",
URL = "http://www.cs.nott.ac.uk/~smg/research/publications/ppsn-2002.pdf",
URL = "http://slater.chem.nott.ac.uk/~natk/Public/PAPERS/gp-ppsn2002.ps.Z",
URL = "http://citeseer.ist.psu.edu/529057.html",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2439&spage=341",
keywords = "genetic algorithms, genetic programming, Theory of EC,
Evolution dynamics",
ISBN = "3-540-44139-5",
annote = "Available from
http://link.springer.de/link/service/series/0558/papers/2439/243900341.pdf",
abstract = "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.",
}
@InProceedings{burke:2003:gecco,
author = "Edmund Burke and Steven Gustafson and Graham Kendall",
title = "Ramped Half-n-Half Initialisation Bias in {GP}",
booktitle = "Genetic and Evolutionary Computation -- GECCO-2003",
editor = "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",
year = "2003",
pages = "1800--1801",
address = "Chicago",
publisher_address = "Berlin",
month = "12-16 " # jul,
volume = "2724",
series = "LNCS",
ISBN = "3-540-40603-4",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, poster",
URL = "http://www.cs.nott.ac.uk/~smg/research/publications/gecco-poster-2003.ps",
URL = "http://www.cs.nott.ac.uk/~smg/research/publications/gecco-poster-2003.pdf",
abstract = "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.",
notes = "GECCO-2003. A joint meeting of the twelfth
International Conference on Genetic Algorithms
(ICGA-2003) and the eighth Annual Genetic Programming
Conference (GP-2003)",
}
@InProceedings{1277273,
author = "Edmund K. Burke and Matthew R. Hyde and Graham Kendall
and John Woodward",
title = "Automatic heuristic generation with genetic
programming: evolving a jack-of-all-trades or a master
of one",
booktitle = "GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation",
year = "2007",
editor = "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",
volume = "2",
isbn13 = "978-1-59593-697-4",
pages = "1559--1565",
address = "London",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1559.pdf",
doi = "doi:10.1145/1276958.1277273",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
month = "7-11 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, bin packing,
heuristics, hyper heuristic, reliability",
abstract = "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.",
notes = "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",
}
@InProceedings{Burke:2007:cec,
author = "E. K. Burke and M. R. Hyde and G. Kendall and J. R.
Woodward",
title = "The Scalability of Evolved on Line Bin Packing
Heuristics",
booktitle = "2007 IEEE Congress on Evolutionary Computation",
year = "2007",
editor = "Dipti Srinivasan and Lipo Wang",
pages = "2530--2537",
address = "Singapore",
month = "25-28 " # sep,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "1-4244-1340-0",
file = "1668.pdf",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and
the IET.
IEEE Catalog Number: 07TH8963C",
}
@InProceedings{Burke:2005:WWERC,
author = "John J. Burke",
title = "Genetic Programming of Crops to Sustain or Increase
Yields under Reduced Irrigation",
booktitle = "World Water and Environmental Resources Congress
2005",
year = "2005",
editor = "Raymond Walton",
address = "Anchorage, Alaska, USA",
month = may # " 15-19",
doi = "doi:10.1061/40792(173)532",
abstract = "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.",
notes = "Perhaps not about GP? c2005 ASCE",
}
@InProceedings{Burke:PPSN:2006,
author = "E. K. Burke and M. R. Hyde and G. Kendall",
title = "Evolving Bin Packing Heuristics with Genetic
Programming",
booktitle = "Parallel Problem Solving from Nature - PPSN IX",
year = "2006",
editor = "Thomas Philip Runarsson and Hans-Georg Beyer and
Edmund Burke and Juan J. Merelo-Guervos and L. Darrell
Whitley and Xin Yao",
volume = "4193",
pages = "860--869",
series = "LNCS",
address = "Reykjavik, Iceland",
publisher_address = "Berlin",
month = "9-13 " # sep,
publisher = "Springer-Verlag",
ISBN = "3-540-38990-3",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.nott.ac.uk/~mvh/ppsn2006.pdf",
doi = "doi:10.1007/11844297_87",
size = "10 pages",
abstract = "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.",
notes = "PPSN-IX",
}
@InCollection{Burke:2010:HBMH,
author = "Edmund K. Burke and Matthew Hyde and Graham Kendall
and Gabriela Ochoa and Ender Ozcan and John R.
Woodward",
title = "A Classification of Hyper-heuristics Approaches",
booktitle = "Handbook of Metaheuristics",
publisher = "Springer",
year = "2010",
editor = "Michel Gendreau and Jean-Yves Potvin",
volume = "57",
series = "International Series in Operations Research \&
Management Science",
chapter = "15",
pages = "449--468",
edition = "2nd",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-4419-1663-1",
URL = "http://www.cs.nott.ac.uk/~gxo/papers/ChapterClassHH.pdf",
doi = "doi:10.1007/978-1-4419-1665-5_15",
abstract = "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.",
}
@Article{Burke:2011:ieeeTEC,
author = "Edmund K. Burke and Matthew R. Hyde and Graham
Kendall",
title = "Grammatical Evolution of Local Search Heuristics",
journal = "IEEE Transactions on Evolutionary Computation",
note = "Accepted for future publication",
keywords = "genetic algorithms, genetic programming, Grammatical
Evolution, Grammar, Heuristic algorithms, Production,
Search problems, Bin packing, heuristics, local search,
stock cutting",
ISSN = "1089-778X",
doi = "doi:10.1109/TEVC.2011.2160401",
size = "12 pages",
abstract = "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.",
notes = "iGE also known as \cite{6029980}",
}
@InCollection{Burkowski:2005:HBBAA,
author = "Forbes Burkowski",
title = "Optimization via Gene Expression Algorithms",
booktitle = "Handbook of Bioinspired Algorithms and Applications",
publisher = "Chapman and Hall/CRC",
year = "2005",
editor = "Stephan Olariu and Albert Y. Zomaya",
series = "Computer \& Information Science Series",
chapter = "8",
pages = "Pages 8--121--8--134",
keywords = "SVM",
isbn13 = "978-1-58488-475-0",
doi = "doi:10.1201/9781420035063.ch8",
notes = "Not on GP?",
}
@InProceedings{busch:2002:EuroGP,
title = "Automatic Generation of Control Programs for Walking
Robots Using Genetic Programming",
author = "Jens Busch and Jens Ziegler and Wolfgang Banzhaf and
Andree Ross and Daniel Sawitzki and Christian Aue",
editor = "James A. Foster and Evelyne Lutton and Julian Miller
and Conor Ryan and Andrea G. B. Tettamanzi",
booktitle = "Genetic Programming, Proceedings of the 5th European
Conference, EuroGP 2002",
volume = "2278",
series = "LNCS",
pages = "258--267",
publisher = "Springer-Verlag",
address = "Kinsale, Ireland",
publisher_address = "Berlin",
month = "3-5 " # apr,
year = "2002",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-43378-3",
URL = "http://ls2-www.cs.uni-dortmund.de/~sawitzki/AGoCPfWRUGP_Proc.pdf",
URL = "http://link.springer-ny.com/link/service/series/0558/bibs/2278/22780258.htm",
URL = "http://link.springer-ny.com/link/service/series/0558/papers/2278/22780258.pdf",
abstract = "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.",
notes = "EuroGP'2002, part of \cite{lutton:2002:GP}",
}
@Misc{oai:CiteSeerPSU:572931,
author = "Stephen F. Bush and Amit B. Kulkarni",
title = "Genetically Induced Communication Network Fault
Tolerance",
howpublished = "Invited Paper: SFI Workshop: Resilient and Adaptive
Defence of Computing Networks 2002",
year = "2002",
keywords = "genetic algorithms, genetic programming",
citeseer-isreferencedby = "oai:CiteSeerPSU:98543",
citeseer-references = "oai:CiteSeerPSU:506477; oai:CiteSeerPSU:155080;
oai:CiteSeerPSU:454895; oai:CiteSeerPSU:439074;
oai:CiteSeerPSU:11748; oai:CiteSeerPSU:373663;
oai:CiteSeerPSU:185401; oai:CiteSeerPSU:163938;
oai:CiteSeerPSU:276739; oai:CiteSeerPSU:470039;
oai:CiteSeerPSU:506805; oai:CiteSeerPSU:461659;
oai:CiteSeerPSU:462512",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:572931",
rights = "unrestricted",
URL = "http://www.crd.ge.com/~bushsf/ftn/GE-SFI-AdaptiveSecurity.pdf",
URL = "http://citeseer.ist.psu.edu/572931.html",
size = "9 pages",
abstract = "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.",
notes = "No confirmation",
}
@Article{1005412,
author = "Stephen F. Bush",
title = "Genetically induced communication network fault
tolerance",
journal = "Complexity",
volume = "9",
number = "2",
year = "2003",
ISSN = "1076-2787",
pages = "19--33",
doi = "doi:10.1002/cplx.20002",
publisher = "John Wiley \& Sons, Inc.",
keywords = "genetic algorithms, genetic programming, active
networks, algorithmic information theory, Kolmogorov
complexity, complexity theory, self-healing networks",
URL = "http://www.crd.ge.com/~bushsf/pdfpapers/ComplexityJournal.pdf",
abstract = "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.",
}
@InProceedings{bush:evows05,
author = "William S. Bush and Alison A. Motsinger and Scott M.
Dudek and Marylyn D. Ritchie",
title = "Can neural network constraints in {GP} provide power
to detect genes associated with human disease?",
booktitle = "Applications of Evolutionary Computing,
EvoWorkshops2005: {EvoBIO}, {EvoCOMNET}, {EvoHOT},
{EvoIASP}, {EvoMUSART}, {EvoSTOC}",
year = "2005",
month = "30 " # mar # "-1 " # apr,
editor = "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",
series = "LNCS",
volume = "3449",
publisher = "Springer Verlag",
address = "Lausanne, Switzerland",
publisher_address = "Berlin",
pages = "44--53",
keywords = "genetic algorithms, genetic programming, evolutionary
computation, ANN",
ISBN = "3-540-25396-3",
ISSN = "0302-9743",
doi = "doi:10.1007/b106856",
abstract = "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.",
notes = "EvoWorkshops2005",
}
@TechReport{butler:1995:eddie,
author = "James M. Butler and Edward P. K. Tsang",
title = "{EDDIE} Beats the Bookies",
institution = "Computer Science, University of Essex",
year = "1995",
type = "Technical Report",
number = "CSM-259",
address = "Colchester CO4 3SQ, UK",
month = "15 " # dec,
keywords = "genetic algorithms, genetic programming",
URL = "http://cswww.essex.ac.uk/technical-reports/index.htm",
URL = "http://citeseer.ist.psu.edu/tsang98eddie.html",
abstract = "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...",
notes = "
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
\cite{tsang:1998:eddie}",
}
@InProceedings{conf/ai/ButlerK09a,
title = "Optimizing a Pseudo Financial Factor Model with
Support Vector Machines and Genetic Programming",
author = "Matthew Butler and Vlado Keselj",
booktitle = "22nd Canadian Conference on Artificial Intelligence,
Canadian AI 2009",
year = "2009",
editor = "Yong Gao and Nathalie Japkowicz",
volume = "5549",
series = "Lecture Notes in Computer Science",
pages = "191--194",
address = "Kelowna, Canada",
month = may # " 25-27",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, support
vector machines, financial forecasting, principle
component analysis",
isbn13 = "978-3-642-01817-6",
doi = "doi:10.1007/978-3-642-01818-3_21",
bibdate = "2009-05-18",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/ai/ai2009.html#ButlerK09a",
abstract = "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.",
}
@Article{butz:2005:GPEM,
author = "Martin V. Butz and Kumara Sastry and David E.
Goldberg",
title = "Strong, Stable, and Reliable Fitness Pressure in {XCS}
due to Tournament Selection",
journal = "Genetic Programming and Evolvable Machines",
year = "2005",
volume = "6",
number = "1",
pages = "53--77",
month = mar,
keywords = "genetic algorithms, classifier systems, LCS, learning
classifier systems, XCS, tournament selection, genetics
based machine learning",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-005-7619-9",
abstract = "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.",
}
@Article{Butz:2006:GPEM,
author = "Martin V. Butz and David E. Goldberg and Pier Luca
Lanzi and Kumara Sastry",
title = "Problem solution sustenance in {XCS}: Markov chain
analysis of niche support distributions and the impact
on computational complexity",
journal = "Genetic Programming and Evolvable Machines",
year = "2007",
volume = "8",
number = "1",
pages = "5--37",
month = mar,
keywords = "genetic algorithms, classifier systems, Learning
classifier systems, LCS, XCS, Niching, Markov chain
analysis, Solution sustenance, Mutation",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-006-9012-8",
size = "33 pages",
abstract = "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.",
}
@Article{buxton:2001:MC,
author = "B. F. Buxton and W. B. Langdon and S. J. Barrett",
title = "Data Fusion by Intelligent Classifier Combination",
journal = "Measurement and Control",
year = "2001",
editor = "Qing-Ping Yang",
volume = "34",
number = "8",
pages = "229--234",
month = oct,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/mc/",
abstract = "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.",
notes = "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.",
}
@Misc{buxton:2002:rocket,
author = "B. F. Buxton and S B Holden and P C Treleaven",
title = "Intelligent Data Analysis and Fusion Techniques in
Pharmaceuticals, Bioprocessing and Process Control",
year = "2002",
month = oct,
keywords = "genetic algorithms, genetic programming, boosting,
support vector machines",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/rocket/EPSRC-final-report.htm",
notes = "End of project report. INTErSECT Faraday Partnership
Flagship Project, 4 January 1999- 3 July 2002 Grant
Reference GR/M43975",
}
@InProceedings{Buzdalov:2011:GECCOcomp,
author = "Maxim Buzdalov",
title = "Generation of tests for programming challenge tasks
using evolution algorithms",
booktitle = "GECCO 2011 Graduate students workshop",
year = "2011",
editor = "Miguel Nicolau",
isbn13 = "978-1-4503-0690-4",
keywords = "genetic algorithms, genetic programming, SBSE",
pages = "763--766",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001858.2002086",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{2002086} Distributed on CD-ROM at
GECCO-2011.
ACM Order Number 910112.",
}
@InProceedings{Byers:2011:GECCO,
author = "Chad M. Byers and Betty H. C. Cheng and Philip K.
McKinley",
title = "Digital enzymes: agents of reaction inside robotic
controllers for the foraging problem",
booktitle = "GECCO '11: Proceedings of the 13th annual conference
on Genetic and evolutionary computation",
year = "2011",
editor = "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",
isbn13 = "978-1-4503-0557-0",
pages = "243--250",
keywords = "genetic algorithms, genetic programming, Artificial
life/robotics/evolvable hardware",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001576.2001610",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{2001610} GECCO-2011 A joint
meeting of the twentieth international conference on
genetic algorithms (ICGA-2011) and the sixteenth annual
genetic programming conference (GP-2011)",
}
@InProceedings{4480232,
author = "M. D. Byington and B. E. Bishop",
title = "Cooperative Robot Swarm Locomotion Using Genetic
Algorithms",
booktitle = "System Theory, 2008. SSST 2008. 40th Southeastern
Symposium on",
year = "2008",
month = mar,
pages = "252--256",
keywords = "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",
doi = "doi:10.1109/SSST.2008.4480232",
ISSN = "0094-2898",
notes = "Not GP, real coded GA applied to ANN",
}
@InProceedings{Byrne:2009:cec,
author = "Jonathan Byrne and Michael O'Neill and Erik Hemberg
and Anthony Brabazon",
title = "Analysis of Constant Creation Techniques on the
Binomial-3 Problem with Grammatical Evolution",
booktitle = "2009 IEEE Congress on Evolutionary Computation",
year = "2009",
editor = "Andy Tyrrell",
pages = "568--573",
address = "Trondheim, Norway",
month = "18-21 " # may,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-2959-2",
file = "P522.pdf",
doi = "doi:10.1109/CEC.2009.4982996",
abstract = "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.",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and
the IET. IEEE Catalog Number: CFP09ICE-CDR",
}
@InProceedings{DBLP:conf/gecco/ByrneOB09,
author = "Jonathan Byrne and Michael O'Neill and Anthony
Brabazon",
title = "Structural and nodal mutation in grammatical
evolution",
booktitle = "GECCO '09: Proceedings of the 11th Annual conference
on Genetic and evolutionary computation",
year = "2009",
editor = "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",
pages = "1881--1882",
address = "Montreal",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "8-12 " # jul,
organisation = "SigEvo",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, Poster",
isbn13 = "978-1-60558-325-9",
bibsource = "DBLP, http://dblp.uni-trier.de",
doi = "doi:10.1145/1569901.1570215",
abstract = "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.",
notes = "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.",
}
@InProceedings{Byrne:2010:EuroGP,
author = "Jonathan Byrne and James McDermott and Michael O'Neill
and Anthony Brabazon",
title = "An Analysis of the Behaviour of Mutation in
Grammatical Evolution",
booktitle = "Proceedings of the 13th European Conference on Genetic
Programming, EuroGP 2010",
year = "2010",
editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara
Silva and Stephen Dignum and A. Sima Uyar",
volume = "6021",
series = "LNCS",
pages = "14--25",
address = "Istanbul",
month = "7-9 " # apr,
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-12147-0",
doi = "doi:10.1007/978-3-642-12148-7_2",
abstract = "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.",
notes = "Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010
held in conjunction with EvoCOP2010 EvoBIO2010 and
EvoApplications2010",
}
@InProceedings{byrne_oneill_brabazon:mendel2010,
author = "J. Byrne and M. O'Neill and A. Brabazon",
title = "Optimising Offensive Moves in Toribash",
booktitle = "Proceedings of Mendel 2010 16th International
Conference on Soft Computing",
editor = "R. Matousek",
pages = "78--85",
year = "2010",
address = "Brno, Czech Republic",
month = "23-25 " # jun,
publisher = "Brno University of Technology",
isbn13 = "978-80-214-4120-0",
notes = "0102 http://www.mendel-conference.org/",
}
@InProceedings{byrne_etal:cec2010,
author = "Jonathan Byrne and James McDermott and Edgar
Galvan-Lopez and Michael O'Neill",
title = "Implementing an Intuitive Mutation Operator for
Interactive Evolutionary 3{D} Design",
booktitle = "2010 IEEE World Congress on Computational
Intelligence",
pages = "2919--2925",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
isbn13 = "978-1-4244-6910-9",
doi = "doi:10.1109/CEC.2010.5586485",
abstract = "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.",
notes = "CEC 2010. WCCI 2010. Also known as \cite{5586485}",
}
@InProceedings{byrne:evoapps11,
author = "Jonathan Byrne and Michael Fenton and Erik Hemberg and
James McDermott and Michael O'Neill and Elizabeth
Shotton and Ciaran Nally",
title = "Combining Structural Analysis and Multi-Objective
Criteria for Evolutionary Architectural Design",
booktitle = "Applications of Evolutionary Computing,
EvoApplications 2011: {EvoCOMNET}, {EvoFIN}, {EvoHOT},
{EvoMUSART}, {EvoSTIM}, {EvoTRANSLOG}",
year = "2011",
month = "27-29 " # apr,
editor = "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",
series = "LNCS",
volume = "6625",
publisher = "Springer Verlag",
address = "Turin, Italy",
publisher_address = "Berlin",
pages = "200--209",
organisation = "EvoStar",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
notes = "Part of \cite{DiChio:2011:evo_b} EvoApplications2011
held inconjunction with EuroGP'2011, EvoCOP2011 and
EvoBIO2011",
}
@InProceedings{Byrne:2011:GECCOcomp,
author = "Jonathan Byrne and Erik Hemberg and Michael O'Neill",
title = "Interactive operators for evolutionary architectural
design",
booktitle = "GECCO '11: Proceedings of the 13th annual conference
companion on Genetic and evolutionary computation",
year = "2011",
editor = "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",
isbn13 = "978-1-4503-0690-4",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, Digital entertainment technologies and arts:
Poster",
pages = "43--44",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001858.2001884",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{2001884} Distributed on CD-ROM at
GECCO-2011.
ACM Order Number 910112.",
}
@Article{Cabalar:2010:NCA,
title = "Constitutive modeling of Leighton Buzzard Sands using
genetic programming",
author = "Ali Firat Cabalar and Abdulkadir Cevik and Ibrahim H.
Guzelbey",
journal = "Neural Computing and Applications",
year = "2010",
number = "5",
volume = "19",
pages = "657--665",
keywords = "genetic algorithms, genetic programming",
publisher = "Springer London",
ISSN = "0941-0643",
doi = "doi:10.1007/s00521-009-0317-4",
size = "9 pages",
abstract = "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.",
affiliation = "University of Gaziantep Geotechnical Engineering
Division, Department of Civil Engineering Gaziantep
Turkey",
}
@Article{Cabalar20091884,
author = "Ali Firat Cabalar and Abdulkadir Cevik",
title = "Genetic programming-based attenuation relationship: An
application of recent earthquakes in Turkey",
journal = "Computers \& Geosciences",
volume = "35",
number = "9",
pages = "1884--1896",
year = "2009",
ISSN = "0098-3004",
doi = "doi:10.1016/j.cageo.2008.10.015",
URL = "http://www.sciencedirect.com/science/article/B6V7D-4W99W08-1/2/aa19b6639659945b1d4e78c6209fe435",
keywords = "genetic algorithms, genetic programming, Attenuation
relationship",
abstract = "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.",
}
@Article{Cabalar201110358,
author = "Ali Firat Cabalar and Abdulkadir Cevik",
title = "Triaxial behavior of sand-mica mixtures using genetic
programming",
journal = "Expert Systems with Applications",
volume = "38",
number = "8",
pages = "10358--10367",
year = "2011",
ISSN = "0957-4174",
doi = "doi:10.1016/j.eswa.2011.02.051",
URL = "http://www.sciencedirect.com/science/article/B6V03-524FSB9-M/2/eb83d6182c4d3c0b1271b301c5a04e15",
keywords = "genetic algorithms, genetic programming, Leighton
Buzzard Sand, Mica, Triaxial testing, Modelling",
abstract = "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.",
}
@InProceedings{CabritaBotzheimRuanoKoczy04,
author = "C. Cabrita and J. Botzheim and A. E. Ruano and L. T.
Koczy",
title = "Design of {B}-spline Neural Networks using a Bacterial
Programming Approach",
booktitle = "Proceedings of the International Joint Conference on
Neural Networks, IJCNN 2004",
address = "Budapest, Hungary",
pages = "2313--2318",
year = "2004",
month = jul,
keywords = "genetic algorithms, genetic programming",
ISSN = "1098-7576",
size = "6 pages",
abstract = "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.",
notes = "This is the first paper on Bacterial Programming.",
}
@InProceedings{Cadar:2010:FoSER,
author = "Cristian Cadar and Peter Pietzuch and Alexander L.
Wolf",
title = "Multiplicity computing: a vision of software
engineering for next-generation computing platform
applications",
booktitle = "Proceedings of the FSE/SDP workshop on Future of
software engineering research",
year = "2010",
editor = "Kevin Sullivan",
series = "FoSER '10",
pages = "81--86",
address = "Santa Fe, New Mexico, USA",
publisher_address = "New York, NY, USA",
month = "7-11 " # nov,
organisation = "ACM sigsoft",
publisher = "ACM",
keywords = "cloud computing, data centers, multicore,
virtualization, Design, Experimentation, Measurement,
Performance, Reliability, Security",
isbn13 = "978-1-4503-0427-6",
URL = "http://www.doc.ic.ac.uk/~cristic/papers/multicomp-foser-10.pdf",
doi = "doi:10.1145/1882362.1882380",
acmid = "1882380",
size = "5 pages",
abstract = "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.",
notes = "Not on GP but does refer to GP work
\cite{DBLP:conf/gecco/ForrestNWG09}. Also known as
\cite{Cadar:2010:MCV:1882362.1882380}",
}
@InProceedings{cagnoni:2004:pre:preproc,
author = "S. Cagnoni",
title = "{GECCO2004} Workshop Proceedings: Preface",
editor = "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",
booktitle = "GECCO 2004 Workshop Proceedings",
year = "2004",
month = "26-30 " # jun,
address = "Seattle, Washington, USA",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/",
notes = "GECCO-2004WKS Distributed on CD-ROM at GECCO-2004",
}
@Article{cagnoni:2005:SMC,
author = "Stefano Cagnoni and Federico Bergenti and Monica
Mordonini and Giovanni Adorni",
title = "Evolving Binary Classifiers Through Parallel
Computation of Multiple Fitness Cases",
journal = "IEEE Transactions on Systems, Man and Cybernetics -
Part B",
year = "2005",
volume = "35",
number = "3",
pages = "548--555",
month = jun,
email = "cagnoni@ce.unipr.it",
keywords = "genetic algorithms, genetic programming, cellular
programming, sub-machine code genetic programming,
multiple classifiers, pattern recognition",
ISSN = "1083-4419",
doi = "doi:10.1109/TSMCB.2005.846671",
size = "8 pages",
abstract = "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.",
notes = "PMID: 15971922 [PubMed - indexed for MEDLINE]",
}
@Article{Cagnoni:2006:IA,
author = "S. Cagnoni and R. Poli",
title = "Genetic and evolutionary Computation",
journal = "Intelligenza Artificiale",
year = "2006",
volume = "3",
number = "1/2",
pages = "94--101",
month = "Marzo-Giugno",
keywords = "genetic algorithms, genetic programming, gec, gas, es,
gsice, italian GEC, human-competitive",
ISSN = "1724-8035",
size = "8 pages",
notes = "In English. Tutorial. http://ia.di.uniba.it/ Periodico
trimestrale dell'Associazione Italiana per
l'Intelligenza Artificiale
by 2012 daily invention machine",
}
@Article{Cagnoni:2008:EC,
author = "S. Cagnoni and E. Lutton and G. Olague",
title = "Editorial Introduction to the Special Issue on
Evolutionary Computer Vision",
journal = "Evolutionary Computation",
year = "2008",
volume = "16",
number = "4",
pages = "437--438",
month = "Winter",
keywords = "genetic algorithms, genetic programming",
ISSN = "1063-6560",
doi = "doi:10.1162/evco.2008.16.4.437",
size = "2 pages",
}
@InProceedings{Cai:2005:HT,
author = "Weihua Cai and Mihir Sen and K. T. Yang and Arturo
Pacheco-Vega",
title = "Genetic-Programming-Based Symbolic Regression for Heat
Transfer Correlations of a Compact Heat Exchanger",
booktitle = "ASME Summer Heat Transfer Conference (HT2005)",
year = "2005",
volume = "4",
pages = "367--374",
address = "San Francisco, California, USA",
month = jul # " 17-22",
publisher = "ASME",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7918-4734-9",
doi = "doi:10.1115/HT2005-72293",
abstract = "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.",
notes = "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",
}
@Article{Cai:2006:IJHMT,
author = "Weihua Cai and Arturo Pacheco-Vega and Mihir Sen and
K. T. Yang",
title = "Heat transfer correlations by symbolic regression",
journal = "International Journal of Heat and Mass Transfer",
year = "2006",
volume = "49",
number = "23-24",
pages = "4352--4359",
month = nov,
keywords = "genetic algorithms, genetic programming, Heat
transfer, Correlations, Symbolic regression, Heat
exchanger",
doi = "doi:10.1016/j.ijheatmasstransfer.2006.04.029",
abstract = "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.",
}
@InProceedings{conf/ices/CaiST05,
author = "Xinye Cai and Stephen L. Smith and Andrew M. Tyrrell",
title = "Benefits of Employing an Implicit Context
Representation on Hardware Geometry of {CGP}",
year = "2005",
pages = "143--154",
editor = "Juan Manuel Moreno and Jordi Madrenas and Jordi Cosp",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3637",
booktitle = "Evolvable Systems: From Biology to Hardware, 6th
International Conference, ICES 2005, Proceedings",
address = "Sitges, Spain",
month = sep # " 12-14",
bibsource = "DBLP, http://dblp.uni-trier.de",
keywords = "genetic algorithms, genetic programming, Cartesian
Genetic Programming",
ISBN = "3-540-28736-1",
doi = "doi:10.1007/11549703_14",
size = "12 pages",
abstract = "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.",
}
@InProceedings{eurogp06:CaiSmothTyrrell,
author = "Xinye Cai and Stephen L. Smith and Andy M. Tyrrell",
title = "Positional Independence and Recombination in Cartesian
Genetic Programming",
editor = "Pierre Collet and Marco Tomassini and Marc Ebner and
Steven Gustafson and Anik\'o Ek\'art",
booktitle = "Proceedings of the 9th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3905",
year = "2006",
address = "Budapest, Hungary",
month = "10 - 12 " # apr,
organisation = "EvoNet",
keywords = "genetic algorithms, genetic programming, Cartesian
Genetic Programming",
ISBN = "3-540-33143-3",
pages = "351--360",
URL = "http://link.springer.de/link/service/series/0558/papers/3905/39050351.pdf",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in
conjunction with EvoCOP2006 and EvoWorkshops2006",
}
@InProceedings{1277300,
author = "Xinye Cai and Stephen M. Welch and Praveen Koduru and
Sanjoy Das",
title = "Discovering structures in gene regulatory networks
using genetic programming and particle swarms",
booktitle = "GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation",
year = "2007",
editor = "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",
volume = "2",
isbn13 = "978-1-59593-697-4",
pages = "1750--1750",
address = "London",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1750.pdf",
doi = "doi:10.1145/1276958.1277300",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
month = "7-11 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming: Poster,
bioinformatics, gene regulatory network, Particle Swarm
Optimisation",
size = "1 page",
abstract = "GP + PSO for gene network discovery",
notes = "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",
}
@Article{Cai:2009:IJBRA,
title = "Simultaneous structure discovery and parameter
estimation in gene networks using a multi-objective
{GP}-{PSO} hybrid approach",
author = "Xinye Cai and Praveen Koduru and Sanjoy Das and
Stephen M. Welch",
journal = "International Journal of Bioinformatics Research and
Applications",
year = "2009",
month = "11 " # jun,
volume = "5",
number = "3",
pages = "254--268",
keywords = "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",
ISSN = "1744-5493",
URL = "http://www.inderscience.com/link.php?id=26418",
doi = "doi:10.1504/IJBRA.2009.026418",
publisher = "Inderscience Publishers",
abstract = "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.",
}
@Article{Cai:1996:ASS,
author = "Yu-Dong Cai",
title = "Genetic programming for prediction of earthquake
sequence type",
journal = "Acta Seismologica Sinica",
publisher = "Seismological Society of China",
volume = "9",
issue = "1",
year = "1996",
pages = "53--57",
month = feb,
keywords = "genetic algorithms, genetic programming, earthquake
sequence, prediction",
ISSN = "1000-9116",
doi = "doi:10.1007/BF02650623",
abstract = "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.",
notes = "Journal now called Earthquake Science (2009-2011)",
affiliation = "Chinese Academy of Sciences Shanghai Institute of
Metallurgy 200050 Shanghai China",
}
@InProceedings{calderoni:1998:GPadsar,
author = "Stephane Calderoni and Pierre Marcenac",
title = "Genetic Programming For Automatic Design Of
Self-Adaptive Robots",
booktitle = "Proceedings of the First European Workshop on Genetic
Programming",
year = "1998",
editor = "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer
and Terence C. Fogarty",
volume = "1391",
series = "LNCS",
pages = "163--177",
address = "Paris",
publisher_address = "Berlin",
month = "14-15 " # apr,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-64360-5",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/13194/http:zSzzSzwww.univ-reunion.frzSz~caldezSzpublicationszSzpaperszSzlncs1391.pdf/calderoni98genetic.pdf",
URL = "http://citeseer.ist.psu.edu/267374.html",
size = "15 pages",
abstract = "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.",
notes = "EuroGP'98",
}
@InProceedings{oai:CiteSeerPSU:185735,
author = "Stephane Calderoni and Pierre Marcenac and Remy
Courdier",
title = "Genetic Encoding of Agent Behavioral Strategy",
booktitle = "Proceedings of the 3rd International Conference on
Multi Agent Systems",
year = "1998",
pages = "403",
publisher = "IEEE Computer Society",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-8186-8500-X",
URL = "http://portal.acm.org/citation.cfm?id=852213&jmp=cit&dl=portal&dl=ACM",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/4918/http:zSzzSzwww.univ-reunion.frzSz~caldezSzrechzSzpublicationszSzpaperszSzicmas98a.pdf/genetic-encoding-of-agent.pdf",
URL = "http://citeseer.ist.psu.edu/185735.html",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:185735",
rights = "unrestricted",
size = "2 pages",
abstract = "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.",
}
@InProceedings{calderoni:1999:BCSMD,
author = "Stephane Calderoni",
title = "Behavior-Based Control System in MultiAgent Domain",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1439",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, artificial
life, adaptive behavior and agents, poster papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-048.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-048.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{oai:CiteSeerPSU:247844,
author = "Stephane Calderoni",
title = "Generic Control Ssystem in MultiAgent Domain",
booktitle = "World Multiconference on Systemics, Cybernetics and
Informatics SCI-99",
year = "1999",
volume = "7",
keywords = "genetic algorithms, genetic programming, Multiagent
Systems, Control Systems, Reinforcement Learning",
URL = "http://citeseer.ist.psu.edu/247844.html",
citeseer-isreferencedby = "oai:CiteSeerPSU:26950",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:247844",
rights = "unrestricted",
abstract = "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.",
}
@InProceedings{Camargo-Bareno:2011:GECCOcomp,
author = "Carlos Ivan {Camargo Bareno} and Cesar Augusto
{Pedraza Bonilla} and Luis Fernado Nino and Jose
Ignacio {Martinez Torre}",
title = "Intrinsic evolvable hardware for combinatorial
synthesis based on So{C}+{FPGA} and {GPU} platforms",
booktitle = "GECCO '11: Proceedings of the 13th annual conference
companion on Genetic and evolutionary computation",
year = "2011",
editor = "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",
isbn13 = "978-1-4503-0690-4",
keywords = "genetic algorithms, genetic programming, GPU: Poster",
pages = "189--190",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001858.2001964",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{2001964} Distributed on CD-ROM at
GECCO-2011.
ACM Order Number 910112.",
}
@InCollection{campbell:2000:EGPDROR,
author = "Elliott Campbell",
title = "Evaluation of Genetic Programming for Determining
Reservoir Operating Rules",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2000",
year = "2000",
editor = "John R. Koza",
pages = "54--59",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "part of \cite{koza:2000:gagp}",
}
@Article{campbell:1993:MM,
author = "Paul J. Campbell",
copyright = "Copyright 1993 Mathematical Association of America",
ISSN = "0025570x",
journal = "Mathematics Magazine",
number = "2",
owner = "wlangdon",
pages = "136--137",
title = "Reviews",
URL = "http://links.jstor.org/sici?sici=0025-570X%28199304%2966%3A2%3C136%3AR%3E2.0.CO%3B2-4",
volume = "66",
year = "1993",
keywords = "genetic algorithms, genetic programming",
size = "15 lines",
notes = "review of \cite{koza:book}",
}
@InProceedings{Can:2010:WSC,
author = "Birkan Can and Cathal Heavey",
title = "Sequential metamodelling with genetic programming and
particle swarms",
booktitle = "Proceedings of the 2009 Winter Simulation Conference
(WSC)",
year = "2009",
month = "13-16 " # dec,
pages = "3150--3157",
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/WSC.2009.5429276",
notes = "Also known as \cite{5429276}",
}
@PhdThesis{Can:thesis,
author = "Birkan Can",
title = "Evolutionary Modelling of Industrial Systems with
Genetic Programming",
school = "University of Limerick",
year = "2011",
address = "Ireland",
keywords = "genetic algorithms, genetic programming",
notes = "2011 Supervisor Dr. Cathal Heavey.
'thesis is available in the University Library'
",
}
@Article{Can2011,
author = "Birkan Can and Cathal Heavey",
title = "Comparison of experimental designs for
simulation-based symbolic regression of manufacturing
systems",
journal = "Computer \& Industrial Engineering",
note = "In Press, Corrected Proof",
year = "2011",
ISSN = "0360-8352",
doi = "doi:10.1016/j.cie.2011.03.012",
URL = "http://www.sciencedirect.com/science/article/B6V27-52JDFD9-1/2/207e7db7ff221a11f1a808666cba277d",
keywords = "genetic algorithms, genetic programming,
Meta-modelling, Design of experiments, Discrete-event
simulation, Decision support",
abstract = "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.",
}
@Article{journals/nca/CanakciBG09,
title = "Prediction of compressive and tensile strength of
Gaziantep basalts via neural networks and gene
expression programming",
author = "Hanifi Canakci and Adil Baykasoglu and Hamza Gullu",
journal = "Neural Computing and Applications",
year = "2009",
number = "8",
volume = "18",
pages = "1031--1041",
keywords = "genetic algorithms, genetic programming, gene
expression programming, Artificial neural networks,
Basalt, Tensile and compressive strength",
doi = "doi:10.1007/s00521-008-0208-0",
abstract = "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.",
notes = "Department of Civil Engineering, University of
Gaziantep, Gaziantep, Turkey (2) Department of
Industrial Engineering, Faculty of Engineering,
University of Gaziantep, 27310 Gaziantep, Turkey",
bibdate = "2009-12-11",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/nca/nca18.html#CanakciBG09",
}
@InProceedings{cangelosi:1999:HADNN,
author = "Angelo Cangelosi",
title = "Heterochrony and Adaptation in Developing Neural
Networks",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1241--1248",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "artificial life, adaptive behavior and agents",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-008.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-008.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@Article{canham:2003:GPEM,
author = "Richard O. Canham and Andy M. Tyrrell",
title = "A Hardware Artificial Immune System and Embryonic
Array for Fault Tolerant Systems",
journal = "Genetic Programming and Evolvable Machines",
year = "2003",
volume = "4",
number = "4",
pages = "359--382",
month = dec,
keywords = "artificial immune systems, embryonic array, fault
tolerance",
ISSN = "1389-2576",
doi = "doi:10.1023/A:1026143128448",
abstract = "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).",
notes = "Special issue on artificial immune systems Article ID:
5144848",
}
@InProceedings{Cano:2010:HAIS,
author = "Alberto Cano and Amelia Zafra and Sebastian Ventura",
title = "Solving Classification Problems Using Genetic
Programming Algorithms on {GPU}s",
booktitle = "Hybrid Artificial Intelligence Systems",
year = "2010",
series = "Lecture Notes in Computer Science",
editor = "Emilio Corchado and Manuel {Grana Romay} and Alexandre
{Manhaes Savio}",
publisher = "Springer",
pages = "17--26",
volume = "6077",
address = "San Sebastian, Spain",
month = jun # " 23-25",
doi = "doi:10.1007/978-3-642-13803-4_3",
email = "i52caroa@uco.es",
keywords = "genetic algorithms, genetic programming, gpu, gpgpu,
gpgpgpu",
size = "10 pages",
abstract = "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.",
affiliation = "University of Cordoba Department of Computing and
Numerical Analysis 14071 Cordoba Spain",
notes = "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",
}
@Article{Cano:2011:SC,
author = "Alberto Cano and Amelia Zafra and Sebastian Ventura",
title = "Speeding up the evaluation phase of {GP}
classification algorithms on {GPU}s",
journal = "Soft Computing - A Fusion of Foundations,
Methodologies and Applications",
year = "2011",
keywords = "genetic algorithms, genetic programming, GPU, Computer
Science",
publisher = "Springer Berlin / Heidelberg",
ISSN = "1432-7643",
doi = "doi:10.1007/s00500-011-0713-4",
size = "16 pages",
abstract = "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.",
notes = "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.",
affiliation = "Department of Computing and Numerical Analysis,
University of Cordoba, 14071 Cordoba, Spain",
}
@Article{cantner:2001:JASSS,
author = "Uwe Cantner and Bernd Ebersberger and Horst Hanusch
and Jens J. Kruger and Andreas Pyka",
title = "Empirically Based Simulation: The Case of Twin Peaks
in National Income",
journal = "The Journal of Artificial Societies and Social
Simulation",
year = "2001",
month = "30-" # jun,
keywords = "genetic algorithms, genetic programming, bimodal
productivity structure, master equation approach",
URL = "http://jasss.soc.surrey.ac.uk/4/3/9.html",
size = "228 kbytes",
abstract = "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.",
notes = "JASSS",
}
@InProceedings{Cantu-Paz:1997:mibcpGA,
author = "Erick Cantu-Paz and David E. Goldberg",
title = "Modeling Idealized Bounding Cases of Parallel Genetic
Algorithms",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "Genetic Algorithms",
pages = "353--361",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
notes = "GP-97",
}
@InProceedings{cantu:1998:demsPGA,
author = "Erick Cantu-Paz",
title = "Designing Efficient Master-Slave Parallel Genetic
Algorithms",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "455",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms",
notes = "SGA-98",
}
@InProceedings{cantu:1998:mcabcPGA,
author = "Erick Cantu-Paz",
title = "Using Markov Chains to Analyze a Bounding Case of
Parallel Genetic Algorithms",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "456--462",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms",
notes = "SGA-98",
}
@InProceedings{cantu-paz:1999:MPTTGA,
author = "Erick Cantu-Paz",
title = "Migration Policies and Takeover Times in Genetic
Algorithms",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "775",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems, poster
papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco99-migpolicy.pdf",
URL = "http://dangermouse.brynmawr.edu/ec/gecco99-migpolicy.pdf",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{cantu-paz:1999:TMRMPGA,
author = "Erick Cantu-Paz",
title = "Topologies, Migration Rates, and Multi-Population
Parallel Genetic Algorithms",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "91--98",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco99-topologies.pdf",
URL = "http://dangermouse.brynmawr.edu/ec/gecco99-topologies.pdf",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{cantu-paz:1999:M,
author = "Erick Cantu-Paz",
title = "Migration policies, selection pressure, and parallel
evolutionary algorithms",
booktitle = "Late Breaking Papers at the 1999 Genetic and
Evolutionary Computation Conference",
year = "1999",
editor = "Scott Brave and Annie S. Wu",
pages = "65--73",
address = "Orlando, Florida, USA",
month = "13 " # jul,
notes = "GECCO-99LB",
}
@Proceedings{cantu-paz:2002:gecco:lbp,
title = "Late Breaking papers at the Genetic and Evolutionary
Computation Conference ({GECCO-2002})",
editor = "Erick Cant{\'u}-Paz",
year = "2002",
month = jul,
publisher = "AAAI",
address = "New York, NY",
publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
keywords = "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",
notes = "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})",
}
@Proceedings{GECCO2003-PartI,
editor = "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",
title = "Genetic and Evolutionary Computation -- {GECCO 2003},
Part {I}",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "2723",
year = "2003",
ISBN = "3-540-40602-6",
address = "Chicago, IL, USA",
month = "12-16 " # jul,
bibsource = "DBLP, http://dblp.uni-trier.de",
keywords = "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",
notes = "GECCO-2003. A joint meeting of the twelfth
International Conference on Genetic Algorithms
(ICGA-2003) and the eighth Annual Genetic Programming
Conference (GP-2003)",
}
@Proceedings{GECCO2003-PartII,
editor = "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",
title = "Genetic and Evolutionary Computation -- {GECCO 2003},
Part {II}",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "2724",
year = "2003",
ISBN = "3-540-40603-4",
month = "12-16 " # jul,
bibsource = "DBLP, http://dblp.uni-trier.de",
keywords = "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",
notes = "GECCO-2003. A joint meeting of the twelfth
International Conference on Genetic Algorithms
(ICGA-2003) and the eighth Annual Genetic Programming
Conference (GP-2003)",
}
@Article{cao:1998:NPSC,
author = "Hongqing Cao and Lishan Kang and Zbigniew Michalewicz
and Yuping Chen",
title = "A Hybrid Evolutionary Modeling Algorithm for System of
Ordinary Differential Equations",
journal = "Neural, Parallel \& Scientific Computations",
year = "1998",
volume = "6",
number = "2",
pages = "171--188",
month = jun,
address = "Atlanta, USA",
publisher = "Dynamic Publishers",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{cao:1998:2eaode,
author = "Hongqing Cao and Lishan Kang and Zbigniew Michalewicz
and Yuping Chen",
title = "A Two-level Evolutionary Algorithm for Modeling System
of Ordinary Differential Equations",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "17--22",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-548-7",
notes = "GP-98",
}
@Article{cao:1999:CC,
author = "Hongqing Cao and Jingxian Yu and Lishan Kang and
Yuping Chen and Yongyan Chen",
title = "The Kinetic Evolutionary Modeling of Complex Systems
of Chemical Reactions",
journal = "Computers \& Chemistry",
year = "1999",
volume = "23",
number = "2",
pages = "143--152",
month = "30 " # mar,
keywords = "genetic algorithms, genetic programming, kinetic
analysis, Complex systems of chemical reactions,
Evolutionary modeling",
doi = "doi:10.1016/S0097-8485(99)00005-4",
abstract = "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.",
}
@InProceedings{cao:1999:EMODEDS,
author = "Hongqing Cao and Lishan Kang and Yuping Chen",
title = "Evolutionary Modeling of Ordinary Differential
Equations for Dynamic Systems",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "959--965",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-401.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-401.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@Article{cao:2000:odeGP,
author = "Hongqing Cao and Lishan Kang and Yuping Chen and
Jingxian Yu",
title = "Evolutionary Modeling of Systems of Ordinary
Differential Equations with Genetic Programming",
journal = "Genetic Programming and Evolvable Machines",
year = "2000",
volume = "1",
number = "4",
pages = "309--337",
month = oct,
keywords = "genetic algorithms, genetic programming, evolutionary
modeling, system of ordinary differential equations,
higher-order ordinary differential equation",
ISSN = "1389-2576",
URL = "http://www.ees.adelaide.edu.au/people/enviro/cao/2000-05.pdf",
doi = "doi:10.1023/A:1010013106294",
size = "29 pages",
abstract = "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.",
notes = "Article ID: 273810",
}
@Article{cao:2000:ode2GP,
author = "Hong-Qing Cao and Li-Shan Kang and Tao Guo and Yu-Ping
Chen and Hugo {de Garis}",
title = "A two-level hybrid evolutionary algorithm for modeling
one-dimensional dynamic systems by higher-order {ODE}
models",
journal = "IEEE Transactions on Systems, Man and Cybernetics --
Part B: Cybernetics",
year = "2000",
volume = "40",
number = "2",
pages = "351--357",
month = apr,
keywords = "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",
ISSN = "1083-4419",
URL = "http://ieeexplore.ieee.org/iel5/3477/18067/00836383.pdf",
size = "7 pages",
abstract = "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.",
}
@Article{cao:2001:CC,
author = "Hongqing Cao and Jingxian Yu and Lishan Kang and Hanxi
Yang and Xinping Ai",
title = "Modeling and prediction for discharge lifetime of
battery systems using hybrid evolutionary algorithms",
journal = "Computers \& Chemistry",
year = "2001",
volume = "25",
number = "3",
pages = "251--259",
month = may,
keywords = "genetic algorithms, genetic programming, Discharge
lifetime of battery systems, Lithium-ion battery,
Hybrid evolutionary modelling",
ISSN = "0097-8485",
doi = "doi:10.1016/S0097-8485(00)00099-1",
abstract = "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.",
notes = "http://www.elsevier.com/wps/find/journaldescription.cws_home/627320/description#description",
}
@Article{cao:2003:WUJNS,
author = "Hongqing Cao and Lishan Kang and Jingxian Yu",
title = "Parallel Implementations of Modeling Dynamical Systems
by Using System of Ordinary Differential Equations",
journal = "Wuhan University Journal of Natural Sciences",
year = "2003",
volume = "8",
number = "IB",
pages = "229--233",
keywords = "genetic algorithms, genetic programming",
}
@Article{cao:2003:NPSC,
author = "Hongqing Cao and Jingxian Yu and Lishan Kang and R I
Bob McKay",
title = "An Experimental Study of Some Control Parameters in
Parallel Genetic Programming",
journal = "Neural, Parallel and Scientific Computation",
year = "2003",
volume = "11",
number = "4",
pages = "377--393",
keywords = "genetic algorithms, genetic programming",
}
@Article{cao:2003:CMA,
author = "Hongqing Cao and Lishan Kang and Yuping Chen and Tao
Guo",
title = "The Dynamic Evolutionary Modeling of {HODE}s for Time
Series Prediction",
journal = "Computers \& Mathematics with Applications",
year = "2003",
volume = "46",
number = "8-9",
pages = "1397--1411",
keywords = "genetic algorithms, genetic programming, Time series,
Differential equation",
URL = "http://www.sciencedirect.com/science/article/B6TYJ-4BRR761-P/2/4d226ed6e682798de2e1d83d01cebd95",
doi = "doi:10.1016/S0898-1221(03)90228-8",
abstract = "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.",
}
@InProceedings{Cao:2003:Aeafmtecfeis,
author = "Hongqing Cao and Jingxian Yu and Lishan Kang",
title = "An evolutionary approach for modeling the equivalent
circuit for electrochemical impedance spectroscopy",
booktitle = "Proceedings of the 2003 Congress on Evolutionary
Computation CEC2003",
editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass
and Kay Chen Tan and Bob McKay and Daryl Essam and Tom
Gedeon",
pages = "1819--1825",
year = "2003",
publisher = "IEEE Press",
address = "Canberra",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "8-12 " # dec,
organisation = "IEEE Neural Network Council (NNC), Engineers Australia
(IEAust), Evolutionary Programming Society (EPS),
Institution of Electrical Engineers (IEE)",
ISBN = "0-7803-7804-0",
notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust,
the EPS, and the IEE.",
keywords = "genetic algorithms, genetic programming, Gene
Expression Programming, GEP, HEMA",
URL = "http://www.ees.adelaide.edu.au/people/enviro/cao/2003-05.pdf",
abstract = "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.",
size = "7 pages",
}
@Article{Cao:2006:EI,
author = "Hongqing Cao and Friedrich Recknagel and Gea-Jae Joo
and Dong-Kyun Kim",
title = "Discovery of Predictive Rule Sets for Chlorophyll-a
Dynamics in the Nakdong River (Korea) by Means of the
Hybrid Evolutionary Algorithm {HEA}",
journal = "Ecological Informatics",
year = "2006",
volume = "1",
number = "1",
pages = "43--53",
month = jan,
keywords = "genetic algorithms, genetic programming, Hybrid
evolutionary algorithm, Rule sets, Chl.a, Sensitivity
analysis, Nakdong River",
ISSN = "1574-9541",
doi = "doi:10.1016/j.ecoinf.2005.08.001",
size = "11 pages",
abstract = "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.",
notes = "http://www.elsevier.com/wps/find/journaldescription.cws_home/705192/description#description",
}
@InCollection{Cao:2006:2lakes,
author = "Hongqing Cao and Friedrich Recknagel and Bomchul Kim
and Noriko Takamura",
title = "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",
booktitle = "Ecological Informatics: Scope, Techniques and
Applications",
publisher = "Springer-Verlag",
year = "2006",
editor = "Friedrich Recknagel",
chapter = "17",
pages = "347--367",
address = "Berlin, Heidelberg, New York",
edition = "2nd",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-28383-8",
notes = "http://www.springer.com/sgw/cda/frontpage/0,11855,5-10031-22-68637391-0,00.html",
}
@Article{Cao2008181,
author = "Hongqing Cao and Friedrich Recknagel and Lydia Cetin
and Byron Zhang",
title = "Process-based simulation library {SALMO}-{OO} for lake
ecosystems. Part 2: Multi-objective parameter
optimization by evolutionary algorithms",
journal = "Ecological Informatics",
volume = "3",
number = "2",
pages = "181--190",
year = "2008",
ISSN = "1574-9541",
doi = "doi:10.1016/j.ecoinf.2008.02.001",
URL = "http://www.sciencedirect.com/science/article/B7W63-4S69SG8-1/2/95e920ec339c554888f67696a93f2f37",
keywords = "genetic algorithms, genetic programming,
Multi-objective parameter optimization, SALMO-OO, Lake
categories, Evolutionary algorithms",
abstract = "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.",
}
@InProceedings{cao:1999:CMSUNN,
author = "Lijuan Cao and Tay Eng Hock (Francis) and Ma Lawrence
and Wai Cheong Yeong",
title = "Classification of the Market States Using Neural
Network",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "776",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems, poster
papers",
ISBN = "1-55860-611-4",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{cao:1999:NBMCANACS,
author = "Lijuan Cao and Tay Eng Hock (Francis)",
title = "Neuro-Genetic Based Method to the Classification of
Acupuncture Needle: {A} Case Study",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "99--105",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@Article{Capcarrece:2004:BS,
author = "Mathieu S. Capcarrece",
title = "An evolving ontogenetic cellular system for better
adaptiveness",
journal = "Biosystems",
year = "2004",
volume = "76",
pages = "177--189",
number = "1-3",
abstract = "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.",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6T2K-4D1R6V6-2/2/ceb26b0139eed613393486f88bc2ac23",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1016/j.biosystems.2004.05.020",
notes = "Papers presented at the Fifth International Workshop
on Information Processing in Cells and Tissues
PMID: 15351141 [PubMed - indexed for MEDLINE]",
}
@Proceedings{DBLP:conf/ecal/2005,
editor = "Mathieu S. Capcarrere and Alex Alves Freitas and Peter
J. Bentley and Colin G. Johnson and Jon Timmis",
title = "8th European Conference on Advances in Artificial
Life, {ECAL} 2005",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3630",
year = "2005",
ISBN = "3-540-28848-1",
address = "Canterbury, UK",
month = sep # " 5-9",
bibsource = "DBLP, http://dblp.uni-trier.de",
}
@InCollection{caplan:2004:GPTP,
author = "Michael Caplan and Ying Becker",
title = "Lessons Learned Using Genetic Programming in a Stock
Picking Context",
booktitle = "Genetic Programming Theory and Practice {II}",
year = "2004",
editor = "Una-May O'Reilly and Tina Yu and Rick L. Riolo and
Bill Worzel",
chapter = "6",
pages = "87--102",
address = "Ann Arbor",
month = "13-15 " # may,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, stock
selection, data mining, fitness functions, quantitative
portfolio management",
ISBN = "0-387-23253-2",
doi = "doi:10.1007/0-387-23254-0_6",
abstract = "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.",
notes = "part of \cite{oreilly:2004:GPTP2}",
}
@InProceedings{garcia:1999:efrbcGAPga,
author = "Santiago Garcia and Fermin Gonzalez and Luciano
Sanchez",
title = "Evolving Fuzzy Rule Based Classifiers with {GA-P}: {A}
Grammatical Approach",
booktitle = "Genetic Programming, Proceedings of EuroGP'99",
year = "1999",
editor = "Riccardo Poli and Peter Nordin and William B. Langdon
and Terence C. Fogarty",
volume = "1598",
series = "LNCS",
pages = "203--210",
address = "Goteborg, Sweden",
publisher_address = "Berlin",
month = "26-27 " # may,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-65899-8",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1598&spage=203",
notes = "EuroGP'99, part of \cite{poli:1999:GP}
Combination of grammar based GP and GA-P with fuzzy
rules. UCI machine learning databases
First author is Santiago Garcia Carbajal",
}
@Article{carbajal:2001:GPEM,
author = "Santiago {Garcia Carbajal} and Fermin Gonzalez
Martinez",
title = "Evolutive Introns: {A} Non-Costly Method of Using
Introns in {GP}",
journal = "Genetic Programming and Evolvable Machines",
year = "2001",
volume = "2",
number = "2",
pages = "111--122",
month = jun,
keywords = "genetic algorithms, genetic programming, bloating,
introns, intertwined spirals",
ISSN = "1389-2576",
doi = "doi:10.1023/A:1011548229751",
abstract = "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.",
notes = "Article ID: 335711",
}
@PhdThesis{GarciaCarbajal:thesis,
author = "Santiago {Garcia Carbajal}",
title = "Automatic Identification of Partial Goals with
Grammar-Directed Genetic Programming",
school = "Faculty of Informatics. GIJON",
year = "2002",
email = "santi.carbajal@gmail.com",
keywords = "genetic algorithms, genetic programming, algorithms,
grammar directed GP",
size = "180 pages",
abstract = "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.",
notes = "In spanish. Available by email",
}
@InProceedings{garcia03,
author = "Santiago Garcia and John Levine and Fermin Gonzalez",
title = "Multi Niche Parallel {GP} with a Junk-code Migration
Model",
booktitle = "Genetic Programming, Proceedings of EuroGP'2003",
year = "2003",
editor = "Conor Ryan and Terence Soule and Maarten Keijzer and
Edward Tsang and Riccardo Poli and Ernesto Costa",
volume = "2610",
series = "LNCS",
pages = "327--334",
address = "Essex",
publisher_address = "Berlin",
month = "14-16 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-00971-X",
URL = "http://www.aiai.ed.ac.uk/~johnl/papers/garcia-eurogp03.ps",
URL = "http://citeseer.ist.psu.edu/575183.html",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=327",
abstract = "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.",
notes = "EuroGP'2003 held in conjunction with EvoWorkshops
2003",
}
@InProceedings{Carbajal:2004:AL,
author = "Santiago {Garcia Carbajal} and Martin Bosque Moran and
Fermin Gonzales Martinez",
title = "{EvolGL:} Life in a Pond",
booktitle = "Artificial Life {XI} Ninth International Conference on
the Simulation and Synthesis of Living Systems",
year = "2004",
editor = "Jordan Pollack and Mark Bedau and Phil Husbands and
Takashi Ikegami and Richard A. Watson",
pages = "75--80",
address = "Boston, Massachusetts",
month = "12-15 " # sep,
publisher = "The MIT Press",
keywords = "genetic algorithms, genetic programming, GA-P,
artificial Life",
ISBN = "0-262-66183-7",
notes = "http://www.alife9.org/ ALIFE9
3D artificial life forms. Evolworm. Promotion of
introns (junk code). Genetic definition (GA-P
\cite{howard: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.",
}
@Article{Garcia:2006:IJSC,
author = "Santiago {Garcia Carbajal} and Nouhad J. Rizk",
title = "Hierarchical Reinforcement Learning with
Grammar-Directed {GA}-{P}",
journal = "International Journal of Soft Computing",
year = "2006",
volume = "1",
number = "1",
pages = "52--60",
month = mar,
email = "carbajal@lsi.uniovi.es",
keywords = "genetic algorithms, genetic programming, reinforcement
learning, grammar, knowledge",
ISSN = "1816-9503",
abstract = "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.",
notes = "http://www.medwellonline.net/ijcs/",
}
@InCollection{Carbajal:2007:SSCE,
author = "Santiago Garcia Carbajal and David Corne and Alejandro
Conty",
title = "Parallelizing Automatic Induction of Langton Parameter
with Genetic Programming",
booktitle = "Science and Supercomputing in Europe",
publisher = "Cineca, Italy",
year = "2007",
editor = "Giovanni erbacci",
volume = "2006",
pages = "540--544",
email = "sgarcia@uniovi.es",
keywords = "genetic algorithms, genetic programming, cellular
automata, parallel programming",
isbn_13 = "978-88-86037-19-8",
URL = "http://www.hpc-europa.org/CD2006/contents/112-Math-Garcia.PDF",
URL = "http://www.hpc-europa.org",
abstract = "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.",
size = "5 pages",
}
@Article{GarciaCarbajal:2007:PPL,
author = "Santiago {Garcia Carbajal}",
title = "Parallelizing Three Dimensional Cellular Automata With
{OpenMP}",
journal = "Parallel Processing Letters",
year = "2007",
volume = "17",
number = "4",
pages = "349--361",
month = dec,
email = "sgarcia@uniovi.es",
keywords = "genetic algorithms, genetic programming, cellular
automata, Parallel Programming",
ISSN = "0129-6264",
URL = "http://www.worldscinet.com/ppl/ppl.shtml",
doi = "doi:10.1142/S0129626407003083",
abstract = "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.",
notes = "PPL",
}
@Misc{card:1999:GPWNTSP,
author = "Stuart Card",
title = "Genetic Programming of Wavelet Networks for Time
Series Prediction",
booktitle = "GECCO-99 Student Workshop",
year = "1999",
editor = "Una-May O'Reilly",
pages = "341--342",
address = "Orlando, Florida, USA",
month = "13 " # jul,
keywords = "genetic algorithms, genetic programming, neural-nets,
wavelets, time, scale, frequency, prediction,
stochastic, nonlinear",
URL = "http://www.borg.com/~stu/GECCO99.html",
abstract = "A hybrid genetic programming / neural network /
wavelet technique for time series prediction is
proposed. Iterative software development and
experimentation are ongoing.",
notes = "GECCO-99WKS Part of wu:1999:GECCOWKS",
}
@InProceedings{card:2004:gsw:swcar,
author = "Stuart W. Card",
title = "Time Series Prediction by Genetic Programming with
Relaxed Assumptions in Mathematica",
editor = "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",
booktitle = "GECCO 2004 Workshop Proceedings",
year = "2004",
month = "26-30 " # jun,
address = "Seattle, Washington, USA",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/WGSW002.pdf",
notes = "GECCO-2004WKS Distributed on CD-ROM at GECCO-2004",
}
@InProceedings{card:2005:CEC,
author = "Stuart W. Card and Chilukuri K. Mohan",
title = "Information Theoretic Indicators of Fitness, Relevant
Diversity \& Pairing Potential in Genetic Programming",
booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary
Computation",
year = "2005",
editor = "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",
volume = "3",
pages = "2545--2552",
address = "Edinburgh, UK",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "2-5 " # sep,
organisation = "IEEE Computational Intelligence Society, Institution
of Electrical Engineers (IEE), Evolutionary Programming
Society (EPS)",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-9363-5",
abstract = "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.",
notes = "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",
}
@InProceedings{1144254,
author = "Stuart W. Card and Chilukuri K. Mohan",
title = "Ensemble selection for evolutionary learning using
information theory and price's theorem",
booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference
on Genetic and evolutionary computation",
year = "2006",
editor = "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",
volume = "2",
ISBN = "1-59593-186-4",
pages = "1587--1588",
address = "Seattle, Washington, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p1587.pdf",
doi = "doi:10.1145/1143997.1144254",
publisher = "ACM Press",
publisher_address = "New York, NY, 10286-1405, USA",
month = "8-12 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "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",
notes = "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",
}
@InCollection{Card:2007:GPTP,
author = "Stuart W. Card and Chilukuri K. Mohan",
title = "Towards an Information Theoretic Framework for Genetic
programming",
booktitle = "Genetic Programming Theory and Practice {V}",
year = "2007",
editor = "Rick L. Riolo and Terence Soule and Bill Worzel",
series = "Genetic and Evolutionary Computation",
chapter = "6",
pages = "87--106",
address = "Ann Arbor",
month = "17-19 " # may,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1007/978-0-387-76308-8",
size = "19 pages",
abstract = "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.",
notes = "http://www.cscs.umich.edu/events/gptp2007/
Card-Mohan-draft-2007-4-4.pdf
part of \cite{Riolo:2007:GPTP} To be published after
workshop Jan 2008?",
}
@InCollection{Card:2008:GPTP,
author = "Stuart W. Card and Chilukuri K. Mohan",
title = "An Application of Information Theoretic Selection to
Evolution of Models with Continuous-valued Inputs",
booktitle = "Genetic Programming Theory and Practice {VI}",
year = "2008",
editor = "Rick L. Riolo and Terence Soule and Bill Worzel",
series = "Genetic and Evolutionary Computation",
chapter = "3",
pages = "29--43",
address = "Ann Arbor",
month = "15-17" # may,
publisher = "Springer",
size = "14 pages",
isbn13 = "978-0-387-87622-1",
notes = "part of \cite{Riolo:2008:GPTP} To be published late
2008",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{Card:2010:geccocomp,
author = "Stuart W. Card",
title = "Information distance based fitness and diversity
metrics",
booktitle = "GECCO 2010 Entropy, information and complexity",
year = "2010",
editor = "Stuart William Card and Yossi Borenstein",
isbn13 = "978-1-4503-0073-5",
keywords = "genetic algorithms, genetic programming",
pages = "1851--1854",
month = "7-11 " # jul,
organisation = "SIGEVO",
address = "Portland, Oregon, USA",
doi = "doi:10.1145/1830761.1830815",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{1830815} Distributed on CD-ROM at
GECCO-2010.
ACM Order Number 910102.",
}
@InProceedings{Cardamone:2011:DSoPIuGP,
title = "Dynamic Synthesis of Program Invariants using Genetic
Programming",
author = "Luigi Cardamone and Andrea Mocci and Carlo Ghezzi",
pages = "617--624",
booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary
Computation",
year = "2011",
editor = "Alice E. Smith",
month = "5-8 " # jun,
address = "New Orleans, USA",
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming, SBSE",
abstract = "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.",
notes = "CEC2011 sponsored by the IEEE Computational
Intelligence Society, and previously sponsored by the
EPS and the IET.",
}
@InProceedings{CarPai02,
author = "Jonas Carlsson and Carlos Paiz and Krister Wolff and
Peter Nordin",
title = "Interactive Evolution of Speech using Voice{XML}
Speaking to you {GP} System.",
booktitle = "Proceedings of the 6th World Multiconference on
Systemics, Cybernetics and Informatics",
year = "2002",
editor = "Nagib Callaos and Alexander Pisarchik and Mitsuyoshi
Ueda",
volume = "VI",
pages = "58--62",
publisher = "IIIS",
keywords = "genetic algorithms, genetic programming, voice XML",
ISBN = "980-07-8150-1",
abstract = "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.",
}
@InCollection{carobus:2000:EGPBUGPCPNH,
author = "Alexander P. Carobus",
title = "Evolution of Game Playing Behavior: Using Genetic
Programming to Create Players for Net Hack",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2000",
year = "2000",
editor = "John R. Koza",
pages = "60--69",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "part of \cite{koza:2000:gagp}",
}
@InProceedings{Carreno:2007:cec,
author = "Emiliano Carreno and Guillermo Leguizamon and Neal
Wagner",
title = "Evolution of Classification Rules for Comprehensible
Knowledge Discovery",
booktitle = "2007 IEEE Congress on Evolutionary Computation",
year = "2007",
editor = "Dipti Srinivasan and Lipo Wang",
pages = "1261--1268",
address = "Singapore",
month = "25-28 " # sep,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "1-4244-1340-0",
file = "1695.pdf",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and
the IET.
IEEE Catalog Number: 07TH8963C",
}
@Article{CarrenoJara:2011:GPEM,
author = "Emiliano {Carreno Jara}",
title = "Long memory time series forecasting by using genetic
programming",
journal = "Genetic Programming and Evolvable Machines",
year = "2012",
volume = "12",
number = "4",
pages = "429--456",
month = dec,
keywords = "genetic algorithms, genetic programming, Long memory,
Time series forecasting, Multi-objective search, ARFIMA
models",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-011-9140-7",
size = "28 pages",
abstract = "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.",
notes = "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",
}
@InProceedings{Carreras:2010:percomWKS,
author = "Iacopo Carreras and David Linner",
title = "Self-evolving applications over opportunistic
communication systems",
booktitle = "8th IEEE International Conference on Pervasive
Computing and Communications Workshops (PERCOM
Workshops, 2010)",
year = "2010",
month = mar # " 29-" # apr # " 2",
pages = "153--158",
keywords = "genetic algorithms, genetic programming, BioNets, P2P,
mobile devices, opportunistic communication systems,
self-evolving applications, mobile radio",
abstract = "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.",
doi = "doi:10.1109/PERCOMW.2010.5470677",
notes = "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 \cite{5470677}",
}
@InProceedings{DBLP:conf/flairs/CarseP01,
author = "Brian Carse and Anthony G. Pipe",
title = "A Framework for Evolving Fuzzy Classifier Systems
Using Genetic Programming",
booktitle = "Proceedings of the Fourteenth International Florida
Artificial Intelligence Research Society Conference",
year = "2001",
editor = "Ingrid Russell and John F. Kolen",
pages = "465--469",
address = "Key West, Florida, USA",
month = may # " 21-23",
publisher = "AAAI Press",
bibsource = "DBLP, http://dblp.uni-trier.de",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-57735-133-9",
abstract = "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",
}
@Article{Carvalho:2002:ASC,
author = "D. R. Carvalho and A. A. Freitas",
title = "A genetic algorithm for discovering small disjunct
rules in data mining",
journal = "Applied Soft Computing",
year = "2002",
volume = "2",
number = "2",
pages = "75--88",
month = dec,
keywords = "genetic algorithms, data mining, classification, Rule
discovery, Small disjuncts",
URL = "http://www.sciencedirect.com/science/article/B6W86-477FN8B-1/2/2704d983f8282d055e302ebab5471fc1",
URL = "http://www.cs.kent.ac.uk/people/staff/aaf/my-publications-ukc.html",
doi = "doi:10.1016/S1568-4946(02)00031-5",
abstract = "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.",
}
@InProceedings{Casanova:2010:cec,
author = "Isidoro J. Casanova",
title = "Tradinnova-{LCS}: Dynamic stock portfolio
decision-making assistance model with genetic based
machine learning",
booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-6910-9",
abstract = "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.",
doi = "doi:10.1109/CEC.2010.5586067",
notes = "WCCI 2010. Also known as \cite{5586067}",
}
@InProceedings{Castelli:2010:cec,
author = "Mauro Castelli and Luca Manzoni and Sara Silva and
Leonardo Vanneschi",
title = "A comparison of the generalization ability of
different genetic programming frameworks",
booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-6910-9",
abstract = "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.",
doi = "doi:10.1109/CEC.2010.5585925",
notes = "WCCI 2010. Also known as \cite{5585925}",
}
@InProceedings{castelli:2011:EuroGP,
author = "Mauro Castelli and Luca Manzoni and Sara Silva and
Leonardo Vanneschi",
title = "A Quantitative Study of Learning and Generalization in
Genetic Programming",
booktitle = "Proceedings of the 14th European Conference on Genetic
Programming, EuroGP 2011",
year = "2011",
month = "27-29 " # apr,
editor = "Sara Silva and James A. Foster and Miguel Nicolau and
Mario Giacobini and Penousal Machado",
series = "LNCS",
volume = "6621",
publisher = "Springer Verlag",
address = "Turin, Italy",
pages = "25--36",
organisation = "EvoStar",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-20406-7",
doi = "doi:10.1007/978-3-642-20407-4_3",
abstract = "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.",
notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in
conjunction with EvoCOP2011 EvoBIO2011 and
EvoApplications2011",
}
@InProceedings{castillo:2002:gecco,
author = "Flor A. Castillo and Ken A. Marshall and James L.
Green and Arthur K. Kordon",
title = "Symbolic Regression In Design Of Experiments: {A} Case
Study With Linearizing Transformations",
booktitle = "GECCO 2002: Proceedings of the Genetic and
Evolutionary Computation Conference",
editor = "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",
year = "2002",
pages = "1043--1047",
address = "New York",
publisher_address = "San Francisco, CA 94104, USA",
month = "9-13 " # jul,
publisher = "Morgan Kaufmann Publishers",
keywords = "genetic algorithms, genetic programming, real world
applications, design of experiment (DoE), lack of fit,
linearizing transformations, symbolic regression",
ISBN = "1-55860-878-8",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/RWA194.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-20.pdf",
notes = "GECCO-2002. A joint meeting of the eleventh
International Conference on Genetic Algorithms
(ICGA-2002) and the seventh Annual Genetic Programming
Conference (GP-2002)",
}
@InProceedings{Castillo:2003:gecco,
author = "Flor Castillo and Kenric Marshall and James Green and
Arthur Kordon",
title = "A Methodology for Combining Symbolic Regression and
Design of Experiments to Improve Empirical Model
Building",
booktitle = "Genetic and Evolutionary Computation -- GECCO-2003",
editor = "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",
year = "2003",
pages = "1975--1985",
address = "Chicago",
publisher_address = "Berlin",
month = "12-16 " # jul,
volume = "2724",
series = "LNCS",
ISBN = "3-540-40603-4",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, symbolic
regression, design of experiments, Real World
Applications",
abstract = "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.",
notes = "GECCO-2003. A joint meeting of the twelfth
International Conference on Genetic Algorithms
(ICGA-2003) and the eights Annual Genetic Programming
Conference (GP-2003)",
}
@InCollection{castillo:2004:GPTP,
author = "Flor Castillo and Arthur Kordon and Jeff Sweeney and
Wayne Zirk",
title = "Using Genetic Programming in Industrial Statistical
Model Building",
booktitle = "Genetic Programming Theory and Practice {II}",
year = "2004",
editor = "Una-May O'Reilly and Tina Yu and Rick L. Riolo and
Bill Worzel",
chapter = "3",
pages = "31--48",
address = "Ann Arbor",
month = "13-15 " # may,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, statistical
model building, symbolic regression, undesigned data",
ISBN = "0-387-23253-2",
doi = "doi:10.1007/0-387-23254-0_3",
abstract = "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.",
notes = "part of \cite{oreilly:2004:GPTP2}",
}
@InProceedings{castillo:1999:GGOMPEA,
author = "P. A. Castillo and V. Rivas and J. J. Merelo and J.
Gonzalez and A. Prieto and G. Romero",
title = "{G}-Prop-{III}: Global Optimization of Multilayer
Perceptrons using an Evolutionary Algorithm",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "942",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "evolution strategies and evolutionary programming,
poster papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/G-Prop-III_poster.ps.gz",
URL = "http://geneura.ugr.es/~pedro/gprop/G-Prop-III_poster.ps.gz",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{castillo:2004:eurogp,
author = "Pedro A. Castillo and Maribel G. Arenas and J. J.
Merelo and Gustavo Romero and Fatima Rateb and Alberto
Prieto",
title = "Comparing hybrid systems to design and optimize
artificial neural networks",
booktitle = "Genetic Programming 7th European Conference, EuroGP
2004, Proceedings",
year = "2004",
editor = "Maarten Keijzer and Una-May O'Reilly and Simon M.
Lucas and Ernesto Costa and Terence Soule",
volume = "3003",
series = "LNCS",
pages = "240--249",
address = "Coimbra, Portugal",
publisher_address = "Berlin",
month = "5-7 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-21346-5",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=240",
abstract = "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.",
notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in
conjunction with EvoCOP2004 and EvoWorkshops2004",
}
@InProceedings{castillo:2004:ueatsvtilmls,
title = "Using Evolutionary Algorithms to Suggest Variable
Transformations in Linear Model Lack-of-Fit
Situations",
author = "Flor Castillo and Jeff Sweeney and Wayne Zirk",
pages = "556--560",
booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary
Computation",
year = "2004",
publisher = "IEEE Press",
month = "20-23 " # jun,
address = "Portland, Oregon",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming, Evolutionary
Computing in the Process Industry",
abstract = "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.",
notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and
the IEE.",
}
@InCollection{Castillo:2006:GPTP,
author = "Flor Castillo and Arthur Kordon and Guido Smits",
title = "Robust Pareto Front Genetic Programming Parameter
Selection Based on Design of Experiments and Industrial
Data",
booktitle = "Genetic Programming Theory and Practice {IV}",
year = "2006",
editor = "Rick L. Riolo and Terence Soule and Bill Worzel",
volume = "5",
series = "Genetic and Evolutionary Computation",
chapter = "2",
pages = "-",
address = "Ann Arbor",
month = "11-13 " # may,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, symbolic
regression, industrial applications, design of
experiments, parameter selection",
ISBN = "0-387-33375-4",
size = "18 pages",
abstract = "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.",
notes = "part of \cite{Riolo:2006:GPTP} Published Jan 2007
after the workshop",
}
@InProceedings{1144264,
author = "Flor Castillo and Arthur Kordon and Guido Smits and
Ben Christenson and Dee Dickerson",
title = "Pareto front genetic programming parameter selection
based on design of experiments and industrial data",
booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference
on Genetic and evolutionary computation",
year = "2006",
editor = "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",
volume = "2",
ISBN = "1-59593-186-4",
pages = "1613--1620",
address = "Seattle, Washington, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p1613.pdf",
doi = "doi:10.1145/1143997.1144264",
publisher = "ACM Press",
publisher_address = "New York, NY, 10286-1405, USA",
month = "8-12 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, Real-World
Applications, industrial applications, Pareto front,
statistical design of experiments, symbolic
regression",
notes = "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",
}
@InCollection{Castillo:2010:GPTP,
author = "Flor Castillo and Arthur Kordon and Carlos Villa",
title = "Genetic Programming Transforms in Linear Regression
Situations",
booktitle = "Genetic Programming Theory and Practice VIII",
year = "2010",
editor = "Rick Riolo and Trent McConaghy and Ekaterina
Vladislavleva",
series = "Genetic and Evolutionary Computation",
volume = "8",
address = "Ann Arbor, USA",
month = "20-22 " # may,
publisher = "Springer",
chapter = "11",
pages = "175--194",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4419-7746-5",
URL = "http://www.springer.com/computer/ai/book/978-1-4419-7746-5",
notes = "part of \cite{Riolo:2010:GPTP}",
}
@InProceedings{Castle:2010:EuroGP,
author = "Tom Castle and Colin G. Johnson",
title = "Positional Effect of Crossover and Mutation in
Grammatical Evolution",
booktitle = "Proceedings of the 13th European Conference on Genetic
Programming, EuroGP 2010",
year = "2010",
editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara
Silva and Stephen Dignum and A. Sima Uyar",
volume = "6021",
series = "LNCS",
pages = "26--37",
address = "Istanbul",
month = "7-9 " # apr,
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, Grammatical
Evolution, crossover, mutation, position, bias",
isbn13 = "978-3-642-12147-0",
doi = "doi:10.1007/978-3-642-12148-7_3",
abstract = "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.",
notes = "5-parity, Santa Fe trail, 6-mux, symbolic regression
Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010
held in conjunction with EvoCOP2010 EvoBIO2010 and
EvoApplications2010",
}
@InProceedings{castle:2012:EuroGP,
author = "Tom Castle and Colin G. Johnson",
title = "Evolving High-Level Imperative Program Trees with
Strongly Formed Genetic Programming",
booktitle = "Proceedings of the 15th European Conference on Genetic
Programming, EuroGP 2012",
year = "2012",
month = "11-13 " # apr,
editor = "Alberto Moraglio and Sara Silva and Krzysztof Krawiec
and Penousal Machado and Carlos Cotta",
series = "LNCS",
volume = "7244",
publisher = "Springer Verlag",
address = "Malaga, Spain",
pages = "1--12",
organisation = "EvoStar",
isbn13 = "978-3-642-29138-8",
URL = "http://www.cs.kent.ac.uk/pubs/2012/3202/content.pdf",
doi = "doi:10.1007/978-3-642-29139-5_1",
size = "12 pages",
keywords = "genetic algorithms, genetic programming, Imperative
programming, Loops",
abstract = "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.",
notes = "EpochX
Part of \cite{Moraglio:2012:GP} EuroGP'2012 held in
conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012
and EvoApplications2012",
}
@InProceedings{Casula:2009:APSURSI,
author = "G. A. Casula and G. Mazzarella and N. Sirena",
title = "Genetic Programming design of wire antennas",
booktitle = "IEEE Antennas and Propagation Society International
Symposium, APSURSI '09",
year = "2009",
month = jun,
pages = "1--4",
keywords = "genetic algorithms, genetic programming, genetic
programming design, wire antennas",
doi = "doi:10.1109/APS.2009.5171505",
ISSN = "1522-3965",
abstract = "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.",
notes = "VSWR, SWR, gain, 800MHz
Also known as \cite{5171505}",
}
@InProceedings{Cattani:2010:cec,
author = "Phil T. Cattani and Colin G. Johnson",
title = "{ME}-{CGP}: Multi Expression Cartesian Genetic
Programming",
booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, Cartesian
Genetic Programming",
isbn13 = "978-1-4244-6910-9",
abstract = "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.",
doi = "doi:10.1109/CEC.2010.5586478",
notes = "WCCI 2010. Also known as \cite{5586478}",
}
@InProceedings{cattral:1999:RAGA,
author = "Robert Cattral and Franz Oppacher and Dwight Deugo",
title = "Rule Acquisition with a Genetic Algorithm",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "778",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, classifier
systems, poster papers",
ISBN = "1-55860-611-4",
abstract = "Data mining, applied to poisonous mushroom machine
learning benchmark",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{cavaretta:1999:DMGPTIPGE,
author = "Michael J. Cavaretta and Kumar Chellapilla",
title = "Data Mining using Genetic Programming: The
Implications of Parsimony on Generalization Error",
booktitle = "Proceedings of the Congress on Evolutionary
Computation",
year = "1999",
editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc
Schoenauer and Xin Yao and Ali Zalzala",
volume = "2",
pages = "1330--1337",
address = "Mayflower Hotel, Washington D.C., USA",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "6-9 " # jul,
organisation = "Congress on Evolutionary Computation, IEEE / Neural
Networks Council, Evolutionary Programming Society,
Galesia, IEE",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, data mining",
ISBN = "0-7803-5536-9 (softbound)",
ISBN = "0-7803-5537-7 (Microfiche)",
notes = "CEC-99 - A joint meeting of the IEEE, Evolutionary
Programming Society, Galesia, and the IEE.
Library of Congress Number = 99-61143",
}
@InCollection{caverlee:2000:AGAADOBS,
author = "James B. Caverlee",
title = "A Genetic Algorithm Approach to Discovering an Optimal
Blackjack Strategy",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2000",
year = "2000",
editor = "John R. Koza",
pages = "70--79",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms",
notes = "part of \cite{koza:2000:gagp}",
}
@InProceedings{1068300,
author = "Rachel Cavill and Steve Smith and Andy Tyrrell",
title = "Multi-chromosomal genetic programming",
booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on
Genetic and evolutionary computation",
year = "2005",
editor = "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",
volume = "2",
ISBN = "1-59593-010-8",
pages = "1753--1759",
address = "Washington DC, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1753.pdf",
doi = "doi:10.1145/1068009.1068300",
publisher = "ACM Press",
publisher_address = "New York, NY, 10286-1405, USA",
month = "25-29 " # jun,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, design,
performance, representations, team evolution",
notes = "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",
}
@InProceedings{Cavill:Tpo:cec2005,
author = "Rachel Cavill and Stephen L. Smith and Andy Tyrrell",
title = "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",
booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary
Computation",
year = "2005",
editor = "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",
pages = "935--941",
address = "Edinburgh, Scotland, UK",
month = "2-5 " # sep,
publisher = "IEEE Press",
volume = "1",
keywords = "genetic algorithms, genetic programming, biology,
cellular biophysics, evolutionary computation,
regression analysis, multiple chromosomes, polyploid
evolutionary algorithm, symbolic regression problem",
ISBN = "0-7803-9363-5",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=10417&isvol=1",
URL = "http://ieeexplore.ieee.org/servlet/opac?punumber=10417",
doi = "doi:10.1109/CEC.2005.1554783",
abstract = "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",
notes = "Last author is NOT Terrell",
}
@InProceedings{1144217,
author = "Rachel Cavill and Stephen L Smith and Andy M Tyrrell",
title = "Variable length genetic algorithms with multiple
chromosomes on a variant of the Onemax problem",
booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference
on Genetic and evolutionary computation",
year = "2006",
editor = "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",
volume = "2",
ISBN = "1-59593-186-4",
pages = "1405--1406",
address = "Seattle, Washington, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p1405.pdf",
doi = "doi:10.1145/1143997.1144217",
publisher = "ACM Press",
publisher_address = "New York, NY, 10286-1405, USA",
month = "8-12 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "Genetic Algorithms: Poster, algorithms performance
design, representation(s), size",
notes = "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",
}
@PhdThesis{cavill_mcgp,
author = "Rachel Cavill",
title = "Multi-Chromosomal Genetic Programming",
school = "Department of Electronics, University of York",
type = "{PhD} Dissertation",
year = "2006",
address = "UK",
keywords = "genetic algorithms, genetic programming",
}
@Article{journals/bioinformatics/CavillKHLNE09,
title = "Genetic algorithms for simultaneous variable and
sample selection in metabonomics",
author = "Rachel Cavill and Hector C. Keun and Elaine Holmes and
John C. Lindon and Jeremy K. Nicholson and Timothy M.
D. Ebbels",
journal = "Bioinformatics",
year = "2009",
number = "1",
volume = "25",
bibdate = "2009-06-22",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/bioinformatics/bioinformatics25.html#CavillKHLNE09",
pages = "112--118",
doi = "doi:10.1093/bioinformatics/btn586",
}
@Article{Cawley:2011:GPEM,
author = "Seamus Cawley and Fearghal Morgan and Brian McGinley
and Sandeep Pande and Liam McDaid and Snaider Carrillo
and Jim Harkin",
title = "Hardware spiking neural network prototyping and
application",
journal = "Genetic Programming and Evolvable Machines",
year = "2011",
volume = "12",
number = "3",
pages = "257--280",
month = sep,
note = "Special Issue Title: Evolvable Hardware Challenges",
keywords = "genetic algorithms, evolvable hardware, EMBRACE,
Spiking neural networks, Network on chip, Intrinsic
evolution, FPGA",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-011-9130-9",
size = "24 pages",
abstract = "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.",
}
@InCollection{cebrian:2004:IESANN,
author = "Manuel Cebrian and Alfonso Ortega {de la Puente} and
Manuel Alfonseca",
title = "Acceleration of a procedure to generate fractal curves
of a given dimension through the probabilistic analysis
of execution time",
booktitle = "Intelligent Engineering Systems Through Artificial
Neural Networks",
publisher = "ASME Press",
year = "2004",
editor = "C. H. Dagli and A. L. Buczak and D. L. Enke and M. J.
Embrecht",
volume = "14",
pages = "265--270",
address = "New York",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7918-0228-0",
URL = "http://www.ii.uam.es/~alfonsec/docs/annie.pdf",
notes = "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",
size = "6 pages",
}
@InProceedings{1277388,
author = "Manuel Cebrian and Manuel Alfonseca and Alfonso
Ortega",
title = "Automatic generation of benchmarks for plagiarism
detection tools using grammatical evolution",
booktitle = "GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation",
year = "2007",
editor = "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",
volume = "2",
isbn13 = "978-1-59593-697-4",
pages = "2253--2253",
address = "London",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2253.pdf",
doi = "doi:10.1145/1276958.1277388",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
month = "7-11 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, Real-World Applications: Poster, human
factors, reliability, source code plagiarism detection
tool assessment",
notes = "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",
}
@PhdThesis{Cebrian_Ramos:thesis,
author = "Manuel {Cebrian Ramos}",
title = "Using Algorithmic Information Theory and Stochastic
Modeling to Improve Classification and Evolutionary
Computation",
school = "Department of Computer Science, Universidad Autonoma
de Madrid",
year = "2007",
type = "Sobresaliente Cum Laude",
address = "Spain",
month = "13 " # jul,
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
URL = "digitool-uam.greendata.es:1801/webclient/DeliveryManager?pid=3411",
size = "244 pages",
abstract = "This thesis presents theoretical and practical
contributions in Algorithmic Information Theory and
(Algorithmic) Stochastic Modelling. Algorithmic
Information Theory is the theory concerned with
obtaining an absolute measure of the information
contained in an object. Stochastic Modelling is a
methodology to improve an algorithm's performance by
means of the introduction of random elements in its
logic.
One of the most interesting advances of Algorithmic
Information Theory is the development of an absolute
measure of similarity between objects. This measure can
only be estimated, as it is incomputable by definition.
The typical estimation relies on the use of data
compression algorithms, being this estimation known as
the compression distance. The two theoretical
contributions of this thesis analyse the quality of
this estimation. The first quantifies the estimation
robustness when the information contained in the
objects is noise-altered, concluding that it is
considerably resistant to noise. The second studies the
impact of the compression algorithm implementation on
the estimation, yielding some practical recipes for
making this choice.
We use variants of the compression distance to develop
two applications for classification and one for
evolutionary computation. The first application
addresses the problem of detecting similarities in
objects which have been generated by a predecessor
common source, independently of whether they use or not
the same coding scheme: this includes detecting
document translation and reconstructing phylogenetic
threes from genetic material. We make use of the
already proved usefulness of compression based
similarity distances for educational plagiarism
detection to develop our second application: AC, an
integrated source code plagiarism detection
environment. The third application makes use of this
distance as a fitness function, which is used by
evolutionary algorithms to automatically generate music
in a given pre-defined style.
Another three new applications are derived using
Stochastic Modeling, two for evolutionary computation
and one for classification. Two of them are intimately
related and make use of the presence of Heavy Tail
probability distributions in the optimisation processes
involved in the generation of fractals by an
evolutionary algorithm, and in the training process of
a multilayer perceptron. This discovery is used to
improve the performance of both algorithms by means of
restart strategies. The last application presented in
this thesis is a successful story of the use of a
special randomised heuristic in a simple genetic
algorithm to yield a state-of-the-art evolutionary
algorithm for solving Constraint Satisfaction
Problems.",
abstract = "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).",
notes = "In english. Supervised by Manuel Alfonseca Moreno /
Alfonso Ortega de la Puente",
}
@Article{Cebrian:2009:ieeeTEC,
author = "Manuel Cebrian and Manuel Alfonseca and Alfonso
Ortega",
title = "Towards the Validation of Plagiarism Detection Tools
by Means of Grammar Evolution",
journal = "IEEE Transactions on Evolutionary Computation",
year = "2009",
month = jun,
volume = "13",
number = "3",
pages = "477--485",
keywords = "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",
ISSN = "1089-778X",
doi = "doi:10.1109/TEVC.2008.2008797",
size = "9 pages",
abstract = "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.",
notes = "also known as \cite{4781609} Not GP",
}
@InCollection{cederberg:2002:TCTGAATNG,
author = "Scott Cederberg",
title = "The evolution of Cooperation: The Genetic Algorithm
Applied to Three Normal-Form Games",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2002",
year = "2002",
editor = "John R. Koza",
pages = "45--51",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms",
URL = "http://www.genetic-programming.org/sp2002/Cederberg.pdf",
notes = "part of \cite{koza:2002:gagp}",
}
@Article{Cellini:2004:FCT,
author = "F. Cellini and A. Chesson and I. Colquhoun and A.
Constable and H. V. Davies and K. H. Engel and A. M. R.
Gatehouse and S. Karenlampi and E. J. Kok and J. -J.
Leguay",
title = "Unintended effects and their detection in genetically
modified crops",
journal = "Food and Chemical Toxicology",
year = "2004",
volume = "42",
pages = "1089--1125",
number = "7",
abstract = "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.",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6T6P-4C004D3-3/2/aa7645e0537a1179bdf1f50aa4c376b3",
URL = "http://www.entransfood.com/products/publications/WG2_paper_rev1_19jan2004_unmarked.pdf",
keywords = "genetic algorithms, genetic programming",
size = "Review copy runs to 103 pages",
notes = "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)
\cite{Johnson:2000:eamGPsir}",
}
@InProceedings{Cerny:2008:gecco,
author = "Brian M. Cerny and Peter C. Nelson and Chi Zhou",
title = "Using differential evolution for symbolic regression
and numerical constant creation",
booktitle = "GECCO '08: Proceedings of the 10th annual conference
on Genetic and evolutionary computation",
year = "2008",
editor = "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",
isbn13 = "978-1-60558-130-9",
pages = "1195--1202",
address = "Atlanta, GA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1195.pdf",
doi = "doi:10.1145/1389095.1389331",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "12-16 " # jul,
keywords = "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",
notes = "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 \cite{1389331}",
}
@InProceedings{1274089,
author = "Ahmet Cetinkaya",
title = "Regular expression generation through grammatical
evolution",
booktitle = "Genetic and Evolutionary Computation Conference
{(GECCO2007)} workshop program",
year = "2007",
month = "7-11 " # jul,
editor = "Tina Yu",
isbn13 = "978-1-59593-698-1",
pages = "2643--2646",
address = "London, United Kingdom",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, regular expressions",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2643.pdf",
doi = "doi:10.1145/1274000.1274089",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "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.",
}
@Article{Cevik:2007:ES,
author = "Abdulkadir Cevik and Ibrahim H. Guzelbey",
title = "A soft computing based approach for the prediction of
ultimate strength of metal plates in compression",
journal = "Engineering Structures",
year = "2007",
volume = "29",
number = "3",
pages = "383--394",
month = mar,
keywords = "genetic algorithms, genetic programming, Soft
computing, Neural networks, Buckling, Plates",
doi = "doi:10.1016/j.engstruct.2006.05.005",
abstract = "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.",
}
@Article{Cevik:2007:JCSR,
author = "Abdulkadir Cevik",
title = "A new formulation for web crippling strength of
cold-formed steel sheeting using genetic programming",
journal = "Journal of Constructional Steel Research",
year = "2007",
volume = "63",
number = "7",
pages = "867--883",
month = jul,
keywords = "genetic algorithms, genetic programming, gene
expression programming, Web crippling, Cold-formed
steel decks, Formulation",
doi = "doi:10.1016/j.jcsr.2006.08.012",
size = "18 pages",
abstract = "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.",
notes = "Karva",
}
@Article{Cevik:2007:JCSRa,
author = "Abdulkadir Cevik",
title = "Genetic programming based formulation of rotation
capacity of wide flange beams",
journal = "Journal of Constructional Steel Research",
year = "2007",
volume = "63",
number = "7",
pages = "884--893",
month = jul,
keywords = "genetic algorithms, genetic programming, Rotation
capacity, Beams, Formulation",
doi = "doi:10.1016/j.jcsr.2006.09.004",
abstract = "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.",
}
@Article{Cevik:2007:JCSRb,
author = "A. Cevik",
title = "A new formulation for longitudinally stiffened webs
subjected to patch loading",
journal = "Journal of Constructional Steel Research",
year = "2007",
volume = "63",
pages = "1328--1340",
keywords = "genetic algorithms, genetic programming, Patch
loading, Formulation, Girders, Webs, Longitudinal
stiffeners",
doi = "doi:10.1016/j.jcsr.2006.12.004",
abstract = "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.",
}
@Article{Cevik2008117,
author = "Abdulkadir Cevik",
title = "Unified formulation for ultimate capacity of shear
failure of arc spot welding using genetic programming",
journal = "Journal of Materials Processing Technology",
volume = "204",
number = "1-3",
pages = "117--124",
year = "2008",
ISSN = "0924-0136",
doi = "doi:10.1016/j.jmatprotec.2007.10.064",
URL = "http://www.sciencedirect.com/science/article/B6TGJ-4R2H7VY-3/2/b16ece537522603ec7cc693ad17fd283",
keywords = "genetic algorithms, genetic programming, Arc spot
welding, Ultimate capacity, Shear failure",
abstract = "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",
}
@Article{Cevik2008:ESwA1,
author = "Abdulkadir Cevik and Ali Firat Cabalar",
title = "Modelling damping ratio and shear modulus of sand-mica
mixtures using genetic programming",
journal = "Expert Systems with Applications",
year = "2009",
volume = "36",
number = "4",
pages = "7749--7757",
month = may,
keywords = "genetic algorithms, genetic programming, Leighton
Buzzard sand, Mica, Resonant column testing",
ISSN = "0957-4174",
URL = "http://www.sciencedirect.com/science/article/B6V03-4TGHN90-2/2/78164c859cf3127425aedcca7e6f7d21",
doi = "doi:10.1016/j.eswa.2008.09.010",
size = "9 pages",
abstract = "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).",
}
@Article{Cevik:2008:ESwA2,
author = "Abdulkadir Cevik and Nihat Atmaca and Talha Ekmekyapar
and Ibrahim H. Guzelbey",
title = "Flexural buckling load prediction of aluminium alloy
columns using soft computing techniques",
journal = "Expert Systems with Applications",
year = "2009",
volume = "36",
number = "3, Part 2",
pages = "6332--6342",
month = apr,
keywords = "genetic algorithms, genetic programming,
Gene-expression programming, Soft computing, Neural
networks, Flexural buckling, Aluminium alloy columns",
ISSN = "0957-4174",
doi = "doi:10.1016/j.eswa.2008.08.011",
URL = "http://www.sciencedirect.com/science/article/B6V03-4TB6X28-1/2/3f64ccc54bc41be648922dc688ccad4a",
size = "11 pages",
abstract = "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.",
}
@Article{Cevik2010527,
author = "Abdulkadir Cevik and M. Tolga Gogus and Ibrahim H.
Guzelbey and Huzeyin Filiz",
title = "Soft computing based formulation for strength
enhancement of {CFRP} confined concrete cylinders",
journal = "Advances in Engineering Software",
volume = "41",
number = "4",
pages = "527--536",
year = "2010",
ISSN = "0965-9978",
doi = "doi:10.1016/j.advengsoft.2009.10.015",
URL = "http://www.sciencedirect.com/science/article/B6V1P-4XPBSMR-1/2/fce8b7ee023873cc437bf1c86ee3eb19",
keywords = "genetic algorithms, genetic programming, Soft
computing, Stepwise regression, FRP confinement,
Concrete cylinder, Strength enhancement",
abstract = "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.",
}
@Article{Cevik20112587,
author = "Abdulkadir Cevik and Ebru {Akcapinar Sezer} and Ali
Firat Cabalar and Candan Gokceoglu",
title = "Modeling of the uniaxial compressive strength of some
clay-bearing rocks using neural network",
journal = "Applied Soft Computing",
volume = "11",
number = "2",
pages = "2587--2594",
year = "2011",
note = "The Impact of Soft Computing for the Progress of
Artificial Intelligence",
ISSN = "1568-4946",
doi = "doi:10.1016/j.asoc.2010.10.008",
URL = "http://www.sciencedirect.com/science/article/B6W86-51F7PJN-1/2/29835a31bf86c4e457cfa3e0ae15bae5",
keywords = "genetic algorithms, genetic programming, Clay-bearing
rock, Uniaxial compressive strength, Neural network,
Slake durability index",
abstract = "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.",
}
@Article{Cevik20115650,
author = "Abdulkadir Cevik",
title = "Neuro-fuzzy modeling of rotation capacity of wide
flange beams",
journal = "Expert Systems with Applications",
volume = "38",
number = "5",
pages = "5650--5661",
year = "2011",
ISSN = "0957-4174",
doi = "doi:10.1016/j.eswa.2010.10.070",
URL = "http://www.sciencedirect.com/science/article/B6V03-51CJ387-K/2/ce5fff4acc0b21a9cd4c1ac3c5afe7df",
keywords = "genetic algorithms, genetic programming, Rotation
capacity, Beams, Neuro-fuzzy, Modelling",
abstract = "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.",
}
@Article{Cevik20115662,
author = "Abdulkadir Cevik",
title = "Modeling strength enhancement of {FRP} confined
concrete cylinders using soft computing",
journal = "Expert Systems with Applications",
volume = "38",
number = "5",
pages = "5662--5673",
year = "2011",
ISSN = "0957-4174",
doi = "doi:10.1016/j.eswa.2010.10.069",
URL = "http://www.sciencedirect.com/science/article/B6V03-51CJ387-J/2/4b0e7942a4c46980f638964d442e332a",
keywords = "genetic algorithms, genetic programming, Soft
computing, Neural networks, Neuro-fuzzy, Stepwise
regression, FRP confinement, Concrete cylinder,
Strength enhancement",
abstract = "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.",
}
@InCollection{chai:2000:DCCPDGP,
author = "Daniel Chai",
title = "Development of a Computer Controller Players for
Daleks using Genetic Programming",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2000",
year = "2000",
editor = "John R. Koza",
pages = "80--89",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "part of \cite{koza:2000:gagp}",
}
@InProceedings{Chaisricharoen:2009:ASICON,
author = "Roungsan Chaisricharoen and Boonruk Chipipop",
title = "Practical tuning of an {OTA}-{C} bandpass biquad via
recurrent geometric programming",
booktitle = "IEEE 8th International Conference on ASIC, ASICON
'09",
year = "2009",
month = "20-23 " # oct,
pages = "1193--1196",
abstract = "The geometric programming which can be globally solved
special cases of nonlinear problems is operated
recurrently with calibrated",
keywords = "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",
doi = "doi:10.1109/ASICON.2009.5351182",
notes = "not on GP. Sch. of Inf. Technol., Mae Fah Luang Univ.,
Chiang Rai, Thailand Also known as \cite{5351182}",
}
@InProceedings{chakraborti:1998:GAplaNLP,
author = "C. Chakraborti and K. K. N. Sastry",
title = "The Genetic Algorithms Approach for Proving Logical
Arguments in Natural Language",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "463--470",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms",
notes = "SGA-98",
}
@Article{Chakraborty:2008:IS,
author = "Uday K. Chakraborty",
title = "Genetic and evolutionary computing",
journal = "Information Sciences",
year = "2008",
volume = "178",
number = "23",
pages = "4419--4420",
month = "1 " # dec,
note = "Introduction to special section on Genetic and
Evolutionary Computing",
keywords = "genetic algorithms, genetic programming",
ISSN = "0020-0255",
}
@Article{Chakraborty:2008:IJICT,
author = "Uday K. Chakraborty",
title = "Genetic programming model of solid oxide fuel cell
stack: first results",
journal = "International Journal of Information and Communication
Technology (IJICT)",
year = "2008",
volume = "1",
number = "3/4",
pages = "453--461",
keywords = "genetic algorithms, genetic programming, solid oxide
fuel cells, SOFC stack, modelling, nonlinear dynamics,
simulation",
publisher = "Inderscience Publishers",
ISSN = "1741-8070",
bibsource = "OAI-PMH server at www.inderscience.com",
language = "eng",
URL = "http://www.inderscience.com/link.php?id=24015",
doi = "doi:10.1504/IJICT.2008.024015",
abstract = "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.",
}
@InProceedings{Chakraborty2:2009:cec,
author = "Uday K. Chakraborty",
title = "An Evolutionary Computation Approach to Predicting
Output Voltage from Fuel Utilization in {SOFC} Stacks",
booktitle = "2009 IEEE Congress on Evolutionary Computation",
year = "2009",
editor = "Andy Tyrrell",
pages = "2165--2171",
address = "Trondheim, Norway",
month = "18-21 " # may,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-2959-2",
file = "P686.pdf",
doi = "doi:10.1109/CEC.2009.4983209",
size = "7 pages",
abstract = "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.",
keywords = "genetic algorithms, genetic programming, RBFANN",
notes = "Fuel cell hydrogen + oxygen = steam + 1.18volts at
1000Centigrade and 1bar. DSS \cite{ga94aGathercole}
Discipulus. NeuroSolutions.
CEC 2009 - A joint meeting of the IEEE, the EPS and the
IET. IEEE Catalog Number: CFP09ICE-CDR",
}
@Article{Chakraborty2009740,
author = "Uday Kumar Chakraborty",
title = "Static and dynamic modeling of solid oxide fuel cell
using genetic programming",
journal = "Energy",
volume = "34",
number = "6",
pages = "740--751",
year = "2009",
ISSN = "0360-5442",
doi = "doi:10.1016/j.energy.2009.02.012",
URL = "http://www.sciencedirect.com/science/article/B6V2S-4W32975-1/2/c334dcacd8fee2c381ecd788e82d33fc",
keywords = "genetic algorithms, genetic programming, Solid oxide
fuel cell, SOFC stack, Dynamic model, Transient
response, Neural network",
abstract = "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.",
}
@Article{chambers:2001:GPEM,
author = "Lance D. Chambers",
title = "Book Review: {Genetic} Programming and Data
Structures: Genetic Programming+Data
Structures=Automatic Programming",
journal = "Genetic Programming and Evolvable Machines",
year = "2001",
volume = "2",
number = "3",
pages = "301--303",
month = sep,
keywords = "genetic algorithms, genetic programming",
ISSN = "1389-2576",
doi = "doi:10.1023/A:1011957528066",
notes = "Review of \cite{langdon:book} Article ID: 357598",
}
@Article{2002ApOpt..41.6260C,
author = "Malik Chami and Denis Robilliard",
title = "Inversion of oceanic constituents in case {I} and {II}
waters with genetic programming algorithms",
year = "2002",
month = oct,
volume = "41",
pages = "6260--6275",
journal = "Applied Optics",
number = "30",
adsurl = "http://adsabs.harvard.edu/cgi-bin/nph-bib_query?bibcode=2002ApOpt..41.6260C&db_key=INST",
adsnote = "Provided by the NASA Astrophysics Data System",
keywords = "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",
URL = "http://ao.osa.org/ViewMedia.cfm?id=70258&seq=0",
size = "16 pages",
abstract = "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.",
notes = "http://adsabs.harvard.edu/cgi-bin/nph-bib_query?bibcode=2002ApOpt..41.6260C&data_type=BIBTEX&db_key=INST%26amp;nocookieset=1",
}
@Article{chan:2007:WR,
author = "Wai Sum Chan and Friedrich Recknagel and Hongqing Cao
and Ho-Dong Park",
title = "Elucidation and short-term forecasting of microcystin
concentrations in Lake Suwa (Japan) by means of
artificial neural networks and evolutionary
algorithms",
journal = "Water Research",
year = "2007",
volume = "41",
number = "10",
pages = "2247--2255",
month = may,
keywords = "genetic algorithms, genetic programming, Lake Suwa,
Microcystis, Microcystin, Ordination, Clustering,
Forecasting, Explanation",
doi = "doi:10.1016/j.watres.2007.02.001",
abstract = "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.",
notes = "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",
}
@InProceedings{chan:1999:MAFEMSGA,
author = "Zeke S. H. Chan and H. W. Ngan and A. B. Rad",
title = "Minimum-Allele-Reserve-Keeper ({MARK}): {A} Fast and
Effective Mutation Scheme for Genetic Algorithm
({GA})",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "106--113",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{chan:1999:AS,
author = "Zeke S. H. Chan and H. W. Ngan and A. B. Rad",
title = "A new method to resist premature convergence:
Synchonising gene-convergence with correlated
recombination",
booktitle = "Late Breaking Papers at the 1999 Genetic and
Evolutionary Computation Conference",
year = "1999",
editor = "Scott Brave and Annie S. Wu",
pages = "74--79",
address = "Orlando, Florida, USA",
month = "13 " # jul,
keywords = "Genetic Algorithms",
notes = "GECCO-99LB",
}
@InCollection{chan:1995:VEWCUGP,
author = "King Choi Chan",
title = "Valid English Word Classifier Using Genetic
Programming",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1995",
year = "1995",
editor = "John R. Koza",
pages = "39--48",
address = "Stanford, California, 94305-3079 USA",
month = "11 " # dec,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-195720-5",
notes = "part of \cite{koza:1995:gagp}",
}
@InCollection{chan:2002:AGPFAGP,
author = "David Michael Chan",
title = "Automatic Generation of Prime Factorization Algorithms
using Genetic Programming",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2002",
year = "2002",
editor = "John R. Koza",
pages = "52--57",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.genetic-programming.org/sp2002/Chan.pdf",
notes = "part of \cite{koza:2002:gagp} {"}GP hard{"} p57",
}
@InProceedings{chan03,
author = "Kit Yan Chan and M. Emin Aydin and Terence C.
Fogarty",
title = "New Factorial Design Theoretic Crossover Operator for
Parametrical Problem",
booktitle = "Genetic Programming, Proceedings of EuroGP'2003",
year = "2003",
editor = "Conor Ryan and Terence Soule and Maarten Keijzer and
Edward Tsang and Riccardo Poli and Ernesto Costa",
volume = "2610",
series = "LNCS",
pages = "22--33",
address = "Essex",
publisher_address = "Berlin",
month = "14-16 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-00971-X",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=22",
abstract = "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.",
notes = "EuroGP'2003 held in conjunction with EvoWorkshops
2003",
}
@InProceedings{chan03b,
author = "Kit Yan Chan and Terence C. Fogarty",
title = "Experimental design based multi-parent crossover
operator",
booktitle = "Genetic Programming, Proceedings of EuroGP'2003",
year = "2003",
editor = "Conor Ryan and Terence Soule and Maarten Keijzer and
Edward Tsang and Riccardo Poli and Ernesto Costa",
volume = "2610",
series = "LNCS",
pages = "297--306",
address = "Essex",
publisher_address = "Berlin",
month = "14-16 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-00971-X",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=297",
abstract = "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.",
notes = "EuroGP'2003 held in conjunction with EvoWorkshops
2003",
}
@InProceedings{chan:2004:eurogp,
author = "Kit Yan Chan and Terence C. Fogarty",
title = "An Evolutionary Algorithm for the Input-Output Block
Assignment Problem",
booktitle = "Genetic Programming 7th European Conference, EuroGP
2004, Proceedings",
year = "2004",
editor = "Maarten Keijzer and Una-May O'Reilly and Simon M.
Lucas and Ernesto Costa and Terence Soule",
volume = "3003",
series = "LNCS",
pages = "250--258",
address = "Coimbra, Portugal",
publisher_address = "Berlin",
month = "5-7 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-21346-5",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=250",
abstract = "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.",
notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in
conjunction with EvoCOP2004 and EvoWorkshops2004",
}
@Article{Chan:2009:JED,
author = "Kit Yan Chan and C. K. Kwong and T. C. Wong",
title = "Modelling customer satisfaction for product
development using genetic programming",
journal = "Journal of Engineering Design",
year = "2009",
volume = "22",
number = "1",
pages = "55--68",
publisher = "Taylor \& Francis",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.informaworld.com/smpp/title~content=t713429619",
doi = "doi:10.1080/09544820902911374",
abstract = "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.",
notes = "Matlab a Department of Industrial and Systems
Engineering, The Hong Kong Polytechnic University,
Kowloon, Hong Kong",
}
@Article{Chan2010506,
author = "K. Y. Chan and C. K. Kwong and T. C. Fogarty",
title = "Modeling manufacturing processes using a genetic
programming-based fuzzy regression with detection of
outliers",
journal = "Information Sciences",
volume = "180",
number = "4",
pages = "506--518",
year = "2010",
ISSN = "0020-0255",
doi = "doi:10.1016/j.ins.2009.10.007",
URL = "http://www.sciencedirect.com/science/article/B6V0C-4XFPR3M-3/2/1f27ff77e40dc7d917de59d3555abf36",
keywords = "genetic algorithms, genetic programming, Fuzzy
regression, Outlier detection, Epoxy dispensing
process",
abstract = "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.",
}
@InProceedings{Chan:2010:ieee-fuzz,
author = "K. Y. Chan and T. S. Dillon and C. K. Kwong",
title = "Using an evolutionary fuzzy regression for affective
product design",
booktitle = "IEEE International Conference on Fuzzy Systems
(FUZZ-IEEE 2010)",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-6920-8",
abstract = "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.",
doi = "doi:10.1109/FUZZY.2010.5584493",
notes = "WCCI 2010. Also known as \cite{5584493}",
}
@InProceedings{Chan:2010:cec,
author = "Kit Yan Chan and Sing Ho Ling and Tharam Singh Dillon
and Hung Nguyen",
title = "Classification of hypoglycemic episodes for Type 1
diabetes mellitus based on neural networks",
booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-6910-9",
abstract = "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.",
doi = "doi:10.1109/CEC.2010.5586320",
notes = "WCCI 2010. Also known as \cite{5586320}",
}
@InProceedings{Chan:2010:cec2,
author = "Kit Yan Chan and Tharam Singh Dillon and Che Kit
Kwong",
title = "Polynomial modeling for manufacturing processes using
a backward elimination based genetic programming",
booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-6910-9",
abstract = "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.",
doi = "doi:10.1109/CEC.2010.5586309",
notes = "WCCI 2010. Also known as \cite{5586309}",
}
@Article{Chan20111623,
author = "Kit Yan Chan and Tharam S. Dillon and C. K. Kwong",
title = "Polynomial modeling for time-varying systems based on
a particle swarm optimization algorithm",
journal = "Information Sciences",
volume = "181",
number = "9",
pages = "1623--1640",
year = "2011",
ISSN = "0020-0255",
doi = "doi:10.1016/j.ins.2011.01.006",
URL = "http://www.sciencedirect.com/science/article/B6V0C-51X1VSV-7/2/12b12f977248967cf70b6cfd1dc37507",
keywords = "genetic algorithms, genetic programming, PSO, Particle
swarm optimisation, Time-varying systems, Polynomial
modelling",
abstract = "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.",
}
@Article{Chan20111648,
author = "K. Y. Chan and C. K. Kwong and T. S. Dillon and Y. C.
Tsim",
title = "Reducing overfitting in manufacturing process modeling
using a backward elimination based genetic
programming",
journal = "Applied Soft Computing",
volume = "11",
number = "2",
pages = "1648--1656",
year = "2011",
note = "The Impact of Soft Computing for the Progress of
Artificial Intelligence",
ISSN = "1568-4946",
doi = "doi:10.1016/j.asoc.2010.04.022",
URL = "http://www.sciencedirect.com/science/article/B6W86-501FPF7-6/2/4bf5179fccc0bf3772b121aef439e062",
keywords = "genetic algorithms, genetic programming, Process
modelling, Polynomial modelling, Overfitting",
abstract = "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.",
}
@Article{Chan20119799,
author = "K. Y. Chan and S. H. Ling and T. S. Dillon and H. T.
Nguyen",
title = "Diagnosis of hypoglycemic episodes using a neural
network based rule discovery system",
journal = "Expert Systems with Applications",
volume = "38",
number = "8",
pages = "9799--9808",
year = "2011",
ISSN = "0957-4174",
doi = "doi:10.1016/j.eswa.2011.02.020",
URL = "http://www.sciencedirect.com/science/article/B6V03-524WF2N-4/2/d9f5c30581fa33cc25387714abbbc4b6",
keywords = "genetic algorithms, genetic programming, Neural
networks, Hypoglycemic episodes, Medical diagnosis,
Type 1 diabetes mellitus",
abstract = "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.",
}
@Article{billchang:2004:GPEM,
author = "Bill C. H. Chang and Asanga Ratnaweera and Saman K.
Halgamuge and Harry C. Watson",
title = "Particle Swarm Optimisation for Protein Motif
Discovery",
journal = "Genetic Programming and Evolvable Machines",
year = "2004",
volume = "5",
number = "2",
pages = "203--214",
month = jun,
keywords = "PSO, particle swarm optimisation, protein sequence
motif, motif discovery, symbolic data optimisation,
HPSO-TVAC",
ISSN = "1389-2576",
doi = "doi:10.1023/B:GENP.0000023688.42515.92",
abstract = "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.",
notes = "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",
}
@MastersThesis{Chia-Lan.Chang:masters,
author = "Chia-Lan Chang",
title = "Dynamic Proportion Portfolio Insurance with Genetic
Programming and Market Volatility Factors Analysis",
school = "National Central University, Jungli",
year = "2005",
address = "Taiwan",
month = "30 " # jun,
keywords = "genetic algorithms, genetic programming, DPPI, CPPI,
market volatility, principal component analysis, PCA",
URL = "http://thesis.lib.ncu.edu.tw/ETD-db/ETD-search-c/getfile?urn=92423002&filename=92423002.pdf",
URL = "http://thesis.lib.ncu.edu.tw/ETD-db/ETD-search-c/view_etd_e?URN=92423002",
size = "45 pages",
abstract = "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.",
}
@InProceedings{Chang:2010:ICEBE,
author = "Hsueh-Hsien Chang and Ching-Lung Lin",
title = "A New Method for Load Identification of Nonintrusive
Energy Management System in Smart Home",
booktitle = "2010 IEEE 7th International Conference on e-Business
Engineering (ICEBE)",
year = "2010",
month = "10-12 " # nov,
pages = "351--357",
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/ICEBE.2010.24",
notes = "Also known as \cite{5704339}",
}
@InProceedings{Chang:2008:ICNC,
author = "Jia-Ruey Chang and Shun-Hsing Chen and Dar-Hao Chen
and Yao-Bin Liu",
title = "Rutting Prediction Model Developed by Genetic
Programming Method Through Full Scale Accelerated
Pavement Testing",
booktitle = "Fourth International Conference on Natural
Computation, ICNC '08",
year = "2008",
month = oct,
volume = "6",
pages = "326--330",
keywords = "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",
doi = "doi:10.1109/ICNC.2008.673",
abstract = "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.",
notes = "Discipulus Also known as \cite{4667854}",
}
@InProceedings{Chang:2010:ICNC,
author = "Jia-Ruey Chang and Sao-Jeng Chao",
title = "Pavement maintenance and rehabilitation decisions
derived by genetic programming",
booktitle = "Sixth International Conference on Natural Computation
(ICNC), 2010",
year = "2010",
month = "10-12 " # aug,
volume = "5",
pages = "2439--2443",
address = "Yantai, Shandong, China",
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/ICNC.2010.5583502",
notes = "Dept. of Civil Eng. & Environ. Inf., MingHsin Univ. of
Sci. & Technol., Hsinchu, Taiwan Also known as
\cite{5583502}",
}
@Article{Chang:2006:mej,
author = "Shoou-Jinn Chang and Hao-Sheng Hou and Yan-Kuin Su",
title = "Automated synthesis of passive filter circuits
including parasitic effects by genetic programming",
journal = "Microelectronics Journal",
year = "2006",
volume = "37",
number = "8",
pages = "792--799",
month = aug,
keywords = "genetic algorithms, genetic programming, Parasitic
effects, Passive filter synthesis",
doi = "doi:10.1016/j.mejo.2005.12.012",
abstract = "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.",
}
@Article{Chang:2005:FGCS,
author = "Yun Seok Chang and Kwang Suk Park and Bo Yeon Kim",
title = "Nonlinear model for {ECG} {R}-{R} interval variation
using genetic programming approach",
journal = "Future Generation Computer Systems",
year = "2005",
volume = "21",
pages = "1117--1123",
number = "7",
abstract = "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.",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6V06-4CVX0RT-1/2/111fea795562435e39023c448749d96a",
month = jul,
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1016/j.future.2004.03.011",
}
@Article{CHS06,
title = "Automated passive filter synthesis using a novel tree
representation and genetic programming",
author = "Shoou-Jinn Chang and Hao-Sheng Hou and Yan-Kuin Su",
journal = "IEEE Transactions on Evolutionary Computation",
volume = "10",
number = "1",
month = feb,
year = "2006",
pages = "93--100",
keywords = "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",
ISSN = "1089-778X",
doi = "doi:10.1109/TEVC.2005.861415",
abstract = "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.",
notes = "INSPEC Accession Number:8753451
Dept. of Electr. Eng., Nat. Cheng Kung Univ., Tainan,
Taiwan",
}
@MastersThesis{Channon:masters,
author = "Alastair D. Channon",
title = "The Evolutionary Emergence route to Artificial
Intelligence",
school = "School of Cognitive and Computing Sciences, University
of Sussex",
year = "1996",
address = "UK",
keywords = "genetic algorithms, genetic programming, Artificial
Intelligence, Emergence, Artificial Life, Neural
Networks, Development, Modularity, Fractals,
Lindenmayer Systems, Recurrence",
URL = "http://www.channon.net/alastair/msc/adc_msc.pdf",
size = "30 pages",
abstract = "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.",
}
@Unpublished{ChaDam97,
author = "A. D. Channon and R. I. Damper",
title = "The Artificial Evolution of Real Intelligence by
Natural Selection",
note = "Published on the web site of and poster presented at
the Fourth European Conference on Artificial Life
(ECAL97), Brighton",
year = "1997",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{ALIFE98*384,
author = "A. D. Channon and R. I. Damper",
title = "Evolving Novel Behaviors via Natural Selection",
booktitle = "Proceedings of the 6th International Conference on
Artificial Life ({ALIFE}-98)",
editor = "Christoph Adami and Richard K. Belew and Hiroaki
Kitano and Charles Taylor",
month = jun # "~27--29",
year = "1998",
pages = "384--388",
publisher = "MIT Press",
keywords = "genetic algorithms, genetic programming, natural
selection",
URL = "http://www.channon.net/alastair/geb/alife6/channon_ad_alife6.pdf",
ISBN = "0-262-51099-5",
address = "Cambridge, MA, USA",
}
@InProceedings{Channon_sab98,
author = "A. D. Channon and R. I. Damper",
title = "Perpetuating evolutionary emergence",
booktitle = "From Animals to Animats 5: Proceedings of the Fifth
International Conference on Simulation of Adaptive
Behavior",
year = "1998",
editor = "Rolf Pfeifer and Bruce Blumberg and Jean-Arcady Meyer
and Stewart W. Wilson",
pages = "534--539",
address = "Zurich, Switzerland",
month = aug # " 17-21",
publisher = "MIT Press",
keywords = "genetic algorithms, genetic programming, natural
selection",
ISBN = "0-262-66144-6",
URL = "http://www.channon.net/alastair/geb/sab98/channon_ad_sab98_nc.pdf",
notes = "http://www.isab.org.uk/confs/sab98.php included in
google books May 2008",
}
@Article{ChaDam00,
author = "A. D. Channon and R. I. Damper",
title = "Towards the evolutionary emergence of increasingly
complex advantageous behaviours",
journal = "International Journal of Systems Science",
year = "2000",
volume = "31",
number = "7",
pages = "843--860",
note = "Special issue on Emergent Properties of Complex
Systems",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.channon.net/alastair/geb/ijssepcs/channon_ad_ijssepcs.pdf",
}
@PhdThesis{channon_ad_phdthesis,
author = "Alastair Channon",
title = "Evolutionary Emergence: The Struggle for Existence in
Artificial Biota",
school = "University of Southampton",
year = "2001",
keywords = "genetic algorithms, genetic programming, natural
selection",
URL = "http://www.channon.net/alastair/geb/phdthesis/channon_ad_phdthesis.pdf",
address = "UK",
month = nov,
size = "111 pages",
abstract = "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.",
}
@InProceedings{Channon:2001:PAT,
author = "Alastair Channon",
title = "Passing the {ALife} Test: Activity Statistics Classify
Evolution in {Geb} as Unbounded",
booktitle = "Advances in Artificial Life: Proceedings of the Sixth
European Conference on Artificial Life (ECAL2001)",
series = "Lecture Notes in Computer Science",
editor = "Jozef Kelemen and Petr Sosik",
volume = "2159",
pages = "417--426",
year = "2001",
keywords = "genetic algorithms, genetic programming, natural
selection",
publisher = "Springer-Verlag",
CODEN = "LNCSD9",
ISSN = "0302-9743",
bibdate = "Sat Feb 2 13:06:02 MST 2002",
bibsource = "http://link.springer-ny.com/link/service/series/0558/tocs/t2159.htm",
URL = "http://www.channon.net/alastair/geb/ecal2001/channon_ad_ecal2001.pdf",
URL = "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",
acknowledgement = "Nelson H. F. Beebe, Center for Scientific
Computing, University of Utah, Department of
Mathematics, 110 LCB, 155 S 1400 E RM 233, Salt Lake
City, UT 84112-0090, USA, Tel: +1 801 581 5254, FAX: +1
801 581 4148, e-mail: \path|beebe@math.utah.edu|,
\path|beebe@acm.org|, \path|beebe@computer.org|,
\path|beebe@ieee.org| (Internet), URL:
\path|http://www.math.utah.edu/~beebe/|",
}
@InProceedings{Channon:2002:alife,
author = "Alastair Channon",
title = "Improving and still passing the {ALife} test:
Component-normalised activity statistics classify
evolution in {Geb} as unbounded",
pages = "173--181",
booktitle = "Proceedings of Artificial Life VIII, the 8th
International Conference on the Simulation and
Synthesis of Living Systems",
year = "2002",
editor = "Russell K. Standish and Mark A. Bedau and Hussein A.
Abbass",
address = "University of New South Wales, Sydney, NSW,
Australia",
publisher_address = "Cambridge, MA, USA",
month = "9th-13th " # dec,
publisher = "The MIT Press",
keywords = "genetic algorithms, genetic programming, natural
selection",
URL = "http://www.channon.net/alastair/geb/alife8/channon_ad_alife8.pdf",
URL = "http://www.alife.org/alife8/proceedings/sub2118.pdf",
size = "10 pages",
abstract = "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.",
notes = "Author claims this is a GP but {"}genetic
programming{"} appears nowhere in it",
}
@Article{Channon:2006:GPEM,
author = "Alastair Channon",
title = "Unbounded evolutionary dynamics in a system of agents
that actively process and transform their environment",
journal = "Genetic Programming and Evolvable Machines",
year = "2006",
volume = "7",
number = "3",
pages = "253--281",
month = oct,
keywords = "artificial life, Evolutionary dynamics, Variable-size
genomes, Coevolution, Biotic selection, Emergence",
ISSN = "1389-2576",
URL = "http://www.channon.net/alastair/papers/channon_ad_gpem.pdf",
doi = "doi:10.1007/s10710-006-9009-3",
abstract = "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.",
}
@Article{chao:2003:GPEM,
author = "Dennis L. Chao and Stephanie Forrest",
title = "Information Immune Systems",
journal = "Genetic Programming and Evolvable Machines",
year = "2003",
volume = "4",
number = "4",
pages = "311--331",
month = dec,
keywords = "artificial immune systems, collaborative design,
collaborative filtering, evolutionary art, information
filtering, biomorphs, sonomorphs, muzak",
ISSN = "1389-2576",
doi = "doi:10.1023/A:1026139027539",
abstract = "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.",
notes = "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
",
}
@InProceedings{Chapelle:2000:isr,
author = "Frederic Chapelle and O. Chocron and Philippe Bidaud",
title = "Genetic programming for inverse kinematics
approximation",
booktitle = "International Symposium on Robotics (ISR'00)",
organization = "International Federation of Robotics",
address = "Montreal, Canada",
pages = "5--11",
month = "14-17 " # may,
year = "2000",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{Chapelle:2000:jjcr,
author = "Frederic Chapelle and G. Dumont and O. Chocron",
title = "Prototypage virtuel de micro-endoscopes par
algorithmes evolutionnaires",
booktitle = "Journees Jeunes Chercheurs en Robotique (JJCR 13)",
address = "Rennes, France",
month = sep,
note = "in french",
size = "12 pages",
keywords = "genetic algorithms",
URL = "http://www.irisa.fr/manifestations/2000/jjcr/Papiers/chapelle.pdf",
year = "2000",
}
@InProceedings{Chapelle:2001:icra,
author = "Frederic Chapelle and Philippe Bidaud",
title = "A closed form for inverse kinematics approximation of
general {6R} manipulators using genetic programming",
booktitle = "IEEE International Conference on Robotics and
Automation (ICRA'01)",
publisher = "IEEE",
address = "Seoul, Korea",
pages = "3364--3369",
month = "21-28 " # may,
year = "2001",
volume = "4",
keywords = "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",
ISSN = "1050-4729",
doi = "doi:10.1109/ROBOT.2001.933137",
size = "6 pages",
abstract = "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.",
notes = "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. \cite{weinbrenner:1997:diploma}",
}
@PhdThesis{Chapelle:2002:thesis,
author = "Frederic Chapelle",
title = "Evaluation de systemes robotiques et comportements
complexes par algorithmes evolutionnaires",
school = "University Pierre et Marie Curie, Paris VI",
month = sep,
year = "2002",
address = "France",
note = "in french",
keywords = "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",
abstract = "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.",
}
@InProceedings{Chapelle:2002:jrtpm,
author = "Frederic Chapelle and Philippe Bidaud and G. Dumont",
title = "Conception et evaluation de micro-endoscopes basees
sur les algorithmes evolutionnaires",
booktitle = "Journees du Reseau Thematique Pluri-disciplinaire
Micro-robotique CNRS",
address = "Rennes, France",
month = nov,
note = "in french",
size = "6 pages",
keywords = "genetic algorithms, genetic programming",
year = "2002",
}
@Article{Chapelle:2004:MMT,
author = "Frederic Chapelle and Philippe Bidaud",
title = "Closed form solutions for inverse kinematics
approximation of general {6R} manipulators",
journal = "Mechanism and Machine Theory",
year = "2004",
month = mar,
volume = "39",
pages = "323--338",
number = "3",
abstract = "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.",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6V46-4B1XNXT-1/2/2bf40af1f930c87f19d6fcc130f2f57a",
keywords = "genetic algorithms, genetic programming, Inverse
kinematics, Mechanical design, Manipulators, Genetic
programming, Symbolic regression",
doi = "doi:10.1016/j.mechmachtheory.2003.09.003",
}
@Article{Chapelle:2006:MMT,
author = "Frederic Chapelle and Philippe Bidaud",
title = "Evaluation functions synthesis for optimal design of
hyper-redundant robotic systems",
journal = "Mechanism and Machine Theory",
year = "2006",
volume = "41",
number = "10",
pages = "1196--1212",
month = oct,
keywords = "genetic algorithms, genetic programming, Mechanical
design, Simultaneous structure/control evaluation,
Functions synthesis, Hyper-redundant micro-robotics,
Minimally invasive surgery",
doi = "doi:10.1016/j.mechmachtheory.2005.11.006",
abstract = "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.",
}
@InProceedings{char:1997:caiGP,
author = "K. Govinda Char",
title = "Constructivist {AI} with {GP}",
booktitle = "Late Breaking Papers at the 1997 Genetic Programming
Conference",
year = "1997",
editor = "John R. Koza",
pages = "28--34",
address = "Stanford University, CA, USA",
publisher_address = "Stanford University, Stanford, California,
94305-3079, USA",
month = "13--16 " # jul,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-206995-8",
notes = "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",
}
@InProceedings{char:1997:elGPcAI,
author = "K. Govinda Char",
title = "Evolution of Learning with Genetic Programming -
Constructivist {AI} with Genetic Programming",
booktitle = "Late Breaking Papers at the 1997 Genetic Programming
Conference",
year = "1997",
editor = "John R. Koza",
pages = "289",
address = "Stanford University, CA, USA",
publisher_address = "Stanford University, Stanford, California,
94305-3079, USA",
month = "13--16 " # jul,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-206995-8",
broken = "http://www.elec.gla.ac.uk/~kchar/gp97.ps",
notes = "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",
}
@InCollection{Char:1997:HEC,
author = "K. Govinda Char and Walter Alden Tackett",
title = "Pattern recognition",
booktitle = "Handbook of Evolutionary Computation",
publisher = "Oxford University Press",
publisher_2 = "Institute of Physics Publishing",
year = "1997",
editor = "Thomas Baeck and David B. Fogel and Zbigniew
Michalewicz",
chapter = "section F1.6.2.5",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7503-0392-1",
URL = "http://www.crcnetbase.com/isbn/9780750308953",
}
@InProceedings{char:1998:clGP,
author = "K. Govinda Char",
title = "Constructive Learning with Genetic Programming",
booktitle = "Late Breaking Papers at EuroGP'98: the First European
Workshop on Genetic Programming",
year = "1998",
editor = "Riccardo Poli and W. B. Langdon and Marc Schoenauer
and Terry Fogarty and Wolfgang Banzhaf",
pages = "1--5",
address = "Paris, France",
publisher_address = "School of Computer Science",
month = "14-15 " # apr,
publisher = "CSRP-98-10, The University of Birmingham, UK",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/csrp-98-10.pdf",
URL = "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1998/CSRP-98-10.ps.gz",
size = "5 pages",
notes = "EuroGP'98LB part of \cite{Poli:1998:egplb}",
}
@PhdThesis{char:thesis,
author = "K. Govinda Char",
title = "Constructivist {AI} with Genetic Programming",
school = "Department of Electronics and Electrical Engineering,
University of Glasgow",
year = "1998",
address = "Rankine Building, Oakfield Avenue, Glasgow G12 8LT,
Scotland, UK",
keywords = "genetic algorithms, genetic programming",
size = "pages",
}
@Article{Charhate:2007:JEME,
author = "S. B. Charhate and M. C. Deo and V. Sanil Kumar",
title = "Soft and hard computing approaches for real-time
prediction of currents in a tide-dominated coastal
area",
journal = "Proceedings of the Institution of Mechanical
Engineers, Part M: Journal of Engineering for the
Maritime Environment",
year = "2007",
volume = "221",
number = "4",
pages = "147--163",
keywords = "genetic algorithms, genetic programming, tidal
currents, neural networks, harmonic analysis, current
measurements",
ISSN = "1475-0902",
doi = "doi:10.1243/14750902JEME77",
size = "19 pages",
abstract = "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.",
notes = "ARIMA, ANN, GP. Department of Civil Engineering,
Indian Institute of Technology, Bombay",
}
@Article{Charhate2008120,
author = "S. B. Charhate and M. C. Deo and S. N. Londhe",
title = "Inverse modeling to derive wind parameters from wave
measurements",
journal = "Applied Ocean Research",
volume = "30",
number = "2",
pages = "120--129",
year = "2008",
ISSN = "0141-1187",
doi = "doi:10.1016/j.apor.2008.08.002",
URL = "http://www.sciencedirect.com/science/article/B6V1V-4TCGM50-1/2/69dcf477c9fc85235d0cc5df25e6a54a",
keywords = "genetic algorithms, genetic programming, Wave buoy,
Wave data, Wind data, Neural networks",
abstract = "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.",
}
@PhdThesis{Charhate:thesis,
author = "Shrikant Bhauraoji Charhate",
title = "Applications of soft computing techniques to solve
coastal and ocean problems",
school = "Department of Civil Engineering, Indian Institute of
Technology, Bombay",
year = "2008",
address = "India",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.civil.iitb.ac.in/~mcdeo/thesis.html",
notes = "Supervised by Dr. M. C. Deo",
}
@Article{Charhate:2009:SOS,
author = "S. B. Charhate and M. C. Deo and S. N. Londhe",
title = "Genetic programming for real-time prediction of
offshore wind",
journal = "Ships and Offshore Structures",
year = "2009",
volume = "4",
number = "1",
pages = "77--88",
month = mar,
keywords = "genetic algorithms, genetic programming, artificial
neural networks, wind speed, wind direction, wind
prediction",
ISSN = "1744-5302",
doi = "doi:10.1080/17445300802492638",
size = "12 pages",
abstract = "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.",
notes = "Department of Civil Engineering, Indian Institute of
Technology Bombay, Mumbai, India",
}
@Article{chattoe:1998:uEArsp,
author = "Edmund Chattoe",
title = "Just How (Un)realistic are Evolutionary Algorithms as
Representations of Social Processes?",
journal = "The Journal of Artificial Societies and Social
Simulation",
year = "1998",
volume = "1",
number = "3",
month = "30 " # jun,
keywords = "genetic algorithms, genetic programming, evolutionary
algorithms, social evolution, selectionist paradigm",
URL = "http://www.soc.surrey.ac.uk/JASSS/1/3/2.html",
size = "158407 bytes",
abstract = "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.",
notes = "JASSS",
}
@Article{chattoe:2004:gagpf,
author = "Edmund Chattoe",
title = "Genetic Algorithms and Genetic Programming in
Computational Finance, Chen, Shu-Heng (ed.)",
journal = "Journal of Artificial Societies and Social
Simulation",
year = "2004",
volume = "7",
number = "4",
month = "31-" # oct,
note = "Book review",
keywords = "genetic algorithms, genetic programming",
URL = "http://jasss.soc.surrey.ac.uk/7/4/reviews/chattoe.html",
notes = "review of \cite{chen:2002:gagpcf}",
}
@InProceedings{DBLP:conf/icarcv/ChaudhariPT08,
author = "Narendra S. Chaudhari and Anuradha Purohit and Aruna
Tiwari",
title = "A multiclass classifier using Genetic Programming",
booktitle = "10th International Conference on Control, Automation,
Robotics and Vision, ICARCV 2008",
year = "2008",
pages = "1884--1887",
address = "Hanoi, Vietnam",
month = "17-20 " # dec,
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1109/ICARCV.2008.4795815",
abstract = "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.",
bibsource = "DBLP, http://dblp.uni-trier.de",
}
@InProceedings{Chaudhary:2009:INMIC,
author = "U. K. Chaudhary and M. Iqbal",
title = "Determination of optimum genetic parameters for
symbolic non-linear regression-like problems in genetic
programming",
booktitle = "IEEE 13th International Multitopic Conference, INMIC
2009",
year = "2009",
month = dec,
pages = "1--5",
keywords = "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",
doi = "doi:10.1109/INMIC.2009.5383162",
abstract = "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.",
notes = "Also known as \cite{5383162}",
}
@InProceedings{Chaudhri:2000:GECCO,
author = "Omer A. Chaudhri and Jason M. Daida and Jonathan C.
Khoo and Wendell S. Richardson and Rachel B. Harrison
and William J. Sloat",
title = "Characterizing a Tunably Difficult Problem in Genetic
Programming",
pages = "395--402",
year = "2000",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2000)",
editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz
and Lee Spector and Ian Parmee and Hans-Georg Beyer",
address = "Las Vegas, Nevada, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "10-12 " # jul,
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-708-0",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/GP206.pdf",
notes = "A joint meeting of the ninth International Conference
on Genetic Algorithms (ICGA-2000) and the fifth Annual
Genetic Programming Conference (GP-2000) Part of
\cite{whitley:2000:GECCO}",
}
@PhdThesis{Chaudhry:thesis,
author = "Asmatullah Chaudhry",
author2 = "Asmat Ullah",
title = "Image Restoration using Machine Learning",
school = "Ghulam Ishaq Khan Institute of Engineering Sciences \&
Technology",
year = "2007",
address = "Topi, NWFP, Pakistan",
month = mar,
email = "asmatullah.chaudhry@gmail.com",
keywords = "genetic algorithms, genetic programming, Image
Restoration",
URL = "http://prr.hec.gov.pk/thesis/2056.pdf",
size = "112 pages",
abstract = "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.",
notes = "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",
}
@Article{Chaudhry:2007:IJIST,
author = "Asmatullah Chaudhry and Asifullah Khan and Asad Ali
and Anwar M. Mirza",
title = "A hybrid image restoration approach: Using fuzzy
punctual kriging and genetic programming",
journal = "International Journal of Imaging Systems and
Technology",
year = "2007",
volume = "17",
number = "4",
pages = "224--231",
keywords = "genetic algorithms, genetic programming, image
restoration, fuzzy logic, punctual kriging, structure
similarity index measure, SSIM, adaptive spatial
filtering",
ISSN = "1098-1098",
doi = "doi:10.1002/ima.20105",
abstract = "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.",
}
@Article{Chaudhry:2009:murjet,
author = "Asmatullah Chaudhry and Anwar M. Mirza and Nisar Ahmed
Memon",
title = "Fusion of Linear and Non-Linear Image Restoration
Filters Using Genetic Programming",
journal = "Mehran university Research Journal of Engineering and
Technology",
year = "2009",
volume = "28",
number = "4",
pages = "429--436",
month = oct,
publisher = "Mehran University of Engineering and Technology",
address = "Pakistan",
email = "asmatullah.chaudhry@gmail.com",
keywords = "genetic algorithms, genetic programming, Image
restoration, E-median filter, Adaptive Wiener filter
(AWF)",
ISSN = "0254-7821",
searchurl = "http://direct.bl.uk/bld/OrderDetails.do?",
abstract = "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.",
notes = "Unique item number RN257688172 Shelfmark 5536.314400",
}
@InProceedings{chavez:2007:MAEB,
author = "F. Chavez and J. L. Guisado and D. Lombrana and F.
Fernandez",
title = "Una Herramienta de Programacion Genetica Paralela que
Aprovecha Recursos Publicos de Computacion",
booktitle = "MAEB'2007, V Congreso Espanol sobre Metaheuristicas,
Algoritmos Evolutivos y Bioinspirados",
year = "2007",
address = "Puerto de la Cruz, Spain",
keywords = "genetic algorithms, genetic programming, Palabras
clave, Algoritmos Paralelos, Programacion Genetica",
URL = "http://icaro.eii.us.es/~jlguisado/publicaciones/MAEB2007_preprint.pdf",
size = "7 pages",
abstract = "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.",
notes = "in Spanish, BOINC",
}
@InProceedings{cheang2:2003:gecco,
author = "Sin Man Cheang and Kin Hong Lee and Kwong Sak Leung",
title = "Data Classification Using Genetic Parallel
Programming",
booktitle = "Genetic and Evolutionary Computation -- GECCO-2003",
editor = "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",
year = "2003",
pages = "1918--1919",
address = "Chicago",
publisher_address = "Berlin",
month = "12-16 " # jul,
volume = "2724",
series = "LNCS",
ISBN = "3-540-40603-4",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, Learning
Classifier Systems, poster",
abstract = "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.",
notes = "GECCO-2003. A joint meeting of the twelfth
International Conference on Genetic Algorithms
(ICGA-2003) and the eighth Annual Genetic Programming
Conference (GP-2003)",
}
@InProceedings{cheang:2003:gecco,
author = "Sin Man Cheang and Kin Hong Lee and Kwong Sak Leung",
title = "Improving Evolvability of Genetic Parallel Programming
Using Dynamic Sample Weighting",
booktitle = "Genetic and Evolutionary Computation -- GECCO-2003",
editor = "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",
year = "2003",
pages = "1802--1803",
address = "Chicago",
publisher_address = "Berlin",
month = "12-16 " # jul,
volume = "2724",
series = "LNCS",
ISBN = "3-540-40603-4",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, poster",
abstract = "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.",
notes = "GECCO-2003. A joint meeting of the twelfth
International Conference on Genetic Algorithms
(ICGA-2003) and the eighth Annual Genetic Programming
Conference (GP-2003)",
}
@InProceedings{cheang:gecco03lbp,
title = "An Empirical Study of the Accelerating Phenomenon in
Genetic Parallel Programming",
pages = "54--61",
author = "Sin Man Cheang and Kin Hong Lee and Kwong Sak Leung",
year = "2003",
address = "Chicago, USA",
month = "12--16 " # jul,
editor = "Bart Rylander",
keywords = "genetic algorithms, genetic programming",
booktitle = "Genetic and Evolutionary Computation Conference Late
Breaking Papers",
notes = "GECCO-2003LB",
}
@InProceedings{cheang:2003:edcpugpp,
author = "Sin Man Cheang and Kin Hong Lee and Kwong Sak Leung",
title = "Evolving data classification programs using genetic
parallel programming",
booktitle = "Proceedings of the 2003 Congress on Evolutionary
Computation CEC2003",
editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass
and Kay Chen Tan and Bob McKay and Daryl Essam and Tom
Gedeon",
pages = "248--255",
year = "2003",
publisher = "IEEE Press",
address = "Canberra",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "8-12 " # dec,
organisation = "IEEE Neural Network Council (NNC), Engineers Australia
(IEAust), Evolutionary Programming Society (EPS),
Institution of Electrical Engineers (IEE)",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-7804-0",
abstract = "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.",
notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust,
the EPS, and the IEE.",
}
@InProceedings{Man:2003:Aswmtgpp,
author = "Sin Man Cheang and Kin Hong Lee and Kwong Sak Leung",
title = "Applying sample weighting methods to genetic parallel
programming",
booktitle = "Proceedings of the 2003 Congress on Evolutionary
Computation CEC2003",
editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass
and Kay Chen Tan and Bob McKay and Daryl Essam and Tom
Gedeon",
pages = "928--935",
year = "2003",
publisher = "IEEE Press",
address = "Canberra",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "8-12 " # dec,
organisation = "IEEE Neural Network Council (NNC), Engineers Australia
(IEAust), Evolutionary Programming Society (EPS),
Institution of Electrical Engineers (IEE)",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-7804-0",
notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust,
the EPS, and the IEE.",
abstract = "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.",
}
@InProceedings{cheang:2003:CIRAS,
author = "Sin Man Cheang",
title = "An Empirical Study of the {GPP} Accelerating
Phenomenon",
booktitle = "Proceedings of the second International Conference on
Computational Intelligence, Robotics and Autonomous
Systems -- CIRAS-2003",
year = "2003",
editor = "P. Vadakkepat and T. W. Wan and T. K. Chen and L. A.
Poh",
pages = "PS04--4--03",
address = "Singapore",
month = "15-18 " # dec,
organisation = "Centre for Intelligent Control, National Univ. of
Singapore",
publisher = "National Univ. of Singapore",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
}
@InProceedings{cheang:2004:eurogp,
author = "Sin Man Cheang and Kin Hong Lee and Kwong Sak Leung",
title = "Designing Optimal Combinational Digital Circuits Using
a Multiple Logic Unit Processor",
booktitle = "Genetic Programming 7th European Conference, EuroGP
2004, Proceedings",
year = "2004",
editor = "Maarten Keijzer and Una-May O'Reilly and Simon M.
Lucas and Ernesto Costa and Terence Soule",
volume = "3003",
series = "LNCS",
pages = "23--34",
address = "Coimbra, Portugal",
publisher_address = "Berlin",
month = "5-7 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-21346-5",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=23",
abstract = "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.",
notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in
conjunction with EvoCOP2004 and EvoWorkshops2004",
}
@Article{Cheang:2006:EC,
author = "Sin Man Cheang and Kwong Sak Leung and Kin Hong Lee",
title = "Genetic Parallel Programming: Design and
Implementation",
journal = "Evolutionary Computation",
year = "2006",
volume = "14",
number = "2",
pages = "129--156",
month = "Summer",
keywords = "genetic algorithms, genetic programming, linear
genetic programming, parallel processor architecture,
MIMD, ALU MAP, GPP",
ISSN = "1063-6560",
doi = "doi:10.1162/evco.2006.14.2.129",
size = "28 pages",
abstract = "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.",
}
@Article{Cheang:2007:tec,
author = "Sin Man Cheang and Kin Hong Lee and Kwong Sak Leung",
title = "Applying Genetic Parallel Programming to Synthesize
Combinational Logic Circuits",
journal = "IEEE Transactions on Evolutionary Computation",
year = "2007",
volume = "11",
number = "4",
pages = "503--520",
month = aug,
keywords = "genetic algorithms, genetic programming, FPGA, Circuit
design, digital circuits, evolvable hardware, parallel
programming",
ISSN = "1389-2576",
doi = "doi:10.1109/TEVC.2006.884044",
size = "18 pages",
abstract = "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.",
}
@Article{cheema:2002:BTP,
author = "Jitender Jit Singh Cheema and Narendra V. Sankpal and
Sanjeev S. Tambe and Bhaskar D. Kulkarni",
title = "Genetic Programming Assisted Stochastic Optimization
Strategies for Optimization of Glucose to Gluconic Acid
Fermentation",
journal = "Biotechnology Progress",
year = "2002",
volume = "18",
number = "6",
pages = "1356--1365",
keywords = "genetic algorithms, genetic programming",
ISSN = "8756-7938",
URL = "http://www3.interscience.wiley.com/journal/121399381/abstract",
doi = "doi:10.1021/bp015509s",
abstract = "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.",
notes = "
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",
}
@InProceedings{Chellapilla:1997:eptm,
author = "Kumar Chellapilla",
title = "Evolutionary Programming with Tree Mutations: Evolving
Computer Programs without Crossover",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "evolutionary programming and evolution strategies",
pages = "431--438",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
notes = "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",
}
@InProceedings{chellapilla:1998:enlbtatuEP,
author = "Kumar Chellapilla",
title = "Evolving Nonlinear Controllers for Backing up a
Truck-and-Trialer Using Evolutionary Programming",
booktitle = "Evolutionary Programming VII: Proceedings of the
Seventh Annual Conference on Evolutionary Programming",
year = "1998",
editor = "V. William Porto and N. Saravanan and D. Waagen and A.
E. Eiben",
volume = "1447",
series = "LNCS",
pages = "417--426",
address = "Mission Valley Marriott, San Diego, California, USA",
publisher_address = "Berlin",
month = "25-27 " # mar,
publisher = "Springer-Verlag",
keywords = "evolutionary programming",
ISBN = "3-540-64891-7",
doi = "doi:10.1007/BFb0040753",
notes = "EP-98.
",
}
@InProceedings{chellapilla:1998:agnoclbbEP,
author = "Kumar Chellapilla",
title = "Automatic Generation of Nonlinear Optimal Control Laws
for Broom Balancing using Evolutionary Programming",
booktitle = "Proceedings of the 1998 IEEE World Congress on
Computational Intelligence",
year = "1998",
pages = "195--200",
address = "Anchorage, Alaska, USA",
month = "5-9 " # may,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-4869-9",
file = "c034.pdf",
size = "6 pages",
abstract = "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.",
notes = "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
World Congress on Computational Intelligence.
Comparison with \cite{koza:book} results",
}
@InProceedings{chellapilla:1998:piempwsx,
author = "Kumar Chellapilla",
title = "A Preliminary Investigation into Evolving Modular
Programs without Subtree Crossover",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "23--31",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-548-7",
notes = "GP-98",
}
@InProceedings{chellapilla:1998:elsoEP,
author = "Kumar Chellapilla and Hemanth Birru and Rao
Sathyanarayan",
title = "Effectivenss of Local Search Operators in Evolutionary
Programming",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "753--761",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "evolutionary programming",
ISBN = "1-55860-548-7",
notes = "GP-98",
}
@Article{Chellapilla:1998:eptm,
author = "Kumar Chellapilla",
title = "Evolving Computer Programs without Subtree Crossover",
journal = "IEEE Transactions on Evolutionary Computation",
year = "1997",
volume = "1",
number = "3",
pages = "209--216",
month = sep,
keywords = "genetic algorithms, genetic programming, symbolic
expressions, Evolutionary Programming, variation
operators",
ISSN = "1089-778X",
doi = "doi:10.1109/4235.661552",
abstract = "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",
notes = "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",
}
@InProceedings{Chen:2009:CINC,
author = "Bing-Rui Chen and Xia-Ting Feng and Cheng-Xiang Yang",
title = "A Self-adapting Algorithm for Identifying Rheology
Model and Its Parameters of Rock Mass",
booktitle = "International Conference on Computational Intelligence
and Natural Computing, CINC '09",
year = "2009",
month = jun,
volume = "2",
pages = "478--481",
keywords = "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",
doi = "doi:10.1109/CINC.2009.39",
abstract = "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.",
notes = "Also known as \cite{5230917}",
}
@PhdThesis{Carla_Chen_Thesis,
author = "Carla Chia-Ming Chen",
title = "Bayesian methodology for genetics of complex
diseases",
school = "Past, QUT Faculties \& Divisions, Faculty of Science
and Technology, Queensland University of Technology",
year = "2010",
address = "Australia",
keywords = "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",
URL = "http://eprints.qut.edu.au/43357/",
URL = "http://eprints.qut.edu.au/43357/1/Carla_Chen_Thesis.pdf",
size = "291 pages",
abstract = "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.",
notes = "ID Code: 43357 Supervisors: Mengersen, Kerrie and
Keith, Jonathan",
}
@Article{Chen:2011:TCBB,
author = "Carla Chia-Ming Chen and Holger Schwender and Jonathan
Keith and Robin Nunkesser and Kerrie Mengersen and
Paula Macrossan",
title = "Methods for Identifying {SNP} Interactions: {A} Review
on Variations of Logic Regression, Random Forest and
{Bayesian} Logistic Regression",
journal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
year = "2011",
volume = "8",
number = "6",
pages = "1580--1591",
month = nov # "-" # dec,
keywords = "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",
ISSN = "1545-5963",
doi = "doi:10.1109/TCBB.2011.46",
size = "12 pages",
abstract = "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.",
notes = "Also known as \cite{5728791}",
}
@InProceedings{chen:2009:SMC,
author = "Ci Chen and Shingo Mabu and Chuan Yue and Kaoru
Shimada and Kotaro Hirasawa",
title = "Network intrusion detection using fuzzy class
association rule mining based on genetic network
programming",
booktitle = "IEEE International Conference on Systems, Man and
Cybernetics, SMC 2009",
year = "2009",
pages = "60--67",
address = "San Antonio, Texas, USA",
month = oct,
keywords = "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",
doi = "doi:10.1109/ICSMC.2009.5346328",
ISSN = "1062-922X",
abstract = "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.",
notes = "Also known as \cite{5346328}",
}
@PhdThesis{etd-0114108-184337,
author = "Chih-Yung Chen",
title = "The Studies of Artificial Intelligent Technology and
Its Applications",
school = "Graduate School of Electrical Engineering, I-Shou
University",
year = "2007",
address = "Kaohsiung, Taiwan",
month = "8 " # dec,
keywords = "genetic algorithms, EHW, Image Processing, Fuzzy
Control, Neural Network, Evolutionary Hardware,
Artificial Intelligence",
URL = "http://ethesys.isu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0114108-184337",
URL = "http://ethesys.isu.edu.tw/ETD-db/ETD-search/getfile?URN=etd-0114108-184337&filename=etd-0114108-184337.pdf",
size = "115 pages",
abstract = "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.",
}
@Article{Chen2009634,
author = "Chih-Yung Chen and Rey-Chue Hwang",
title = "A new variable topology for evolutionary hardware
design",
journal = "Expert Systems with Applications",
volume = "36",
number = "1",
pages = "634--642",
year = "2009",
ISSN = "0957-4174",
doi = "doi:10.1016/j.eswa.2007.09.017",
URL = "http://www.sciencedirect.com/science/article/B6V03-4PV2RVX-6/2/6aa751f84c76e323ab6ddab36f70e63d",
keywords = "genetic algorithms, genetic programming, evolvable
hardware, Evolutionary hardware design, Slicing
structure, Routing graph",
abstract = "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.",
notes = "EHW, GP, graph based GA",
}
@InProceedings{conf/ausai/ChenZ05,
author = "Guang Chen and Mengjie Zhang",
title = "Evolving While-Loop Structures in Genetic Programming
for Factorial and Ant Problems",
year = "2005",
pages = "1079--1085",
booktitle = "AI 2005: Advances in Artificial Intelligence, 18th
Australian Joint Conference on Artificial Intelligence,
Proceedings",
editor = "Shichao Zhang and Ray Jarvis",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3809",
address = "Sydney, Australia",
month = dec # " 5-9",
keywords = "genetic algorithms, genetic programming, STGP",
ISBN = "3-540-30462-2",
doi = "doi:10.1007/11589990_144",
size = "7 pages",
abstract = "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.",
notes = "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.",
}
@InProceedings{Jiah-ShingChen:2003:CINC,
author = "Jiah-Shing Chen and Ping-Chen Lin",
title = "Multi-Valued Stock Valuation Based on Fuzzy Genetic
Programming Approach",
booktitle = "Procceedings of the Sixth International Conference on
Computational Intelligence and Natural Computing",
year = "2003",
address = "Embassy Suites Hotel and Conference Center, Cary,
North Carolina USA",
month = sep # " 26-30",
keywords = "genetic algorithms, genetic programming",
notes = "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.",
}
@InProceedings{conf/icnc/ChenC05b,
title = "Dynamical Proportion Portfolio Insurance with Genetic
Programming",
author = "Jiah-Shing Chen and Chia-Lan Chang",
year = "2005",
pages = "735--743",
editor = "Lipo Wang and Ke Chen and Yew-Soon Ong",
booktitle = "Advances in Natural Computation, First International
Conference, ICNC 2005, Proceedings, Part II",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3611",
address = "Changsha, China",
month = aug # " 27-29",
bibdate = "2005-08-01",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/icnc/icnc2005-2.html#ChenC05b",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-28325-0",
doi = "doi:10.1007/11539117_104",
abstract = "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.",
}
@Article{Chen:2007:ESA,
author = "J. S. Chen and Benjamin Penyang Liao",
title = "Piecewise nonlinear goal-directed {CPPI} strategy",
journal = "Expert Systems with Applications",
year = "2007",
volume = "33",
number = "4",
pages = "857--869",
month = nov,
keywords = "genetic algorithms, genetic programming, Portfolio
insurance strategy, Goal-directed strategy, Piecewise
linear GDCPPI strategy, Piecewise nonlinear GDCPPI
strategy",
doi = "doi:10.1016/j.eswa.2006.07.001",
abstract = "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.",
}
@Article{Chen2008273,
author = "Jiah-Shing Chen and Chia-Lan Chang and Jia-Li Hou and
Yao-Tang Lin",
title = "Dynamic proportion portfolio insurance using genetic
programming with principal component analysis",
journal = "Expert Systems with Applications",
volume = "35",
number = "1-2",
pages = "273--278",
year = "2008",
ISSN = "0957-4174",
doi = "doi:10.1016/j.eswa.2007.06.030",
URL = "http://www.sciencedirect.com/science/article/B6V03-4P40KHS-4/2/0bbb6228d04a3a1a4d59108b17c37664",
keywords = "genetic algorithms, genetic programming, Dynamic
proportion portfolio insurance (DPPI), Constant
proportion portfolio insurance (CPPI), Principal
component analysis (PCA)",
abstract = "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.",
}
@InProceedings{conf/awic/ChenLW05,
title = "Distributed Service Management Based on Genetic
Programming",
author = "Jing Chen and Zeng-zhi Li and Yun-lan Wang",
year = "2005",
pages = "83--88",
editor = "Piotr S. Szczepaniak and Janusz Kacprzyk and Adam
Niewiadomski",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3528",
ISBN = "3-540-26219-9",
booktitle = "Advances in Web Intelligence Third International
Atlantic Web Intelligence Conference, AWIC 2005,
Proceedings",
address = "Lodz, Poland",
month = "6-9 " # jun,
bibdate = "2005-05-30",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/awic/awic2005.html#ChenLW05",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-26219-9",
doi = "doi:10.1007/11495772_14",
size = "6 pages",
abstract = "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.",
}
@InProceedings{Chen:2005:ICMLC,
author = "Jing Chen and Zeng-Zhi Li and Zhi-Gang Liao and
Yun-Lan Wang",
title = "Distributed Service Performance Management Based on
Linear Regression and Genetic Programming",
booktitle = "Proceedings of 2005 International Conference on
Machine Learning and Cybernetics",
year = "2005",
volume = "1",
pages = "560--563",
month = "18-21 " # aug,
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1109/ICMLC.2005.1527007",
abstract = "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.",
notes = "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",
}
@Article{chen:290,
author = "Li Chen",
title = "Study of Applying Macroevolutionary Genetic
Programming to Concrete Strength Estimation",
publisher = "ASCE",
year = "2003",
journal = "Journal of Computing in Civil Engineering",
volume = "17",
number = "4",
pages = "290--294",
month = oct,
keywords = "genetic algorithms, genetic programming, civil
engineering computing, compressive strength, mixtures,
concrete",
URL = "http://link.aip.org/link/?QCP/17/290/1",
doi = "doi:10.1061/(ASCE)0887-3801(2003)17:4(290)",
abstract = "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.",
notes = "Dept. of Civil Engineering, Chung Hua Univ., Hsin Chu,
Taiwan 30067, Republic of China.",
}
@Article{Chen2008296,
author = "Li Chen and Chih-Hung Tan and Shuh-Ji Kao and
Tai-Sheng Wang",
title = "Improvement of remote monitoring on water quality in a
subtropical reservoir by incorporating grammatical
evolution with parallel genetic algorithms into
satellite imagery",
journal = "Water Research",
volume = "42",
number = "1-2",
pages = "296--306",
year = "2008",
ISSN = "0043-1354",
doi = "doi:10.1016/j.watres.2007.07.014",
URL = "http://www.sciencedirect.com/science/article/B6V73-4P7FS78-1/2/1cc0a607d7b67fe51a5f0d27a2c9d0fc",
keywords = "genetic algorithms, genetic programming, Grammatical
evolution, Parallel genetic algorithm, Water quality
monitoring, Chlorophyll-a, Remote-sensed imagery",
abstract = "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.",
}
@Article{journals/soco/ChenCCHH07,
author = "Mu-Yen Chen and Kuang-Ku Chen and Heien-Kun Chiang and
Hwa-Shan Huang and Mu-Jung Huang",
title = "Comparing extended classifier system and genetic
programming for financial forecasting: an empirical
study",
journal = "Soft Computing",
year = "2007",
volume = "11",
number = "12",
pages = "1173--1183",
keywords = "genetic algorithms, genetic programming, Learning
classifier system, Extended classifier system, Machine
learning",
doi = "doi:10.1007/s00500-007-0161-3",
abstract = "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.",
bibdate = "2008-03-11",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/soco/soco11.html#ChenCCHH07",
}
@InProceedings{WSEAS_466-157_Chen,
author = "Peng Chen and Takashi Isoda and Shinichiro Mitutake
and Shinji Koyama",
title = "Automatic Running Planning for Omni{$\phi$}Directional
Mobile Robot By Genetic Programming",
year = "2003",
month = aug # "~11-13",
pages = "5",
booktitle = "WSEAS SEPAD-AIKED-ISPRA-EHAC",
address = "Rethymno, Greece",
organisation = "The World Scientific and Engineering Academy and
Society (WSEAS)",
keywords = "genetic algorithms, genetic programming",
}
@Article{Chen:2005:MSSP,
author = "Peng Chen and Masatoshi Taniguchi and Toshio Toyota
and Zhengja He",
title = "Fault diagnosis method for machinery in unsteady
operating condition by instantaneous power spectrum and
genetic programming",
journal = "Mechanical Systems and Signal Processing",
year = "2005",
volume = "19",
pages = "175--194",
number = "1",
abstract = "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.",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6WN1-4BKPSGD-2/2/6c68916b11c23706a7fee9f780c0e637",
month = jan,
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1016/j.ymssp.2003.11.004",
}
@InProceedings{chen:2007:CIDM,
author = "Qing-Shan Chen and De-Fu Zhang and Li-Jun Wei and
Huo-Wang Chen",
title = "A Modified Genetic Programming for Behavior Scoring
Problem",
booktitle = "IEEE Symposium on Computational Intelligence and Data
Mining, CIDM 2007",
year = "2007",
pages = "535--539",
address = "Honolulu, HI, USA",
month = mar # " 1-" # apr # " 5",
publisher = "IEEE",
keywords = "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",
ISBN = "1-4244-0705-2",
doi = "doi:10.1109/CIDM.2007.368921",
size = "5 pages",
abstract = "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",
bibdate = "2007-09-13",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/cidm/cidm2007.html#Qing-ShanDLH07",
}
@Article{Chen20102054,
author = "Shih-Huang Chen and Jun-Nan Chen",
title = "Forecasting container throughputs at ports using
genetic programming",
journal = "Expert Systems with Applications",
volume = "37",
number = "3",
pages = "2054--2058",
year = "2010",
ISSN = "0957-4174",
doi = "doi:10.1016/j.eswa.2009.06.054",
URL = "http://www.sciencedirect.com/science/article/B6V03-4WNXTWY-M/2/1a5e0fe084ba3ea36303bd280acecc04",
keywords = "genetic algorithms, genetic programming, Container
throughput, Forecasting",
abstract = "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.",
}
@InProceedings{chen:1995:psmrGP,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "Predicting Stock Returns with Genetic Programming: Do
the Short-Term Nonlinear Regularities Exist?",
booktitle = "Proceedings of the Fifth International Workshop on
Artificial Intelligence and Statistics",
year = "1995",
editor = "Doug Fisher",
pages = "95--101",
address = "Ft. Lauderdale, Florida, U.S.A.",
month = jan # " 4-7",
organisation = "Society for Artificial Intelligence and Statistics",
keywords = "genetic algorithms, genetic programming",
notes = "http://web.archive.org/web/20011127035349/http://www.vuse.vanderbilt.edu/~dfisher/ai-stats/fifth-workshop/contents.html",
}
@InProceedings{chen:1995:cqtm,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "On the Competitiveness of the Quantity Theory of
Money: {A} Natural-Selection Test Based on Genetic
Programming",
booktitle = "11th International Conference on Advanced Science and
Technology",
year = "1995",
address = "Chicago, Illinois, U.S.A",
month = "25-27 " # mar,
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{chen:1995:cale,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "On the Coordination and Adaptability of the Large
Economy: An Application of Genetic Programming to the
Cobweb Model",
booktitle = "Proceedings of the First International Conference on
Applications of Dynamic Models to Economics",
year = "1995",
number = "3",
series = "The School of Management National Central University's
International Conference Series",
pages = "121--159",
address = "ChungLi, Taiwan, R.O.C.",
month = jun # " 17-18",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{chen:1995:GPpsme,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "Genetic Programming, Predictability and Stock Market
Efficiency",
booktitle = "Proceedings of 1995 IFAC/IFIP/IFORS/SEDC Symposium on
Modelling and Control of National and Regional
Economies",
year = "1995",
volume = "II",
address = "Gold Coast, Australia",
month = jul # " 3-5",
organisation = "International Federation of Automatic Control",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{chen:1995:pcdsGP,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "Predicting Chaotic Dynamic Systems with Genetic
Programming",
booktitle = "Proceedings of the 50th International Statistical
Institute Session",
year = "1995",
address = "Beijing",
month = aug # " 21-29",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{chen:1995:itmeeipt,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "Information Transmission, Market Efficiency and the
Evolution of Information-Processing Technology",
booktitle = "Proceedings of the 1995 National Conference on
Management of Technology",
year = "1995",
editor = "C. Houng",
pages = "339--348",
organisation = "Chinese Society of Management of Technology",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{chen:1996:MAAMAW,
author = "Shu-Heng Chen and John Duffy and Chia-Hsuan Yeh",
title = "Modelling Coordination Game as a Multi-Agent Adaptive
System by Genetic Programming",
booktitle = "Position Papers of the 7th European Workshop on
Modelling Autonomous Agents in a Multi-Agent World
(MAAMAW'96)",
year = "1996",
editor = "W. {Van de Velde} and J. W. Perram",
month = jan # " 22-25",
organisation = "Institute for Perception Research, Eindhoven, The
Netherlands",
note = "Technical Report 96-1",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{chen:1996:GPcfe,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "Genetic Programming in Computable Financial
Economics",
booktitle = "Proceedings of the ISCA 11th Conference: Computers and
Their Applications",
year = "1996",
pages = "135--138",
address = "San Francisco, California, U.S.A.",
month = mar # " 7-9",
publisher = "ISCA Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-880843-15-3",
URL = "ftp://econo.nccu.edu.tw/AI-ECON/YEH/1996/ISCA96/isca96.ps",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/15814/ftp:zSzzSzecono.nccu.edu.twzSzAI-ECONzSzYEHzSz1996zSzISCA96zSzisca96.pdf/genetic-programming-in-computable.pdf",
URL = "http://citeseer.ist.psu.edu/324902.html",
size = "5 pages",
abstract = "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...",
}
@InProceedings{chen:1996:bgntemh,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "Bridging the Gap between Nonlinearity Tests and the
Efficient Market Hypothesis by Genetic Programming",
booktitle = "Proceedings of the IEEE/IAFE 1996 Conference on
Computational Intelligence for Financial Engineering",
year = "1996",
pages = "34--39",
address = "Crowne Plaza Manhattan, New York City",
month = mar # " 24-26",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-3236-9",
}
@InCollection{chen:1996:GPpsme,
author = "Shu-Heng Chen",
title = "Genetic Programming, Predictability, and Stock Market
Efficiency",
booktitle = "Modelling and Control of National and Regional
Economies 1995",
publisher = "Pergamon",
year = "1996",
editor = "L. Vlacic and T. Nguyen and D. Cecez-Kecmanovic",
pages = "283--288",
address = "Oxford, Great Britain",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-08-042376-0",
}
@InProceedings{chen:1996:cale:GPcm,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "On the Coordination and Adaptability of the Large
Economy: An Application of Genetic Programming to the
Cobweb Model",
booktitle = "Preprints of 13th World Congress International
Federation of Automatic Control",
year = "1996",
volume = "L",
pages = "279--284",
address = "San Francisco, CA, USA",
month = jun # " 30-" # jul # " 5",
keywords = "genetic algorithms, genetic programming",
}
@InCollection{chen:1996:aigp2,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "Genetic Programming Learning and the Cobweb Model",
booktitle = "Advances in Genetic Programming 2",
publisher = "MIT Press",
year = "1996",
editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}",
pages = "443--466",
chapter = "22",
address = "Cambridge, MA, USA",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-262-01158-1",
URL = "http://www.aiecon.org/staff/shc/pdf/AGP2.pdf",
URL = "http://cisnet.mit.edu/Advances-in-Genetic-Programming/482",
abstract = "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'.",
size = "18 pages",
}
@InProceedings{chen:1996:GPcgcbr,
author = "Shu-Heng Chen and John Duffy and Chia-Hsuan Yeh",
title = "Genetic Programming in the Coordination Game with a
Chaotic Best-Response Function",
booktitle = "Evolutionary Programming V: Proceedings of the Fifth
Annual Conference on Evolutionary Programming",
year = "1996",
editor = "Lawrence J. Fogel and Peter J. Angeline and Thomas
Baeck",
pages = "277--286",
address = "San Diego",
publisher_address = "Cambridge, MA, USA",
month = feb # " 29-" # mar # " 3",
publisher = "MIT Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-262-06190-2",
URL = "ftp://econo.nccu.edu.tw/AI-ECON/YEH/1996/EP96/ep96.ps",
URL = "http://citeseer.ist.psu.edu/rd/6296950%2C326396%2C1%2C0.25%2CDownload/http://citeseer.ist.psu.edu/cache/papers/cs/15814/ftp:zSzzSzecono.nccu.edu.twzSzAI-ECONzSzYEHzSz1996zSzEP96zSzep96.pdf/chen96genetic.pdf",
URL = "http://citeseer.ist.psu.edu/326396.html",
size = "11 pages",
abstract = "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...",
notes = "EP-96
http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=8383
",
}
@Article{chen:1996:caemh,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "Toward a Computable Approach to the Efficient Market
Hypothesis: An Application of Genetic Programming",
journal = "Journal of Economic Dynamics and Control",
year = "1997",
volume = "21",
number = "6",
pages = "1043--1063",
month = "1 " # jun,
keywords = "genetic algorithms, genetic programming, Evolutionary
computation, Minimum description length principle, Mean
absolute percentage error, Efficient market
hypothesis",
doi = "doi:10.1016/S0165-1889(97)82991-0",
URL = "http://www.sciencedirect.com/science/article/B6V85-3SWYBJD-P/2/d1bb80ffce780c45697f44001e20f672",
abstract = "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
search. 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.",
notes = "Society of Computational Economics Conference JEL
classification codes: C63; G14",
}
@InProceedings{chen:1996:esGP,
author = "Shu-Heng Chen and John Duffy and Chia-Hsuan Yeh",
title = "Equilibrium Selection Using Genetic Programming",
booktitle = "Progress in Neural Information Precessing: Proceedings
of the International Conference on Neural Information
Processing (ICONIP'96)",
year = "1996",
editor = "S. Amari and L. Xu and L. Chan and I. King and K.
Leung",
volume = "2",
pages = "1341--1346",
address = "Hong Kong Convention and Exhibition Center, Hong
Kong",
publisher_address = "Singapore",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "981-3083-04-2",
URL = "ftp://econo.nccu.edu.tw/AI-ECON/YEH/1996/ICONIP96/iconip96.ps",
URL = "http://citeseer.ist.psu.edu/rd/6296950%2C323448%2C1%2C0.25%2CDownload/http://citeseer.ist.psu.edu/cache/papers/cs/15814/ftp:zSzzSzecono.nccu.edu.twzSzAI-ECONzSzYEHzSz1996zSzICONIP96zSziconip96.pdf/equilibrium-selection-using-genetic.pdf",
URL = "http://citeseer.ist.psu.edu/323448.html",
size = "8 pages",
abstract = "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...",
notes = "
",
}
@InProceedings{chen:1996:GPlcms,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "Genetic Programming Learning in the Cobweb Model with
Speculators",
booktitle = "Proceedings of 3rd Conference on Business Education",
year = "1996",
pages = "155--176",
address = "Department of Business Education, National Changhua
University of Education, Chunghua, Taiwan",
month = dec # " 5",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{chen:1996:GPlcmsICS,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "Genetic Programming Learning in the Cobweb Model with
Speculators",
booktitle = "International Computer Symposium (ICS'96). Proceedings
of International Conference on Artificial
Intelligence",
year = "1996",
pages = "39--46",
address = "National Sun Yat-Sen University, Kaohsiung, Taiwan,
R.O.C.",
month = dec # " 19-21",
keywords = "genetic algorithms, genetic programming",
}
@Article{chen:1996:itmeeipt,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "Information Transmission, Market Efficiency and the
Evolution of Information-Processing Technology",
journal = "Journal of Technology Management",
year = "1996",
volume = "1",
number = "1",
pages = "23--41",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{chen:1996:cfaGPothers,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "A Comparison of Forcast Accuracy between Genetic
Programming and Other Forcasters: {A} loss-Differential
Approach",
booktitle = "The First International Workshop on Machine Learning,
Forecasting, and Optimization (MALFO96)",
year = "1996",
editor = "Daniel Borrajo and Pedro Isasi",
pages = "39--51",
address = "Gatafe, Spain",
month = "10--12 " # jul,
organisation = "Universidad Carlos III de Madrid",
keywords = "genetic algorithms, genetic programming",
ISBN = "84-89315-04-3",
broken = "http://grial.uc3m.es/~dborrajo/malfo96.html",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/chen_1996_cfaGPothers.pdf",
size = "13 pages",
}
@InProceedings{chen:1996:gpemh,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "Genetic Programming and the Efficient Market
Hypothesis",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "45--53",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
URL = "ftp://econo.nccu.edu.tw/AI-ECON/YEH/1996/GP96/gp96.ps",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/15814/ftp:zSzzSzecono.nccu.edu.twzSzAI-ECONzSzYEHzSz1996zSzGP96zSzgp96.pdf/chen96genetic.pdf",
URL = "http://citeseer.ist.psu.edu/chen96genetic.html",
size = "9 pages",
abstract = "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...",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96",
}
@InProceedings{chen:1997:stfr,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "Speculative Trades and Financial Regulations:
Simulations Based on Genetic Programming",
booktitle = "Proceedings of the IEEE/IAFE 1997 Computational
Intelligence for Financial Engineering (CIFEr'97)",
year = "1997",
pages = "123--129",
address = "New York City, U.S.A.",
month = mar # " 24-25",
publisher = "IEEE Press",
keywords = "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",
doi = "doi:10.1109/CIFER.1997.618924",
size = "7 pages",
abstract = "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",
}
@InProceedings{chen:1997:setpGP,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "Simulating Economic Transition Processes by Genetic
Programming",
booktitle = "Proceedings of the International Conference on
Transition to Advanced Market Institutions and
Economies: Systems and Operations Research Challenges
(Transition'97)",
year = "1997",
editor = "R. Kulikowski and Z. Nahorski and J. W. Owsinski",
pages = "87--93",
address = "Warsaw, Poland",
month = jun # " 18-21",
organisation = "System Research Institute and Polish Academy of
Sciences",
keywords = "genetic algorithms, genetic programming",
ISBN = "83-85847-81-2",
}
@InProceedings{chen:1997:trstpv,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "Trading Restrictions, Speculative Trades and Price
Volatility: An Application of Genetic Programming",
booktitle = "Proceedings of the 3rd International Mendel Conference
on Genetic Algorithms, Optimization Problems, Fuzzy
Logic, Neural Networks, Rough Sets (Mendel'97)",
year = "1997",
pages = "31--37.",
address = "Brno, Czech Republic",
publisher_address = "Brno",
month = jun # " 25-27",
publisher = "PC-DIR",
keywords = "genetic algorithms, genetic programming",
ISBN = "80-214-0884-7",
URL = "ftp://econo.nccu.edu.tw/AI-ECON/YEH/1997/MENDEL97/mendel97.ps",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/15814/ftp:zSzzSzecono.nccu.edu.twzSzAI-ECONzSzYEHzSz1997zSzMENDEL97zSzmendel97.pdf/chen97trading.pdf",
URL = "http://citeseer.ist.psu.edu/chen97trading.html",
size = "8 pages",
abstract = "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...",
}
@InProceedings{chen:1997:eannGPfd,
author = "Shu-Heng Chen and Chih-Chi Ni",
title = "Evolutionary Artificial Neural Networks and Genetic
Programming: {A} Comparative Study Based on Financial
Data",
booktitle = "Artificial Neural Nets and Genetic Algorithms:
Proceedings of the International Conference,
ICANNGA97",
year = "1997",
editor = "George D. Smith and Nigel C. Steele and Rudolf F.
Albrecht",
pages = "397--400",
address = "University of East Anglia, Norwich, UK",
publisher = "Springer-Verlag",
note = "published in 1998",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-211-83087-1",
notes = "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
\cite{chen:1997:eannGPfd}",
}
@InProceedings{chen:1997:msGP,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "Modeling Speculators with Genetic Programming",
booktitle = "Proceedings of the Sixth Conference on Evolutionary
Programming",
year = "1997",
editor = "Peter J. Angeline and Robert G. Reynolds and John R.
McDonnell and Russ Eberhart",
volume = "1213",
series = "Lecture Notes in Computer Science",
pages = "137--147",
address = "Indianapolis, Indiana, USA",
publisher_address = "Berlin",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, no-trade
theorems",
URL = "ftp://econo.nccu.edu.tw/AI-ECON/YEH/1997/EP97/ep97.ps",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/15814/ftp:zSzzSzecono.nccu.edu.twzSzAI-ECONzSzYEHzSz1997zSzEP97zSzep97.pdf/chen96modeling.pdf",
URL = "http://citeseer.ist.psu.edu/chen96modeling.html",
size = "9 pages",
abstract = "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,...",
notes = "EP-97",
}
@InProceedings{chen:1997:GPmvfts,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "Using Genetic Programming to Model Volatility in
Financial Time Series",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "58--63",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
ISBN = "1-55860-483-9",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/chen_1997_GPmvfts.pdf",
size = "6 pages",
abstract = "RGP tested by using Nikkei 255 and S&P 500 as an
example",
notes = "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.",
}
@InProceedings{chen:1997:GPmvfts:NS+P,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "Using Genetic Programming to Model Volatility in
Financial Time Series: The Case of Nikkei 225 and
{S}\&{P} 500",
booktitle = "Proceedings of the 4th JAFEE International Conference
on Investments and Derivatives (JIC'97)",
year = "1997",
pages = "288--306",
address = "Aoyoma Gakuin University, Tokyo, Japan",
month = jul # " 29-31",
keywords = "genetic algorithms, genetic programming",
URL = "ftp://econo.nccu.edu.tw/AI-ECON/YEH/1997/JIC97/jic97.ps",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/15814/ftp:zSzzSzecono.nccu.edu.twzSzAI-ECONzSzYEHzSz1997zSzJIC97zSzjic97.pdf/chen97using.pdf",
URL = "http://citeseer.ist.psu.edu/322892.html",
size = "16 pages",
abstract = "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...",
}
@InProceedings{chen:1997:stfr:ICJAI,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "Speculative Trades and Financial Regulations:
Simulation Bassed on Genetic Programming",
booktitle = "Working Notes of The IJCAI-97: Workshop on Business
Applications of AI. Fifteenth International Joint
Conference on Artificial Intelligence (IJCAI'97)",
year = "1997",
editor = "A. Ghose",
pages = "1--8",
address = "Nagoya, Japan",
month = aug # " 23-29",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{chen:1997:mscGPo,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "Modelling Structural Changes with Genetic Programming:
An Outline",
booktitle = "Proceedings of 15th IMACS World Congress on Scientific
Computation, Moldelling and Applied Mathematics",
year = "1997",
editor = "A. Sydow",
volume = "2",
pages = "621--626",
address = "Berlin",
month = aug # " 24-29",
publisher = "Numerical Mathematics, Wissenschaft \& Technik
Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-89685-552-2",
}
@InCollection{Chen:1997:SunYatSen,
author = "Shuheng Chen and Jiaxuan Ye",
title = "Competition in {"}Quantity theory of money{"} :
Genetic Programming Application in Knowledge
Discovery",
booktitle = "Development(s) and Application(s) of Measurement
Method(s) in Social Science",
publisher = "Sun Yat-Sen Institute for Social Sciences and
Philosophy",
year = "1997",
editor = "Wenshan Yang",
number = "41",
series = "Literature of Sun Yat-Sen Institute for Social
Sciences and Philosophy",
chapter = "7",
pages = "139--183",
address = "Taipei, Taiwan",
month = sep,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.issp.sinica.edu.tw/chinese/book/ebook/pdf1/bk41/charp-7.pdf",
notes = "In Chinese.
Description of GP being used for economic modeling of
GDP based on \cite{koza:book}. Tests GP's ability to
{"}discover{"} money supply equation M2-GNP in USA and
in Taiwanese datasets.
Also known as \cite{bk41/charp-7}",
size = "52 pages",
}
@InProceedings{chen:1998:GPogmidir,
author = "Shu-Heng Chen and C-H. Yeh",
title = "Genetic Programming in the Overlapping Generations
Model: An Ilustration with the Dynamics of the
Inflation Rate",
booktitle = "Evolutionary Programming VII: Proceedings of the
Seventh Annual Conference on Evolutionary Programming",
year = "1998",
editor = "V. William Porto and N. Saravanan and D. Waagen and A.
E. Eiben",
volume = "1447",
series = "LNCS",
pages = "829--837",
address = "Mission Valley Marriott, San Diego, California, USA",
publisher_address = "Berlin",
month = "25-27 " # mar,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-64891-7",
doi = "doi:10.1007/BFb0040753",
notes = "EP-98. National Chengchi University",
}
@InProceedings{chen:1998:opGP,
author = "Shu-Heng Chen and Chia-Hsuan Yeh and Woh-Chiang Lee",
title = "Option Pricing with Genetic Programming",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "32--37",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-548-7",
URL = "ftp://econo.nccu.edu.tw/AI-ECON/YEH/1998/GP98/gp98.ps",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/15815/ftp:zSzzSzecono.nccu.edu.twzSzAI-ECONzSzYEHzSz1998zSzGP98zSzgp98.pdf/option-pricing-with-genetic.pdf",
URL = "http://citeseer.ist.psu.edu/324313.html",
size = "7 pages",
abstract = "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...",
notes = "GP-98",
}
@InProceedings{chen:1998:hdsGP,
author = "Shu-Heng Chen and W.-C. Lee and C.-H. Yeh",
title = "Hedging Derivative Securities with Genetic
Programming",
booktitle = "Application of Machine Learning and Data Mining in
Finance: Workshop at ECML-98",
year = "1998",
editor = "G. Nakhaeizadeh and E. Steurer",
pages = "140--151",
address = "Dorint-Parkhotel, Chemnitz, Germany",
month = "24 " # apr,
keywords = "genetic algorithms, genetic programming",
ISSN = "0947-5125",
notes = "ECML-98 workshop
6
http://www.tu-chemnitz.de/informatik/ecml98/ws6_ag.txt",
}
@InProceedings{oai:CiteSeerPSU:454950,
author = "Shu-Heng Chen and Hung-Shuo Wang and Byoung-Tak
Zhang",
title = "Forecasting High-Frequency Financial Time Series with
Evolutionary Neural Trees: The Case of Hang-Seng Stock
Index",
booktitle = "Proceedings of the International Conference on
Artificial Intelligence, IC-AI '99",
year = "1999",
editor = "Hamid R. Arabnia",
volume = "2",
pages = "437--443",
address = "Las Vegas, Nevada, USA",
month = "28 " # jun # "-1 " # jul,
publisher = "CSREA Press",
keywords = "genetic algorithms, genetic programming, Evolutionary
Artificial Neural Networks, Neural Trees, Sigma-Pi
Neural Trees, Breeder Genetic Algorithm",
ISBN = "1-892512-17-3",
bibsource = "DBLP, http://dblp.uni-trier.de",
URL = "http://bi.snu.ac.kr/Publications/Conferences/International/ICAI99.ps",
URL = "http://citeseer.ist.psu.edu/454950.html",
citeseer-isreferencedby = "oai:CiteSeerPSU:407872;
oai:CiteSeerPSU:67015",
citeseer-references = "oai:CiteSeerPSU:4642; oai:CiteSeerPSU:185401;
oai:CiteSeerPSU:103144",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:454950",
rights = "unrestricted",
size = "7 pages",
abstract = "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.",
notes = "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.",
}
@InProceedings{chen:1999:GATSSPSNEMCS,
author = "Shu-Heng Chen and Wei-Yuan Lin and Chueh-Iong Tsao",
title = "Genetic Algorithms, Trading Strategies and Stochastic
Processes: Some New Evidence from Monte Carlo
Simulations",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "114--121",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-397.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-397.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{SHChen:1999:gpabmsm,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "Genetic Programming in the Agent-Based Modeling of
Stock Markets",
booktitle = "Fifth International Conference: Computing in Economics
and Finance",
year = "1999",
editor = "David A. Belsley and Christopher F. Baum",
pages = "77",
address = "Boston College, MA, USA",
month = "24-26 " # jun,
note = "Book of Abstracts",
keywords = "genetic algorithms, genetic programming, Agent-Based
Computational Economics, Social Learning, Business
School, Artificial Stock Markets, Simulated Annealing,
Peer Pressure",
URL = "http://fmwww.bc.edu/cef99/papers/ChenYeh.pdf",
size = "22 pages",
abstract = "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.",
notes = "PDF and abstract on paper differ in detail. Using PDF
info",
}
@InProceedings{chen:1999:TAFFEAABGAM,
author = "Shu-Heng Chen and Tzu-Wen Kuo",
title = "Towards an Agent-Based Foundation of Financial
Econometrics: An Approach Based on Genetic-Programming
Artificial Markets",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "966",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-425c.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-425c.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{chen:1999:GPAASM,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "Genetic Programming in the Agent-Based Artificial
Stock Market",
booktitle = "Proceedings of the Congress on Evolutionary
Computation",
year = "1999",
editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc
Schoenauer and Xin Yao and Ali Zalzala",
volume = "2",
pages = "834--841",
address = "Mayflower Hotel, Washington D.C., USA",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "6-9 " # jul,
organisation = "Congress on Evolutionary Computation, IEEE / Neural
Networks Council, Evolutionary Programming Society,
Galesia, IEE",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, algorithms",
ISBN = "0-7803-5536-9 (softbound)",
ISBN = "0-7803-5537-7 (Microfiche)",
notes = "CEC-99 - A joint meeting of the IEEE, Evolutionary
Programming Society, Galesia, and the IEE.
Library of Congress Number = 99-61143",
}
@Article{chen:1999:SC,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "Modeling the expectations of inflation in the {OLG}
model with genetic programming",
journal = "Soft Computing - A Fusion of Foundations,
Methodologies and Applications",
year = "1999",
volume = "3",
number = "3",
pages = "53--62",
month = sep,
keywords = "genetic algorithms, genetic programming, overlapping
generations models, bounded rationality, agent-based
computational economics, Pareto-superior equilibrium",
ISSN = "1432-7643",
doi = "doi:10.1007/s005000050053",
abstract = "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.",
}
@InProceedings{RePEc:sce:scecf0:328,
author = "Shu-Heng Chen and Chung-Chi Liao and Chi-Hsuan Yeh",
title = "On The Emergent Properties Of Artificial Stock
Markets: Some Initial Evidences",
booktitle = "Computing in Economics and Finance",
year = "2000",
address = "Universitat Pompeu Fabra, Barcelona, Spain",
month = "6-8 " # jul,
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "http://ideas.repec.org/p/sce/scecf0/328.html
CEF 2000 number 328",
}
@InProceedings{Shu-HengChen:2000:CEF,
author = "Shu-Heng Chen",
title = "On Bargaining Strategies in the {SFI} Double Auction
Tournaments: Is Genetic Programming the Answer?",
booktitle = "Computing in Economics and Finance",
year = "2000",
address = "Universitat Pompeu Fabra, Barcelona, Spain",
month = "6-8 " # jul,
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "http://enginy.upf.es/SCE/index2.html
22 Aug 2004
http://ideas.repec.org/p/sce/scecf0/329.html",
}
@InProceedings{Chen:2000:TAB,
author = "Shu-Heng Chen",
title = "Toward an Agent-Based Computational Modeling of
Bargaining Strategies in Double Auction Markets with
Genetic Programming",
booktitle = "Intelligent Data Engineering and Automated Learning -
IDEAL 2000: Data Mining, Financial Engineering, and
Intelligent Agents",
editor = "Kwong Sak Leung and Lai-Wan Chan and Helen Meng",
year = "2000",
series = "Lecture Notes in Computer Science",
volume = "1983",
pages = "517--531",
address = "Shatin, N.T., Hong Kong, China",
month = "13-15 " # dec,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-41450-9",
CODEN = "LNCSD9",
ISSN = "0302-9743",
bibdate = "Tue Sep 10 19:08:58 MDT 2002",
URL = "http://www.aiecon.org/staff/shc/pdf/toward_an_agent.pdf",
URL = "http://citeseer.ist.psu.edu/463839.html",
URL = "http://link.springer-ny.com/link/service/series/0558/bibs/1983/19830517.htm",
URL = "http://link.springer-ny.com/link/service/series/0558/papers/1983/19830517.pdf",
acknowledgement = ack-nhfb,
size = "15 pages",
abstract = "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]).",
}
@Article{Chen:2000:AOR,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "Simulating economic transition processes by genetic
programming",
journal = "Annals of Operations Research",
year = "2000",
volume = "97",
number = "1-4",
pages = "265--286",
month = dec,
keywords = "genetic algorithms, genetic programming, Kolmogorov
complexity, minimum description length principle,
bounded rationality, short selling",
ISSN = "0254-5330",
doi = "doi:10.1023/A:1018972006990",
abstract = "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.",
}
@InProceedings{oai:CiteSeerPSU:475338,
author = "Shu-Heng Chen and Bin-Tzong Chie",
title = "The Schema Analysis of Emergent Bargaining Strategies
in Agent-Based Double Auction Markets",
booktitle = "Fourth International Conference on Computational
Intelligence and Multimedia Applications (ICCIMA'01)",
year = "2001",
pages = "61",
address = "Yokusike City, Japan",
month = "30 " # oct # "-1 " # nov,
keywords = "genetic algorithms, genetic programming, Double
Auctions, Bargaining Strategies, Predatory Pricing,
Truth-Tellers",
URL = "http://www.aiecon.org/staff/shc/pdf/iccima3.pdf",
URL = "http://csdl.computer.org/comp/proceedings/iccima/2001/1312/00/13120061abs.htm",
URL = "http://citeseer.ist.psu.edu/475338.html",
citeseer-isreferencedby = "oai:CiteSeerPSU:72003",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:475338",
rights = "unrestricted",
abstract = "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.",
}
@Article{Shu-HengChen:2001:JEDC,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "Evolving traders and the business school with genetic
programming: {A} new architecture of the agent-based
artificial stock market",
journal = "Journal of Economic Dynamics and Control",
year = "2001",
volume = "25",
number = "3-4",
pages = "363--393",
month = mar,
keywords = "genetic algorithms, genetic programming, Agent-based
computational economics, Social learning, Business
school, Artificial stock markets",
doi = "doi:10.1016/S0165-1889(00)00030-0",
abstract = "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.",
notes = "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",
}
@Article{Chen:2002:EJEMED,
author = "Shu-Heng Chen and John Duffy and Chia-Hsuan Yeh",
title = "Equilibrium Selection via Adaptation: Using Genetic
Programming to Model Learning in a Coordination Game",
journal = "The Electronic Journal of Evolutionary Modeling and
Economic Dynamics",
year = "2002",
month = "15 " # jan,
keywords = "genetic algorithms, genetic programming, Adaptation,
Coordination Game, Equilibrium Selection, Survival of
the Fittest",
ISSN = "1298-0137",
URL = "http://sclab.mis.yzu.edu.tw/faculty/yeh/paper/2002/e-jemed2002.pdf",
URL = "http://beagle.montesquieu.u-bordeaux.fr/jemed/1002/",
size = "44 pages",
abstract = "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.",
notes = "RePEc:jem:ejemed:1002",
}
@Article{Chen:2002:JEBO,
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
title = "On the emergent properties of artificial stock
markets: the efficient market hypothesis and the
rational expectations hypothesis",
journal = "Journal of Economic Behavior \& Organization",
year = "2002",
volume = "49",
pages = "217--239",
number = "2",
keywords = "genetic algorithms, genetic programming, Artificial
stock markets, Emergent properties, Efficient market
hypothesis, Rational expectations hypothesis",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6V8F-45F900X-8/2/c034ae35c111ca061a11cae1df4b2cd5",
ISSN = "0167-2681",
doi = "doi:10.1016/S0167-2681(02)00068-9",
abstract = "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.",
}
@Book{chen:2002:gagpcf,
editor = "Shu-Heng Chen",
title = "Genetic Algorithms and Genetic Programming in
Computational Finance",
publisher = "Kluwer Academic Publishers",
year = "2002",
address = "Dordrecht",
month = jul,
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7923-7601-3",
URL = "http://www.springer.com/west/home/business?SGWID=4-40517-22-33195998-detailsPage=ppmmedia|toc",
notes = "Sometimes refered to as Genetic Algorithms and
Programming in Computational Finance",
size = "512 pages",
}
@InCollection{ChenAO:2002:gagpcf,
author = "Shu-Heng Chen",
title = "An Overview",
booktitle = "Genetic Algorithms and Genetic Programming in
Computational Finance",
publisher = "Kluwer Academic Press",
year = "2002",
editor = "Shu-Heng Chen",
chapter = "1",
pages = "1--28?",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7923-7601-3",
notes = "part of \cite{chen:2002:gagpcf}",
}
@InCollection{ChenKuoShieh:2002:gagpcf,
author = "Shu-Heng Chen and Tzu-Wen Kuo and Yuh-Pyng Shieh",
title = "Genetic Programming: {A} Tutorial",
booktitle = "Genetic Algorithms and Genetic Programming in
Computational Finance",
publisher = "Kluwer Academic Press",
year = "2002",
editor = "Shu-Heng Chen",
chapter = "3",
pages = "55--80?",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7923-7601-3",
notes = "part of \cite{chen:2002:gagpcf}",
}
@InCollection{ChenLiao:2002:gagpcf,
author = "Shu-Heng Chen and Chung-Chih Liao",
title = "Price Discovery in Agent-Based Computational Modeling
of the Artificial Stock Market",
booktitle = "Genetic Algorithms and Genetic Programming in
Computational Finance",
publisher = "Kluwer Academic Press",
year = "2002",
editor = "Shu-Heng Chen",
chapter = "16",
pages = "335--356?",
keywords = "genetic algorithms, genetic programming, Price
Discovery, Homogeneous Rational Expectation
Equilibrium, Agent-Based Computational Finance,
Excessive Volatility",
ISBN = "0-7923-7601-3",
URL = "http://www.aiecon.org/staff/shc/pdf/apga002.pdf",
abstract = "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.",
notes = "part of \cite{chen:2002:gagpcf}",
size = "8 pages",
}
@InCollection{ChenTaiChie:2002:gagpcf,
author = "Shu-Heng Chen and Chung-Ching Tai and Bin-Tzong Chie",
title = "Individual Rationality as a Partial Impediment to
Market Efficiency: Allocative Efficiency of Markets
with Smart Traders",
booktitle = "Genetic Algorithms and Genetic Programming in
Computational Finance",
publisher = "Kluwer Academic Press",
year = "2002",
editor = "Shu-Heng Chen",
chapter = "17",
pages = "379--396?",
keywords = "genetic algorithms, genetic programming, Agent-Based
Double Auction Markets, Quote Limits, Alpha Value,
Allocative Efficiency",
ISBN = "0-7923-7601-3",
URL = "http://www.econ.iastate.edu/tesfatsi/shusmart.ps",
notes = "part of \cite{chen:2002:gagpcf}",
size = "21 pages",
}
@InProceedings{chen03,
author = "Shu-Heng Chen and Tzu-Wen Kuo",
title = "Overfitting or Poor Learning: {A} Critique of Current
Financial Applications of {GP}",
booktitle = "Genetic Programming, Proceedings of EuroGP'2003",
year = "2003",
editor = "Conor Ryan and Terence Soule and Maarten Keijzer and
Edward Tsang and Riccardo Poli and Ernesto Costa",
volume = "2610",
series = "LNCS",
pages = "34--46",
address = "Essex",
publisher_address = "Berlin",
month = "14-16 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-00971-X",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=34",
abstract = "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.",
notes = "EuroGP'2003 held in conjunction with EvoWorkshops
2003",
}
@InProceedings{Shu-HengChen:2003:CINC,
author = "Shu-Heng Chen and Tzu-Wen Kuo",
title = "Modeling International Short-Term Capital Flow with
Genetic Programming",
booktitle = "Procceedings of the Sixth International Conference on
Computational Intelligence and Natural Computing",
year = "2003",
address = "Embassy Suites Hotel and Conference Center, Cary,
North Carolina USA",
month = sep # " 26-30",
keywords = "genetic algorithms, genetic programming",
notes = "http://axon.cs.byu.edu/CINC/
http://www.ee.duke.edu/JCIS/
National Chengchi University, Taiwan",
}
@InProceedings{chen:2004:lbp,
author = "Shu-Heng Chen and Bin-Tzong Chie",
title = "Functional Modularity in the Test Bed of Economic
Theory -- Using Genetic Programming",
booktitle = "Late Breaking Papers at the 2004 Genetic and
Evolutionary Computation Conference",
year = "2004",
editor = "Maarten Keijzer",
address = "Seattle, Washington, USA",
month = "26 " # jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/LBP062.pdf",
abstract = "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.",
notes = "Part of keijzer:2004:GECCO:lbp",
}
@Article{Shu-HengChen:2004:IJMPB,
author = "Shu-Heng Chen and Bin-Tzong Chie",
title = "Functional Modularity in the Fundamentals of Economic
Theory: Toward an Agent-Based Economic Modeling of the
Evolution of Technology",
journal = "International Journal of Modern Physics B",
year = "2004",
volume = "18",
number = "17-19",
pages = "2376--2386",
month = jul # " 30",
keywords = "genetic algorithms, genetic programming, Agent-based
computational economics, innovation, functional
modularity",
doi = "doi:10.1142/S0217979204025403",
abstract = "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.",
notes = "A1 AI-ECON Research Center, Department of Economics,
National Chengchi University, Taipei, 116, Taiwan ROC",
}
@Article{Chen:2005:IS,
author = "Shu-Heng Chen and Chung-Chih Liao",
title = "Agent-based computational modeling of the stock
price-volume relation",
journal = "Information Sciences",
year = "2005",
volume = "170",
pages = "75--100",
number = "1",
abstract = "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.",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6V0C-4B3JHTS-6/2/9e023835b1c70f176d1903dd3a8b638e",
month = "18 " # feb,
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1016/j.ins.2003.03.026",
}
@InProceedings{DBLP:conf/icnc/ChenH05a,
author = "Shu-Heng Chen and Ya-Chi Huang",
title = "On the Role of Risk Preference in Survivability",
booktitle = "Advances in Natural Computation, Proceedings of First
International Conference, ICNC 2005, Part III",
year = "2005",
editor = "Lipo Wang and Ke Chen and Yew-Soon Ong",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3612",
pages = "612--621",
address = "Changsha, China",
month = aug # " 27-29",
keywords = "genetic algorithms",
ISBN = "3-540-28320-X",
bibsource = "DBLP, http://dblp.uni-trier.de",
URL = "http://www4.nccu.edu.tw/ezkm11/ezcatfiles/cust/img/img/29.pdf",
doi = "doi:10.1007/11539902_74",
abstract = "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.",
notes = "ICNC (3)",
}
@InCollection{Chen:2006:CNEI,
author = "Shu-Heng Chen and Bin-Tzong Chie",
title = "A Functional Modularity Approach to Agent-based
Modeling of the Evolution of Technology",
booktitle = "The Complex Networks of Economic Interactions: Essays
in Agent-Based Economics and Econophysics",
publisher = "Springer",
year = "2006",
editor = "Akira Namatame and Yuuji Aruka and Taisei Kaizouji",
volume = "567",
series = "Lecture Notes in Economics and Mathematical Systems",
pages = "165--178",
month = jan,
keywords = "genetic algorithms, genetic programming, agent-based
computational economics, innovation, functional
modularity",
ISBN = "3-540-28726-4",
abstract = "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.",
}
@Article{Chen:2006:IS,
author = "Shu-Heng Chen",
title = "Computationally intelligent agents in economics and
Finance",
journal = "Information Sciences",
year = "2007",
volume = "177",
number = "5",
pages = "1153--1168",
month = "1 " # mar,
keywords = "genetic algorithms, genetic programming, Computational
intelligence, Agent-based computational economics",
URL = "http://www.aiecon.org/staff/shc/pdf/INS_7416.pdf",
doi = "doi:10.1016/j.ins.2006.08.001",
size = "16 pages",
abstract = "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.",
notes = "The 3rd International Workshop on Computational
Intelligence in Economics and Finance (CIEF'2003)",
}
@InProceedings{conf/iconip/ChenN06,
title = "Pretests for Genetic-Programming Evolved Trading
Programs: zero-intelligence Strategies and Lottery
Trading",
author = "Shu-Heng Chen and Nicolas Navet",
booktitle = "Neural Information Processing, 13th International
Conference, {ICONIP} 2006, Proceedings, Part {III}",
publisher = "Springer",
year = "2006",
volume = "4234",
editor = "Irwin King and Jun Wang and Laiwan Chan and DeLiang L.
Wang",
pages = "450--460",
series = "Lecture Notes in Computer Science",
address = "Hong Kong, China",
month = oct # " 3-6",
bibdate = "2006-10-23",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/iconip/iconip2006-3.html#ChenN06",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-46484-0",
doi = "doi:10.1007/11893295_50",
abstract = "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.",
}
@InCollection{Chen:2007:chen,
title = "Failure of Genetic-Programming Induced Trading
Strategies: Distinguishing between Efficient Markets
and Inefficient Algorithms",
author = "Shu-heng Chen and Nicolas Navet",
booktitle = "Computational Intelligence in Economics and Finance:
Volume II",
publisher = "Springer",
year = "2007",
editor = "Shu-Heng Chen and Paul P. Wang and Tzu-Wen Kuo",
pages = "169--182",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-540-72820-7",
URL = "http://www.loria.fr/~nnavet/publi/SHC_NN_Springer2007.pdf",
URL = "http://www.springer.com/computer/ai/book/978-3-540-72820-7",
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.144.5068",
bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
contributor = "CiteSeerX",
language = "en",
oai = "oai:CiteSeerXPSU:10.1.1.144.5068",
abstract = "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.",
}
@InCollection{Chen:2008:GPTP,
author = "Shu-Heng Chen and Ren-Jie Zeng and Tina Yu",
title = "Co-Evolving Trading Strategies to Analyze Bounded
Rationality in Double Auction Markets",
booktitle = "Genetic Programming Theory and Practice {VI}",
year = "2008",
editor = "Rick L. Riolo and Terence Soule and Bill Worzel",
series = "Genetic and Evolutionary Computation",
chapter = "13",
pages = "195--215",
address = "Ann Arbor",
month = "15-17" # may,
publisher = "Springer",
size = "20 pages",
isbn13 = "978-0-387-87622-1",
notes = "part of \cite{Riolo:2008:GPTP} To be published late
2008",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{Chen:2009:CIFEr,
author = "Shu-Heng Chen and Chung-Ching Tai",
title = "Modeling intelligence of learning agents in an
artificial double auction market",
booktitle = "IEEE Symposium on Computational Intelligence for
Financial Engineering, CIFEr '09",
year = "2009",
month = "30 " # mar # "-" # apr # " 2",
pages = "36--42",
keywords = "genetic algorithms, genetic programming, artificial
double auction market, individual intelligence
modeling, learning agents, psychological,
socioeconomic, software agents, commerce, psychology,
socio-economic effects, software agents",
doi = "doi:10.1109/CIFER.2009.4937500",
abstract = "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.",
notes = "Also known as \cite{4937500}",
}
@InProceedings{Chen:2009:eurogp,
author = "Shu-Heng Chen and Chung-Ching Tai",
title = "Modeling Intelligence of Learning Agents in An
Artificial Double Auction Market",
booktitle = "Proceedings of the 12th European Conference on Genetic
Programming, EuroGP 2009",
year = "2009",
editor = "Leonardo Vanneschi and Steven Gustafson and Alberto
Moraglio and Ivanoe {De Falco} and Marc Ebner",
volume = "5481",
series = "LNCS",
pages = "171--182",
address = "Tuebingen",
month = apr # " 15-17",
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-01180-1",
doi = "doi:10.1007/978-3-642-01181-8_15",
abstract = "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.",
notes = "Part of \cite{conf/eurogp/2009} EuroGP'2009 held in
conjunction with EvoCOP2009, EvoBIO2009 and
EvoWorkshops2009",
}
@InProceedings{ChenZY:2009:GEC,
author = "Shu-Heng Chen and Ren-Jie Zeng and Tina Yu",
title = "Analysis of micro-behavior and bounded rationality in
double auction markets using co-evolutionary {GP}",
booktitle = "GEC '09: Proceedings of the first ACM/SIGEVO Summit on
Genetic and Evolutionary Computation",
year = "2009",
editor = "Lihong Xu and Erik D. Goodman and Guoliang Chen and
Darrell Whitley and Yongsheng Ding",
bibsource = "DBLP, http://dblp.uni-trier.de",
pages = "807--810",
address = "Shanghai, China",
organisation = "SigEvo",
doi = "doi:10.1145/1543834.1543948",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = jun # " 12-14",
isbn13 = "978-1-60558-326-6",
keywords = "genetic algorithms, genetic programming, Poster",
abstract = "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.",
notes = "Also known as \cite{DBLP:conf/gecco/ChenZY09} part of
\cite{DBLP:conf/gec/2009}",
}
@InProceedings{conf/mabs/ChenTW09,
title = "Does Cognitive Capacity Matter When Learning Using
Genetic Programming in Double Auction Markets?",
author = "Shu-Heng Chen and Chung-Ching Tai and Shu G. Wang",
publisher = "Springer",
year = "2009",
volume = "5683",
bibdate = "2010-09-03",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/mabs/mabs2009.html#ChenTW09",
booktitle = "MABS",
editor = "Gennaro di Tosto and H. Van Dyke Parunak",
isbn13 = "978-3-642-13552-1",
pages = "37--48",
series = "Lecture Notes in Computer Science",
URL = "http://dx.doi.org/10.1007/978-3-642-13553-8",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{chen:1998:ecso,
author = "Stephen Chen and Stephen F. Smith",
title = "Experiments on Commonality in Sequencing Operators",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "471--478",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms",
notes = "SGA-98",
}
@InProceedings{chen:1999:IGASSRAFSS,
author = "Stephen Chen and Stephen F. Smith",
title = "Improving Genetic Algorithms by Search Space
Reductions (with Applications to Flow Shop
Scheduling)",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "135--140",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-829.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-829.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{chen:1999:INACCS,
author = "Stephen Chen and Stephen F. Smith",
title = "Introducing a New Advantage of Crossover:
Commonality-Based Selection",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "122--128",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-827.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-827.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{chen:1999:NCSRCFSS,
author = "Stephen Chen and Cesar Guerra-Salcedo and Stephen F.
Smith",
title = "Non-Standard Crossover for a Standard Representation
-- Commonality-Based Feature Subset Selection",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "129--134",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-828.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-828.pdf",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InCollection{Chen:2010:BIEF,
author = "Xi Chen and Ye Pang and Guihuan Zheng",
title = "Macroeconomic Forecasting Using Genetic Programming
Based Vector Error Correction Model",
booktitle = "Buisness Intelligence in Economic Forcasting",
publisher = "IGI Global",
year = "2010",
editor = "Jue Wang and Shouyang Wang",
chapter = "1",
pages = "1--15",
keywords = "genetic algorithms, genetic programming",
isbn13 = "9781615206292",
doi = "doi:10.4018/978-1-61520-629-2",
abstract = "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.",
notes = "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)",
size = "15 pages",
}
@Article{Chen:2004:EAAI,
author = "Xiaofang Chen and Weihua Gui and Yalin Wang and Lihui
Cen",
title = "Multi-step optimal control of complex process: a
genetic programming strategy and its application",
journal = "Engineering Applications of Artificial Intelligence",
year = "2004",
volume = "17",
pages = "491--500",
number = "5",
keywords = "genetic algorithms, genetic programming, Multi-step
comprehensive evaluation, Fitness function, Process
optimal control",
ISSN = "0952-1976",
URL = "http://www.sciencedirect.com/science/article/B6V2M-4CMHSNB-1/2/5c02b126719099d090f4dba0eaaa5cea",
doi = "doi:10.1016/j.engappai.2004.04.018",
owner = "wlangdon",
abstract = "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.",
}
@InProceedings{Chen:2008:ICNC,
author = "Xiao-nan Chen and Hai-tao Chen and Lin Qiu and
Chun-qing Duan",
title = "Model of Water Production Function with Genetic
Programming",
booktitle = "Fourth International Conference on Natural
Computation, ICNC '08",
year = "2008",
month = oct,
volume = "6",
pages = "311--314",
keywords = "genetic algorithms, genetic programming, evolution
calculation, optimal structure searching, water
production function, water stress, irrigation, search
problems",
doi = "doi:10.1109/ICNC.2008.118",
abstract = "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.",
notes = "Also known as \cite{4667851}",
}
@InProceedings{Chen:2007:cec,
author = "Yan Chen and Shingo Mabu and Kotaro Hirasawa and
Jinglu Hu",
title = "Genetic Network Programming with Sarsa Learning and
Its Application to Creating Stock Trading Rules",
booktitle = "2007 IEEE Congress on Evolutionary Computation",
year = "2007",
editor = "Dipti Srinivasan and Lipo Wang",
pages = "220--227",
address = "Singapore",
month = "25-28 " # sep,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "1-4244-1340-0",
file = "1636.pdf",
keywords = "genetic algorithms, genetic programming",
abstract = "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&Hold method and its effectiveness has been
confirmed.",
notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and
the IET.
IEEE Catalog Number: 07TH8963C",
}
@InProceedings{Chen2:2008:cec,
author = "Yan Chen and Shingo Mabu and Kaoru Shimada and Kotaro
Hirasawa",
title = "Real Time Updating Genetic Network Programming for
Adapting to the Change of Stock Prices",
booktitle = "2008 IEEE World Congress on Computational
Intelligence",
year = "2008",
editor = "Jun Wang",
address = "Hong Kong",
month = "1-6 " # jun,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-1823-7",
file = "EC0109.pdf",
abstract = "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&Hold method to confirm its
effectiveness, and it is clarified that the proposed
trading model can obtain much higher profits than
Buy&Hold method.",
keywords = "genetic algorithms, genetic programming",
notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
EPS and the IET.",
}
@InProceedings{Chen:2008:gecco,
author = "Yan Chen and Shingo Mabu and Kaoru Shimada and Kotaro
Hirasawa",
title = "Construction of portfolio optimization system using
genetic network programming with control nodes",
booktitle = "GECCO '08: Proceedings of the 10th annual conference
on Genetic and evolutionary computation",
year = "2008",
editor = "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",
isbn13 = "978-1-60558-130-9",
pages = "1693--1694",
address = "Atlanta, GA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1693.pdf",
doi = "doi:10.1145/1389095.1389413",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "12-16 " # jul,
keywords = "genetic algorithms, genetic programming, control node,
genetic network programming, portfolio optimisation,
reinforcement learning, Real-World application:
Poster",
notes = "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 \cite{1389413}",
}
@InProceedings{Chen2:2009:cec,
author = "Yan Chen and Shingo Mabu and Etsushi Ohkawa and Kotaro
Hirasawa",
title = "Constructing Portfolio Investment Strategy Based on
Time Adapting Genetic Network Programming",
booktitle = "2009 IEEE Congress on Evolutionary Computation",
year = "2009",
editor = "Andy Tyrrell",
pages = "2379--2386",
address = "Trondheim, Norway",
month = "18-21 " # may,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-2959-2",
file = "P026.pdf",
doi = "doi:10.1109/CEC.2009.4983238",
keywords = "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",
abstract = "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&Hold
method to confirm its effectiveness, and it is
clarified that the proposed investment strategy is
effective on the portfolio optimization problem.",
notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and
the IET. IEEE Catalog Number: CFP09ICE-CDR. Also known
as \cite{4983238}",
}
@InProceedings{Chen:2009:ieeeSMC,
author = "Yan Chen and Kotaro Hirasawa and Shingo Mabu",
title = "A portfolio selection model using genetic relation
algorithm and genetic network programming",
booktitle = "IEEE International Conference on Systems, Man and
Cybernetics, SMC 2009",
year = "2009",
month = "11-14 " # oct,
pages = "4378--4383",
abstract = "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.",
keywords = "genetic algorithms, genetic programming, genetic
network programming, correlation coefficient,
evolutionary method, genetic relation algorithm,
portfolio selection model, stock market, stock
markets",
doi = "doi:10.1109/ICSMC.2009.5346940",
ISSN = "1062-922X",
notes = "Also known as \cite{5346940}",
}
@Article{Chen200910735,
author = "Yan Chen and Etsushi Ohkawa and Shingo Mabu and Kaoru
Shimada and Kotaro Hirasawa",
title = "A portfolio optimization model using Genetic Network
Programming with control nodes",
journal = "Expert Systems with Applications",
volume = "36",
number = "7",
pages = "10735--10745",
year = "2009",
ISSN = "0957-4174",
doi = "doi:10.1016/j.eswa.2009.02.049",
URL = "http://www.sciencedirect.com/science/article/B6V03-4VPD6KS-2/2/3cf6750a5518ab6e7d6cf817197d96bd",
keywords = "genetic algorithms, genetic programming, Portfolio
optimization, Genetic Network Programming, Control
node, Reinforcement learning",
abstract = "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.",
}
@Article{Chen200912537,
author = "Yan Chen and Shingo Mabu and Kaoru Shimada and Kotaro
Hirasawa",
title = "A genetic network programming with learning approach
for enhanced stock trading model",
journal = "Expert Systems with Applications",
volume = "36",
number = "10",
pages = "12537--12546",
year = "2009",
ISSN = "0957-4174",
doi = "doi:10.1016/j.eswa.2009.05.054",
URL = "http://www.sciencedirect.com/science/article/B6V03-4WC113D-2/2/a6c6277183e3b22cc3cc50ba71d7062f",
keywords = "genetic algorithms, genetic programming, Genetic
Network Programming, Sarsa Learning, Stock trading
model, Technical Index, Candlestick Chart",
abstract = "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.",
}
@Article{Chen2009,
author = "Yan Chen and Shingo Mabu and Kotaro Hirasawa",
title = "A model of portfolio optimization using time adapting
genetic network programming",
journal = "Computers \& Operations Research",
year = "2010",
volume = "37",
number = "10",
pages = "1697--1707",
month = oct,
ISSN = "0305-0548",
doi = "doi:10.1016/j.cor.2009.12.003",
URL = "http://www.sciencedirect.com/science/article/B6VC5-4Y0D6CX-1/2/2b2154c00eb0c11cef64666b20be06e1",
keywords = "genetic algorithms, genetic programming, Genetic
network programming, Portfolio optimisation,
Reinforcement learning, Technical indices, Candlestick
chart",
abstract = "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.",
}
@InProceedings{Chen:2010:cec,
author = "Yan Chen and Shingo Mabu and Kotaro Hirasawa",
title = "A portfolio selection strategy using Genetic Relation
Algorithm",
booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-6910-9",
abstract = "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.",
doi = "doi:10.1109/CEC.2010.5586430",
notes = "WCCI 2010. Also known as \cite{5586430}",
}
@PhdThesis{YuehuiChen:thesis,
author = "Yuehui Chen",
title = "Hybrid Soft Computing Approach to Identification and
Control of Nonlinear Systems",
school = "Department of Computer Science, Kumamoto University",
year = "2001",
address = "Japan",
month = mar,
email = "CHEN Yuehui ",
keywords = "genetic algorithms, genetic programming, PIPE
Algorithm",
URL = "http://www31.freeweb.ne.jp/computer/chen_yh/thesis.pdf.001",
URL = "http://www31.freeweb.ne.jp/computer/chen_yh/thesis.pdf.002",
URL = "http://www31.freeweb.ne.jp/computer/chen_yh/thesis.pdf.003",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/yuehui.chen/YuehuiChenThesis.pdf",
size = "182 pages",
abstract = "
Recently, complex industrial plants such as mobile
robots, flexible manufacturing system etc., are often
required to perform complex tasks with high precision
under ill-defined conditions, and conventional control
techniques may not be quite effective in these systems.
Soft computing approaches are some computational models
inspired by the simulated human and/or natural
intelligence, and includes fuzzy logic, artificial
neural networks, genetic and evolutionary algorithms.
There have been many successful researches for the
identification and control of nonlinear systems by
using various soft computing techniques with different
computational architectures. The experiences gained
over the past decade indicate that it can be more
effective to use the various soft computing approaches
in a combined manner. But there is no common
recognition about how to combine them in an effective
way, and a unified framework of hybrid soft computing
models in which various soft computing models can be
developed, evolved and evaluated has not been
established.",
abstract = "In this research, a unified framework of hybrid soft
computing models is proposed and it is applied to the
identification and control of industrial plants. First,
a scheme for identification and control of nonlinear
systems using probabilistic incremental program
evolution algorithm (PIPE) is proposed. Based on the
modified PIPE (MPIPE) and some parameter tuning
strategies, a unified framework of hybrid soft
computing models is constructed for the identification
of nonlinear systems, and then the hybrid soft
computing based controller design principles and
methods are developed. As an application, the proposed
methods are applied to the identification and control
of the thrust force (cutting torque) in the drilling
system.
This dissertation consists of six chapters as
follows:
In Chapter 1, the background and the current state of
soft computing researches, and the purpose of the
thesis are described briefly.
In chapter 2, the basic elements of soft computing
technique are discussed, including the evolutionary
algorithms and random search algorithm, neural networks
and fuzzy logic systems. The problems and disadvantages
of the soft computing approaches are pointed out and
their modification and improvements are given.",
abstract = "In chapter 3, in order to cope with the problems of
architecture selection and parameter optimization of
soft computing models simultaneously, a unified
framework is constructed in which various hybrid soft
computing models can be developed, evolved and
evaluated. In the proposed method, the architecture of
the hybrid soft computing models is evolved by MPIPE
and the parameters used in soft computing models are
optimized by hybrid or non-hybrid parameter
optimization strategy, respectively. The effectiveness
of the proposed method has been confirmed by simulation
studies.
In chapter 4, some common soft computing based
controller design principles are discussed briefly.
Then a new control scheme for nonlinear systems based
on PIPE algorithm is proposed. Finally, based on the
basis function networks a unified framework for control
of affine and non-affine nonlinear systems is presented
with guaranteed stability analysis. The simulation and
experimental results show the effectiveness of the
proposed controller.",
abstract = "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.",
notes = "my PDF reader barfed 20 July 2001. url_2 ok",
}
@InProceedings{Chen:2006:ESANN,
author = "Yuehui Chen and Bo Yang and Ajith Abraham",
title = "Optimal design of hierarchical wavelet networks for
time-series forecasting",
booktitle = "14th European Symposium on Artificial Neural Networks
(ESANN 2006)",
year = "2006",
pages = "155--160",
address = "Bruges, Belgium",
month = apr # " 26-28",
keywords = "genetic algorithms, genetic programming, ECGP",
isbn13 = "2-930307-06-4",
URL = "http://www.dice.ucl.ac.be/Proceedings/esann/esannpdf/es2006-57.pdf",
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.136.9044",
size = "6 pages",
bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
bibsource = "DBLP, http://dblp.uni-trier.de",
language = "en",
oai = "oai:CiteSeerXPSU:10.1.1.136.9044",
abstract = "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.",
}
@Article{Chen:2006:N,
author = "Yuehui Chen and Ajith Abraham and Bo Yang",
title = "Feature selection and classification using flexible
neural tree",
journal = "Neurocomputing",
year = "2006",
volume = "70",
number = "1-3",
pages = "305--313",
month = dec,
note = "Selected Papers from the 7th Brazilian Symposium on
Neural Networks (SBRN '04), 7th Brazilian Symposium on
Neural Networks",
keywords = "genetic algorithms, genetic programming, Flexible
neural tree model, Memetic algorithm, Intrusion
detection system, Breast cancer classification",
doi = "doi:10.1016/j.neucom.2006.01.022",
abstract = "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.",
}
@Article{Chen:2007:N,
author = "Yuehui Chen and Bo Yang and Ajith Abraham",
title = "Flexible neural trees ensemble for stock index
modeling",
journal = "Neurocomputing",
year = "2007",
volume = "70",
number = "4-6",
pages = "697--703",
month = jan,
note = "Advanced Neurocomputing Theory and Methodology -
Selected papers from the International Conference on
Intelligent Computing 2005 (ICIC 2005), International
Conference on Intelligent Computing 2005",
keywords = "genetic algorithms, genetic programming, Flexible
neural tree, GP-like tree structure-based evolutionary
algorithm, Particle swarm optimisation, Ensemble
learning, Stock index",
doi = "doi:10.1016/j.neucom.2006.10.005",
abstract = "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.",
}
@Book{Chen:2010:book,
author = "Yuehui Chen and Ajith Abraham",
title = "Tree-Structure based Hybrid Computational
Intelligence",
publisher = "Springer",
year = "2010",
volume = "2",
series = "Intelligent Systems Reference Library",
keywords = "genetic algorithms, genetic programming, Computational
Intelligence, flexible neural trees, flexible neural
trees networks, neural networks",
isbn13 = "978-3-642-04738-1",
URL = "http://www.springer.com/engineering/book/978-3-642-04738-1",
doi = "doi:10.1007/978-3-642-04739-8",
abstract = "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.",
notes = "Theoretical Foundations and Applications ECGP, fuzzy,
iris dataset",
size = "210 pages",
}
@Article{Chen2011106,
author = "Yuehui Chen and Bin Yang and Qingfang Meng and Yaou
Zhao and Ajith Abraham",
title = "Time-series forecasting using a system of ordinary
differential equations",
journal = "Information Sciences",
volume = "181",
number = "1",
pages = "106--114",
year = "2011",
ISSN = "0020-0255",
doi = "doi:10.1016/j.ins.2010.09.006",
URL = "http://www.sciencedirect.com/science/article/B6V0C-5100HS4-3/2/c9722759c9e35e7dba49e35c559ae617",
keywords = "genetic algorithms, genetic programming, PSO, Hybrid
evolutionary method, Network traffic, Small-time scale,
The additive tree models, Ordinary differential
equations, Particle swarm optimisation",
abstract = "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.",
}
@PhdThesis{Yuxin_Chen:thesis,
author = "Yuxin Chen",
title = "A Novel Hybrid Focused Crawling Algorithm to Build
Domain-Specific Collections",
school = "Virginia Polytechnic Institute and State University",
year = "2007",
address = "Blacksburg, Virginia, USA",
month = feb # " 5",
keywords = "genetic algorithms, genetic programming, digital
libraries, focused crawler, classification,
meta-search",
URL = "http://scholar.lib.vt.edu/theses/available/etd-02162007-005107/",
URL = "http://scholar.lib.vt.edu/theses/available/etd-02162007-005107/unrestricted/YuxinDissertation_etd_final1.pdf",
URN = "etd-02162007-005107",
size = "85 pages",
abstract = "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.",
}
@InProceedings{Chen:2007:WISP,
author = "Zheng Chen and Siwei Lu",
title = "A Genetic Programming Approach for Classification of
Textures Based on Wavelet Analysis",
booktitle = "IEEE International Symposium on Intelligent Signal
Processing, WISP 2007",
year = "2007",
month = oct,
pages = "1--6",
keywords = "genetic algorithms, genetic programming, feature
extraction, texture classification, wavelet analysis,
wavelet decomposition, feature extraction, image
classification, image texture, wavelet transforms",
doi = "doi:10.1109/WISP.2007.4447575",
abstract = "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.",
notes = "Also known as \cite{4447575}",
}
@InCollection{Cheng:1997:rphGPri,
author = "Cleve Cheng",
title = "Recognizing Poker Hands with Genetic Programming and
Restricted Iteration",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1997",
publisher = "Stanford Bookstore",
year = "1997",
editor = "John R. Koza",
pages = "18--27",
address = "Stanford, California, 94305-3079 USA",
month = "17 " # mar,
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-205981-2",
notes = "part of \cite{koza:1997:GAGPs}",
}
@InProceedings{Cheng:2009:ASIA,
author = "Huifang Cheng and Yongqiang Zhang and Jing Zhao",
title = "Improved Genetic Programming Model for Software
Reliability",
booktitle = "International Asia Symposium on Intelligent
Interaction and Affective Computing, ASIA '09",
year = "2009",
month = dec,
pages = "164--167",
keywords = "genetic algorithms, genetic programming, SBSE, IGP
algorithm, improved genetic programming model, software
failure mechanism, software reliability, software
reliability",
doi = "doi:10.1109/ASIA.2009.38",
abstract = "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.",
notes = "Also known as \cite{5376005}",
}
@InProceedings{Cheng:2009:ASIA2,
author = "Huifang Cheng and Yongqiang Zhang and Fangping Li",
title = "Improved Genetic Programming Algorithm",
booktitle = "International Asia Symposium on Intelligent
Interaction and Affective Computing, ASIA '09",
year = "2009",
month = dec,
pages = "168--171",
abstract = "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.",
keywords = "genetic algorithms, genetic programming, canonical
genetic programming algorithm, crossover operator,
mutation operation, problem solving, reproduction
operator, symbolic regression, regression analysis",
doi = "doi:10.1109/ASIA.2009.39",
notes = "Also known as \cite{5376006}",
}
@InProceedings{oai:CiteSeerPSU:521419,
author = "V. H. L. Cheng and L. S. Crawford and P. K. Menon",
title = "Air Traffic Control Using Genetic Search Techniques",
booktitle = "1999 IEEE International Conference on Control
Applications",
year = "1999",
address = "Hawai'i, HA, USA",
month = aug # " 22-27",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.optisyn.com/research/papers/papers/1999/traffic_99.pdf",
URL = "http://citeseer.ist.psu.edu/521419.html",
citeseer-references = "oai:CiteSeerPSU:212034",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:521419",
rights = "unrestricted",
abstract = "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.",
}
@Article{DBLP:journals/ijcia/ChiaT01,
author = "Henry Wai Kit Chia and Chew Lim Tan",
title = "Neural Logic Network Learning Using Genetic
Programming",
journal = "International Journal of Computational Intelligence
and Applications",
volume = "1",
number = "4",
year = "2001",
bibsource = "DBLP, http://dblp.uni-trier.de",
pages = "357--368",
keywords = "genetic algorithms, genetic programming, Neural
network, rule-based learning, data mining",
doi = "doi:10.1142/S1469026801000299",
abstract = "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.",
}
@InProceedings{chia:2004:lbp,
author = "Henry Wai-Kit Chia and Chew-Lim Tan",
title = "Association-Based Evolution of Comprehensible Neural
Logic Networks",
booktitle = "Late Breaking Papers at the 2004 Genetic and
Evolutionary Computation Conference",
year = "2004",
editor = "Maarten Keijzer",
address = "Seattle, Washington, USA",
month = "26 " # jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/LBP061.pdf",
abstract = "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.",
notes = "Part of \cite{keijzer:2004:GECCO:lbp}",
}
@InProceedings{chia:cas:gecco2004,
author = "Henry Wai-Kit Chia and Chew-Lim Tan",
title = "Confidence and Support Classification Using
Genetically Programmed Neural Logic Networks",
booktitle = "Genetic and Evolutionary Computation -- GECCO-2004,
Part II",
year = "2004",
editor = "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",
series = "Lecture Notes in Computer Science",
pages = "836--837",
address = "Seattle, WA, USA",
publisher_address = "Heidelberg",
month = "26-30 " # jun,
organisation = "ISGEC",
publisher = "Springer-Verlag",
volume = "3103",
ISBN = "3-540-22343-6",
ISSN = "0302-9743",
URL = "http://www.comp.nus.edu.sg/~tancl/Papers/GECCO2004/gecco04post.pdf",
URL = "http://link.springer.de/link/service/series/0558/bibs/3103/31030836.htm",
size = "2",
keywords = "genetic algorithms, genetic programming, Poster",
abstract = "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.",
notes = "GECCO-2004 A joint meeting of the thirteenth
international conference on genetic algorithms
(ICGA-2004) and the ninth annual genetic programming
conference (GP-2004)",
}
@Article{10.1109/TKDE.2006.111,
author = "Henry W. K. Chia and Chew Lim Tan and Sam Y. Sung",
title = "Enhancing Knowledge Discovery via Association-Based
Evolution of Neural Logic Networks",
journal = "IEEE Transactions on Knowledge and Data Engineering",
volume = "18",
number = "7",
year = "2006",
publisher = "IEEE Computer Society",
address = "Los Alamitos, CA, USA",
pages = "889--901",
keywords = "genetic algorithms, genetic programming, Data mining,
knowledge acquisition, connectionism and neural nets,
rule-based knowledge representation",
ISSN = "1041-4347",
doi = "doi:10.1109/TKDE.2006.111",
abstract = "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.",
}
@InProceedings{chia-hsuanyeh:2001:gecco,
title = "The Differences between Social and Individual Learning
on the Time Series Properties: The Approach Based on
Genetic Programming",
author = "Chia-Hsuan Yeh and Shu-Heng Chen",
pages = "191",
year = "2001",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2001)",
editor = "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",
address = "San Francisco, California, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "7-11 " # jul,
keywords = "genetic algorithms, genetic programming: Poster,
Social Learning, Individual Learning, Artificial Stock
Market, Agent-Based Modeling",
ISBN = "1-55860-774-9",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d02.pdf",
notes = "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 \cite{spector:2001:GECCO}",
}
@InProceedings{Chiang:2010:3CA,
author = "Cheng-Hsiung Chiang",
title = "A genetic programming based rule generation approach
for intelligent control systems",
booktitle = "2010 International Symposium on Computer Communication
Control and Automation (3CA)",
year = "2010",
month = may,
volume = "1",
pages = "104--107",
abstract = "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.",
keywords = "genetic algorithms, genetic programming, genetic
programming intelligent control system, percepter,
radaptor, rule generation approach, symbolic rule
controller, intelligent control, learning (artificial
intelligence), path planning",
doi = "doi:10.1109/3CA.2010.5533882",
notes = "Also known as \cite{5533882}",
}
@TechReport{wpa98086,
author = "N. K. Chidambaran and Chi-Wen {Jevons Lee} and Joaquin
R. Trigueros",
title = "An Adaptive Evolutionary Approach to Option Pricing
via Genetic Programming",
institution = "Leonard N. Stern School of Buisness, New York
University",
year = "1998",
type = "Working paper",
number = "FIN-98-086",
month = nov,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.stern.nyu.edu/fin/workpapers/wpa98086.pdf",
abstract = "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.",
notes = "see also \cite{chidambaran:1998:aeaopGP} and
\cite{chidambaran:2002:ECEF}",
size = "48 pages",
}
@InProceedings{chidambaran:1998:aeaopGP,
author = "N. K. Chidambaran and C. H. Jevons Lee and Joaquin R.
Trigueros",
title = "An Adaptive Evolutionary Approach to Option Pricing
via Genetic Programming",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "38--41",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-548-7",
notes = "GP-98
See also \cite{wpa98086}",
}
@InCollection{chidambaran:2002:ECEF,
author = "N. K. Chidambaran and Joaquin Triqueros and Chi-Wen
Jevons Lee",
title = "Option Pricing via Genetic Programming",
booktitle = "Evolutionary Computation in Economics and Finance",
publisher = "Physica Verlag",
year = "2002",
editor = "Shu-Heng Chen",
volume = "100",
series = "Studies in Fuzziness and Soft Computing",
chapter = "20",
pages = "383--398?",
month = "2002",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-7908-1476-8",
notes = "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 \cite{wpa98086}",
}
@InProceedings{Chidambaran:2003:WSC,
author = "N. K. Chidambaran",
title = "Genetic programming with Monte Carlo simulation for
option pricing",
booktitle = "Proceedings of the 2003 Winter Simulation Conference",
year = "2003",
editor = "S. Chick and P. J. Sanchez and D. Ferrin and D. J.
Morrice",
volume = "1",
pages = "285--292",
address = "New Orleans, USA",
month = "7-10 " # dec,
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-8132-7",
URL = "http://www.informs-sim.org/wsc03papers/035.pdf",
size = "8 pages",
abstract = "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.",
notes = "details from ieee",
}
@InProceedings{Chie:gecco06lbp,
author = "Bin-Tzong Chie and Chih-Chien Wang",
title = "Model for Evolutionary Technology - An Automatically
Defined Terminal Approach",
booktitle = "Late breaking paper at Genetic and Evolutionary
Computation Conference {(GECCO'2006)}",
year = "2006",
month = "8-12 " # jul,
editor = "J{\"{o}}rn Grahl",
address = "Seattle, WA, USA",
notes = "Distributed on CD-ROM at GECCO-2006",
keywords = "genetic algorithms, genetic programming, Automatically
Defined Terminal, Agent-Based Modeling",
abstract = "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.",
}
@Article{Chien:2002:ESA,
author = "Been-Chian Chien and Jung Yi Lin and Tzung-Pei Hong",
title = "Learning discriminant functions with fuzzy attributes
for classification using genetic programming",
journal = "Expert Systems with Applications",
year = "2002",
volume = "23",
pages = "31--37",
number = "1",
owner = "wlangdon",
keywords = "genetic algorithms, genetic programming,
Classification, Knowledge discovery, Fuzzy sets",
ISSN = "0957-4174",
URL = "http://www.sciencedirect.com/science/article/B6V03-45C00T2-1/2/e7d49cc18dd12961ac2e5c114c41f667",
doi = "doi:10.1016/S0957-4174(02)00025-8",
abstract = "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.",
}
@InProceedings{Chien:2002:KES,
author = "Been Chian Chien and Jung Yi Lin",
title = "A Classifier with the Function-based Decision Tree",
booktitle = "Proceedings of KES'2002 the Sixth International
Conference on Knowledge-Based Intelligent Information
Engineering Systems",
year = "2002",
editor = "E. Damiani and L. C. Jain and R. J. Howlett and N.
Ichalkaranje",
volume = "82",
series = "Frontiers in Artificial Intelligence and
Applications",
pages = "648--652",
address = "Podere d'Ombriano, Crema, Italy",
publisher_address = "Amsterdam",
month = "19-19 " # sep,
publisher = "IOS Press",
keywords = "genetic algorithms, genetic programming,
classification, decision tree",
ISBN = "1-58603-280-1",
notes = "http://www.iospress.nl/loadtop/load.php?isbn=1586032801",
}
@InProceedings{Chien:2003:DaWaK,
author = "Been-Chian Chien and Jui-Hsiang Yang and Wen-Yang
Lin",
title = "Generating Effective Classifiers with Supervised
Learning of Genetic Programming",
booktitle = "Data Warehousing and Knowledge Discovery: 5th
International Conference, DaWaK 2003",
year = "2003",
volume = "2737",
series = "Lecture Notes in Computer Science",
pages = "192--201",
address = "Prague, Czech Republic",
month = "3-5 " # sep,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
URL = "http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=2737&spage=192",
doi = "doi:10.1007/b11825",
ISBN = "3-540-40807-X",
abstract = "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.",
}
@Article{Chien:2004:PR,
author = "Been-Chian Chien and Jung-Yi Lin and Wei-Pang Yang",
title = "Learning effective classifiers with {Z}-value measure
based on genetic programming",
journal = "Pattern Recognition",
year = "2004",
volume = "37",
pages = "1957--1972",
number = "10",
abstract = "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.",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6V14-4CPVJFT-3/2/51f0ecbd7d198da15f4ae094e378c5d0",
month = oct,
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1016/j.patcog.2004.03.016",
}
@InProceedings{Chien:2006:ICSMC,
author = "Been-Chian Chien and Jui-Hsiang Yang",
title = "Features Selection based on Rough Membership and
Genetic Programming",
booktitle = "IEEE International Conference on Systems, Man and
Cybernetics, ICSMC '06",
year = "2006",
volume = "5",
pages = "4124--4129",
address = "Taipei, Taiwan",
month = "8-11 " # oct,
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-4244-0100-3",
doi = "doi:10.1109/ICSMC.2006.384780",
abstract = "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.",
notes = "Member, IEEE, National University of Tainan, Tainan
700, Taiwan, R. O. C. Tel: +886-6-2606123 ext. 7707,
fax:+886-6-2606125;",
}
@Article{DBLP:journals/mvl/ChienYH11,
author = "Been-Chian Chien and Jui-Hsiang Yang and Tzung-Pei
Hong",
title = "Learning Discriminant Functions based on Genetic
Programming and Rough Sets",
journal = "Multiple-Valued Logic and Soft Computing",
year = "2011",
volume = "17",
number = "2-3",
pages = "135--155",
keywords = "genetic algorithms, genetic programming, Machine
learning, discriminant function, classification, rough
sets.",
URL = "http://www.oldcitypublishing.com/MVLSC/MVLSCabstracts/MVLSC17.2-3abstracts/MVLSCv17n2-3p135-155Chien.html",
abstract = "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.",
bibsource = "DBLP, http://dblp.uni-trier.de",
}
@InCollection{chien:2000:GTRUEM,
author = "Edward K. Chien",
title = "Grid-Based Trace Routing Using Evolutionary Methods",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2000",
year = "2000",
editor = "John R. Koza",
pages = "90--97",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms",
notes = "part of \cite{koza:2000:gagp}",
}
@InProceedings{chikara:1999:CLASCS,
author = "Maezawa Chikara and Atsumi Masayasu",
title = "Collaborative Learning Agents with Structural
Classifier Systems",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "777",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems, poster
papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-859.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-859.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@TechReport{CS-TR-07-3,
author = "Barret Chin and Mengjie Zhang",
title = "Object Detection using Neural Networks and Genetic
Programming",
institution = "Computer Science, Victoria University of Wellington",
year = "2007",
type = "Technical report",
number = "CS-TR-07-3",
address = "New Zealand",
month = nov,
keywords = "genetic algorithms, genetic programming, object
detection, neural networks, region refinement, feature
selection",
URL = "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-07/CS-TR-07-3.pdf",
URL = "http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-07-3.abs.html",
abstract = "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.",
size = "pages 13",
}
@InProceedings{conf/evoW/ChinZ08,
title = "Object Detection Using Neural Networks and Genetic
Programming",
author = "Barret Chin and Mengjie Zhang",
bibdate = "2008-04-15",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/evoW/evoW2008.html#ChinZ08",
booktitle = "Proceedings of Evo{COMNET}, Evo{FIN}, Evo{HOT},
Evo{IASP}, Evo{MUSART}, Evo{NUM}, Evo{STOC}, and
EvoTransLog, Applications of Evolutionary Computing,
EvoWorkshops",
publisher = "Springer",
year = "2008",
volume = "4974",
editor = "Mario Giacobini and Anthony Brabazon and Stefano
Cagnoni and Gianni {Di Caro} and Rolf Drechsler and
Anik{\'o} Ek{\'a}rt and Anna Esparcia-Alc{\'a}zar 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",
isbn13 = "978-3-540-78760-0",
pages = "335--340",
series = "Lecture Notes in Computer Science",
doi = "doi:10.1007/978-3-540-78761-7_34",
address = "Naples",
month = "26-28 " # mar,
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{1144138,
author = "Clement Chion and Luis E. {Da Costa} and Jacques-Andre
Landry",
title = "Genetic programming for agricultural purposes",
booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference
on Genetic and evolutionary computation",
year = "2006",
editor = "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",
volume = "1",
ISBN = "1-59593-186-4",
pages = "783--790",
address = "Seattle, Washington, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p783.pdf",
doi = "doi:10.1145/1143997.1144138",
publisher = "ACM Press",
publisher_address = "New York, NY, 10286-1405, USA",
month = "8-12 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, crop nitrogen
content, GP, hyperspectral imagery, management,
precision farming, remote sensing, site-specific
management, spectral vegetation indices (SVI),
vegetation indices",
notes = "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",
}
@Article{Chion:2008:ieeeTGRS,
author = "Clement Chion and Jacques-Andre Landry and Luis {Da
Costa}",
title = "A Genetic-Programming-Based Method for Hyperspectral
Data Information Extraction: Agricultural
Applications",
journal = "IEEE Transactions on Geoscience and Remote Sensing",
year = "2008",
month = aug,
volume = "46",
number = "8",
pages = "2446--2457",
keywords = "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",
doi = "doi:10.1109/TGRS.2008.922061",
ISSN = "0196-2892",
abstract = "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.",
notes = "Also known as \cite{4559746}",
}
@InProceedings{1277274,
author = "Darren M. Chitty",
title = "A data parallel approach to genetic programming using
programmable graphics hardware",
booktitle = "GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation",
year = "2007",
editor = "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",
volume = "2",
isbn13 = "978-1-59593-697-4",
pages = "1566--1573",
address = "London",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1566.pdf",
doi = "doi:10.1145/1276958.1277274",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
month = "7-11 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, data
parallelism, GPU, graphics cards",
abstract = "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.",
notes = "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.",
}
@InProceedings{Chiu:2001:AGP,
author = "Chaochang Chiu and Jih-Tay Hsu and Chih-Yung Lin",
title = "The Application of Genetic Programming in Milk Yield
Prediction for Dairy Cows",
booktitle = "Rough Sets and Current Trends in Computing : Second
International Conference, RSCTC 2000. Revised Papers",
editor = "W. Ziarko and Y. Yao",
volume = "2005",
pages = "598--602",
series = "Lecture Notes in Computer Science",
address = "Banff, Canada",
publisher_address = "Heidelberg",
month = oct # " 16-19",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, dynamic
mutation, milk yield prediction",
year = "2001",
CODEN = "LNCSD9",
ISSN = "0302-9743",
bibdate = "Sat Feb 2 13:03:23 MST 2002",
URL = "http://link.springer-ny.com/link/service/series/0558/bibs/2005/20050598.htm",
URL = "http://link.springer-ny.com/link/service/series/0558/papers/2005/20050598.pdf",
acknowledgement = ack-nhfb,
abstract = "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.",
}
@InProceedings{cho:1996:mNNeGP,
author = "Sung-Bae Cho and Katsunori Shimohara",
title = "Modular Neural Networks Evolved by Genetic
Programming",
booktitle = "Proceedings of the 1996 {IEEE} International
Conference on Evolutionary Computation",
year = "1996",
volume = "1",
pages = "681--684",
address = "Nagoya, Japan",
month = "20-22 " # may,
organisation = "IEEE Neural Network Council",
keywords = "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",
ISBN = "0-7803-2902-3",
doi = "doi:10.1109/ICEC.1996.542683",
size = "4 pages",
abstract = "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.",
notes = "ICEC-96 Evolves ANN network for recognising human
written characters",
}
@Article{cho:1998:mNNeGP,
author = "Sung-Bae Cho and Katsunori Shimohara",
title = "Evolutionary Learning of Modular Neural Networks with
Genetic Programming",
journal = "Applied Intelligence",
year = "1998",
volume = "9",
number = "3",
pages = "191--200",
month = nov # "/" # dec,
keywords = "genetic algorithms, genetic programming, neural
networks, evolutionary computation, modules, emergence,
handwritten digits, OCR",
ISSN = "0924-669X",
notes = "Evolves ANN network for categorizing human written
characters. USA Federal post office dataset online?
",
}
@Proceedings{aspgp03,
title = "Proceedings of The First Asian-Pacific Workshop on
Genetic Programming",
year = "2003",
editor = "Sung-Bae Cho and Nguyen Xuan Hoai and Yin Shan",
address = "Rydges (lakeside) Hotel, Canberra, Australia",
month = "8 " # dec,
organisation = "School of Information Technology and Electrical
Engineering, Australian Defence Force Academy,
University College, University of New South Wales,
Australia",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-9751724-0-9",
URL = "http://www.cs.adfa.edu.au/~cec_gp/",
size = "62 pages",
}
@InProceedings{D.Y.Cho:1998:GPmacstt,
author = "D. Y. Cho and B. T. Zhang",
title = "Genetic programming of multi-agent cooperation
strategies for table transport",
booktitle = "The Third Asian Fuzzy Systems Symposium",
year = "1998",
editor = "K. C. Min",
pages = "170--175",
address = "Kyungnam University, Masan, Korea",
month = "18-21 " # jun,
organisation = "Korea Fuzzy Logic and Intelligent Systems Society
(KFIS)",
keywords = "genetic algorithms, genetic programming",
notes = "AFSS'98",
}
@InProceedings{cho:1999:GPalecri,
author = "D. Y. Cho and B. T. Zhang",
title = "Genetic programming-based Alife techniques for
evolving collective robotic intelligence",
booktitle = "Proceedings 4th International Symposium on Artificial
Life and Robotics",
year = "1999",
editor = "M. Sugisaka",
pages = "236--239",
address = "B-Con Plaza, Beppu, Oita, Japan",
month = "19-22 " # jan,
keywords = "genetic algorithms, genetic programming, artificial
life, multiagent learning, fitness switching, training
data selection",
URL = "http://bi.snu.ac.kr/Publications/Conferences/International/AROB99.ps",
URL = "http://citeseer.ist.psu.edu/455064.html",
abstract = "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.",
notes = "AROB'99 Details from www site etc",
}
@InProceedings{cho:2000:BEAENTMTSD,
author = "Dong-Yeon Cho and Byoung-Tak Zhang",
title = "Bayesian Evolutionary Algorithms for Evolving Neural
Tree Models of Time Series Data",
booktitle = "Proceedings of the 2000 Congress on Evolutionary
Computation CEC00",
volume = "2",
year = "2000",
pages = "1451--1458",
address = "La Jolla Marriott Hotel La Jolla, California, USA",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "6-9 " # jul,
organisation = "IEEE Neural Network Council (NNC), Evolutionary
Programming Society (EPS), Institution of Electrical
Engineers (IEE)",
publisher = "IEEE Press",
keywords = "time series",
ISBN = "0-7803-6375-2",
abstract = "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.",
notes = "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",
}
@Article{Cho:2006:B,
author = "Dong-Yeon Cho and Kwang-Hyun Cho and Byoung-Tak
Zhang",
title = "Identification of biochemical networks by {S}-tree
based genetic programming",
journal = "Bioinformatics",
year = "2006",
volume = "22",
number = "13",
pages = "1631--1640",
month = jul,
keywords = "genetic algorithms, genetic programming",
ISSN = "1367-4803",
doi = "doi:10.1093/bioinformatics/btl122",
abstract = "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.",
notes = "C The Author 2006",
}
@InProceedings{Choenni:1999:SGB,
author = "Sunil Choenni",
title = "On the Suitability of Genetic-Based Algorithms for
Data Mining",
booktitle = "Advances in Database Technologies",
editor = "Yahiko Kambayashi and Dik Lun Lee and Ee-Peng Lim and
Mukesh Kumar Mohania and Yoshifumi Masunaga",
series = "LNCS",
volume = "1552",
pages = "55--67",
year = "1999",
CODEN = "LNCSD9",
ISSN = "0302-9743",
bibdate = "Tue Sep 14 06:09:05 MDT 1999",
acknowledgement = ack-nhfb,
keywords = "genetic algorithms, genetic programming, ADT,
conceptual modelling, database technologies, mobile
data access, spatio-temporal data management",
address = "Singapore",
month = "19-20 " # nov # " 1998",
publisher = "Springer-Verlag",
email = "choenni@nrl.nl",
keywords = "genetic algorithms",
ISBN = "3-540-65690-1",
notes = "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.
\cite{choenni: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",
}
@TechReport{choenni:1998:SGADM,
author = "Sunil Choenni",
title = "On the Suitability of Genetic-Based Algorithms for
Data Mining",
institution = "National Aerospace Laboratory",
year = "1998",
number = "NLR-TP-98484",
address = "Amsterdam",
month = nov,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.nlr.nl/NLR-TP-98484.pdf",
URL = "http://citeseer.ist.psu.edu/271039.html",
abstract = "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.",
notes = "shorter version published as
\cite{Choenni:1999:SGB}
page 22 {"}real-life database, FAA incident database,
contains aircraft incident data 1978-95{"}",
size = "26 pages",
}
@TechReport{choenni:1999:ieGDMa,
author = "Sunil Choenni",
title = "Implementation and Evaluation of a Genetic-Based Data
Mining Algorithm",
institution = "National Aerospace Laboratory",
year = "1999",
number = "NLR-TR-99281",
address = "Amsterdam",
month = jul,
keywords = "genetic algorithms, genetic programming",
abstract = "GA can be rapidly implemented for DM yielding
reasonable results. However, building an operational
tool requires more effort",
notes = "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.
",
size = "13 pages",
}
@InProceedings{choi:1996:LANGA,
author = "Andy Choi",
title = "Optimizing Local Area Networks Using Genetic
Algorithms",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "Genetic Algorithms",
pages = "467--472",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96 GA paper",
}
@InCollection{choi:1995:OLANUGA,
author = "Andy Choi",
title = "Optimizing Local Area Networks Using Genetic
Algorithms",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1995",
year = "1995",
editor = "John R. Koza",
pages = "49--58",
address = "Stanford, California, 94305-3079 USA",
month = "11 " # dec,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-195720-5",
notes = "part of \cite{koza:1995:gagp}",
}
@InCollection{choi:2003:SEGADOLSJUPGMEM,
author = "Seongim Choi",
title = "Speedups for Efficient Genetic Algorithms: Design
Optimization of Low-Boom Supersonic Jet Using Parallel
{GA} and Micro-{GA} with External Memory",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2003",
year = "2003",
editor = "John R. Koza",
pages = "21--30",
address = "Stanford, California, 94305-3079 USA",
month = "4 " # dec,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms",
URL = "http://www.genetic-programming.org/sp2003/Choi.pdf",
notes = "part of \cite{koza:2003:gagp}",
}
@InProceedings{choi:pao:gecco2004,
author = "Sung-Soon Choi and Byung-Ro Moon",
title = "Polynomial Approximation of Survival Probabilities
Under Multi-point Crossover",
booktitle = "Genetic and Evolutionary Computation -- GECCO-2004,
Part I",
year = "2004",
editor = "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",
series = "Lecture Notes in Computer Science",
pages = "994--1005",
address = "Seattle, WA, USA",
publisher_address = "Heidelberg",
month = "26-30 " # jun,
organisation = "ISGEC",
publisher = "Springer-Verlag",
volume = "3102",
ISBN = "3-540-22344-4",
ISSN = "0302-9743",
URL = "http://link.springer.de/link/service/series/0558/bibs/3102/31020994.htm",
size = "12",
keywords = "genetic algorithms, genetic programming",
notes = "GECCO-2004 A joint meeting of the thirteenth
international conference on genetic algorithms
(ICGA-2004) and the ninth annual genetic programming
conference (GP-2004)",
}
@InProceedings{Choi:2010:ICIP,
author = "Wook-Jin Choi and Tae-Sun Choi",
title = "Computer-aided detection of pulmonary nodules using
genetic programming",
booktitle = "17th IEEE International Conference on Image Processing
(ICIP 2010 )",
year = "2010",
month = "26-29 " # sep,
pages = "4353--4356",
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/ICIP.2010.5652369",
ISSN = "1522-4880",
notes = "Sch. of Inf. & Mechatron., Gwangju Inst. of Sci. &
Technol. (GIST), Gwangju, South Korea. Also known as
\cite{5652369}",
}
@MastersThesis{p.chong:mastersthesis,
author = "Fuey Sian Chong",
title = "A Java based Distributed Approach to Genetic
Programming on the Internet",
school = "Computer Science, University of Birmingham",
year = "1998",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.bham.ac.uk/~wbl/ftp/papers/p.chong/p.chong.msc.25-sep-98.ps.gz",
size = "103 pages",
abstract = "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.",
notes = "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",
}
@TechReport{chong:1999:jDGPiTR,
author = "Fuey Sian Chong",
title = "Java based Distributed Genetic Programming on the
Internet",
institution = "University of Birmingham, School of Computer Science",
year = "1999",
number = "CSRP-99-7",
month = apr,
keywords = "genetic algorithms, genetic programming, DGP",
URL = "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1999/CSRP-99-07.ps.gz",
abstract = "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.",
notes = "long version of \cite{chong:1999:jDGPi} Phyllis
Chong",
size = "8 pages",
}
@InProceedings{chong:1999:jDGPi,
author = "Fuey Sian Chong and W. B. Langdon",
title = "Java based Distributed Genetic Programming on the
Internet",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1229",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
note = "Full text in technical report CSRP-99-7",
keywords = "genetic algorithms, genetic programming, DGP,
Distributed Computing, Java Applet / Application, World
Wide Computing, Internet, Servlets, poster",
ISBN = "1-55860-611-4",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/p.chong/DGPposter.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/p.chong/DGPposter.ps.gz",
URL = "http://www.cs.bham.ac.uk/~wbl/ftp/papers/p.chong/DGPposter.ps.gz",
size = "1 page",
abstract = "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.",
notes = "GECCO-99, part of \cite{banzhaf: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 \cite{chong: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",
}
@InProceedings{chong:1999:parGA,
author = "Fuey Sian Chong",
title = "Java based Distributed Genetic Programming on the
Internet",
booktitle = "Evolutionary computation and parallel processing",
year = "1999",
editor = "Erick Cantu-Paz and Bill Punch",
pages = "163--166",
address = "Orlando, Florida, USA",
month = "13 " # jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.bham.ac.uk/~wbl/ftp/papers/p.chong/GeccoWkShop.ps.gz",
size = "4 pages",
notes = "GECCO'99 WKSHOP Phyllis Chong",
}
@Misc{chong:1999:jDGPis,
author = "Fuey Sian Chong and W. B. Langdon",
title = "Java based Distributed Genetic Programming on the
Internet",
booktitle = "GECCO-99 Student Workshop",
year = "1999",
editor = "Una-May O'Reilly",
pages = "345",
address = "Orlando, Florida, USA",
month = "13 " # jul,
keywords = "genetic algorithms, genetic programming, distributed,
evolutionary programming, Internet, java, parallel",
URL = "http://www.cs.bham.ac.uk/~wbl/ftp/papers/p.chong/DGPposter.ps.gz",
abstract = "GECCO'99 graduate WKSHOP Phyllis Chong",
}
@InCollection{chong:2002:GAACG,
author = "Sanders Chong",
title = "Genetic Algorithms Applied to Computational Genomics",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2002",
year = "2002",
editor = "John R. Koza",
pages = "58--64",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms",
URL = "http://www.genetic-programming.org/sp2002/Chong.pdf",
notes = "part of \cite{koza:2002:gagp}",
}
@Article{oai:CiteSeerPSU:421006,
title = "Using Perturbation To Improve Robustness Of Solutions
Generated By Genetic Programming For Robot Learning",
author = "Prabhas Chongstitvatana",
journal = "Journal of Circuits, Systems and Computers",
year = "1999",
volume = "9",
number = "1-2",
pages = "133--143",
publisher = "World Scientific Publishing Company",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.worldscinet.com/123/09/0901n02/S0218126699000128.html",
doi = "doi:10.1142/S0218126699000128",
citeseer-isreferencedby = "oai:CiteSeerPSU:397249;
oai:CiteSeerPSU:59033",
citeseer-references = "oai:CiteSeerPSU:212034; oai:CiteSeerPSU:51923;
oai:CiteSeerPSU:70404; oai:CiteSeerPSU:23925;
oai:CiteSeerPSU:61708; oai:CiteSeerPSU:14506;
oai:CiteSeerPSU:160348; oai:CiteSeerPSU:115106",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:421006",
rights = "unrestricted",
URL = "http://citeseer.ist.psu.edu/421006.html",
abstract = "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.",
notes = "discrete 2D 500x750 simulation, smellLeft,smellRight",
}
@InCollection{choo:2000:EDLBC,
author = "Shou-yen Choo",
title = "Emergence of a Division of Labor in a Bee Colony",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2000",
year = "2000",
editor = "John R. Koza",
pages = "98--107",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "part of \cite{koza:2000:gagp}",
}
@Article{BC-telepar:97,
author = "B. Chopard and Y. Baggi and P. Luthi and J. F. Wagen",
title = "Wave Propagation and Optimal Antenna Layout using a
Genetic Algorithm",
journal = "Speedup",
year = "1997",
volume = "11",
number = "2",
pages = "42--47",
month = nov,
note = "TelePar Conference, EPFL, 1997",
notes = "SPEEDUP Journal speedup@cscs.ch
",
}
@Article{chopard2000,
author = "B. Chopard and O. Pictet and M. Tomassini",
title = "Parallel and distributed evolutionary computation for
financial applications",
journal = "Parallel Algorithms and Applications",
year = "2000",
volume = "15",
pages = "15--36",
keywords = "genetic algorithms, genetic programming",
ISSN = "1063-7192",
notes = "On Saturday, January 01, 2005 this journal was renamed
International Journal of Parallel, Emergent and
Distributed Systems.",
}
@Article{Chou200957,
author = "I-Chun Chou and Eberhard O. Voit",
title = "Recent developments in parameter estimation and
structure identification of biochemical and genomic
systems",
journal = "Mathematical Biosciences",
volume = "219",
number = "2",
pages = "57--83",
year = "2009",
ISSN = "0025-5564",
doi = "doi:10.1016/j.mbs.2009.03.002",
URL = "http://www.sciencedirect.com/science/article/B6VHX-4VXDV4R-2/2/f7f1904f15cf7aa7404c664ae4658ce8",
keywords = "genetic algorithms, genetic programming, Parameter
estimation, Network identification, Inverse modelling,
Biochemical Systems Theory",
abstract = "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.",
notes = "GP included in Survey",
}
@InProceedings{Chou:1998:GC,
author = "Li-Der Chou and Shao-Chi Wang",
title = "Channel assignment using genetic programming in
wireless networks",
booktitle = "Global Telecommunications Conference, 1998. GLOBECOM
98. The Bridge to Global Integration. IEEE",
year = "1998",
volume = "5",
pages = "2664--2668",
address = "Sydney, NSW, Australia",
month = "8-12 " # nov,
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-4984-9",
doi = "doi:10.1109/GLOCOM.1998.776469",
abstract = "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",
notes = "INSPEC Accession Number: 6430014
Dept. of Comput. Sci. & Inf. Eng., Nat. Central Univ.,
Chung-Li;",
}
@InProceedings{chouza09:_passiv_analog_filter_desig_using,
author = "Mariano Chouza and Claudio Rancan and Osvaldo Clua and
and Ramon Garcia-Martinez",
title = "Passive Analog Filter Design Using {GP} Population
Control Strategies",
booktitle = "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",
pages = "153--158",
year = "2009",
editor = "Been-Chian Chien and Tzung-Pei Hong",
volume = "214",
series = "Studies in Computational Intelligence",
publisher = "Springer-Verlag",
}
publisher_address = {Berlin},
keywords = {genetic algorithms, genetic programming},
ISBN13 = {978-3-540-92813-3},
url = {http://www.iidia.com.ar/rgm/articulos/CIS-214-153-158.pdf},
doi = {doi:10.1007/978-3-540-92814-0_24},
abstract = {This paper presents the use of two different
strategies for genetic programming (GP) population growth control:
decreasing the computational effort by plagues and dynamic
adjustment of fitness; applied to passive analog filters design
based on general topologies. Obtained experimental results show that
proposed strategies improve the design process performance.},
)
@InProceedings{christensen:2002:EuroGP,
title = "An Analysis of {Koza}'s Computational Effort Statistic
for Genetic Programming",
author = "Steffen Christensen and Franz Oppacher",
editor = "James A. Foster and Evelyne Lutton and Julian Miller
and Conor Ryan and Andrea G. B. Tettamanzi",
booktitle = "Genetic Programming, Proceedings of the 5th European
Conference, EuroGP 2002",
volume = "2278",
series = "LNCS",
pages = "182--191",
publisher = "Springer-Verlag",
address = "Kinsale, Ireland",
publisher_address = "Berlin",
month = "3-5 " # apr,
year = "2002",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-43378-3",
URL = "http://link.springer-ny.com/link/service/series/0558/bibs/2278/22780182.htm",
URL = "http://link.springer-ny.com/link/service/series/0558/papers/2278/22780182.pdf",
abstract = "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.",
notes = "EuroGP'2002, part of \cite{lutton:2002:GP}",
}
@InProceedings{Christensen:2006:CEC,
author = "Steffen Christensen and Franz Oppacher",
title = "The {Y}-Test: Fairly Comparing Experimental Setups
with Unequal Effort",
booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary
Computation",
year = "2006",
editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and
Simon M. Lucas",
pages = "1060--1065",
address = "Vancouver",
month = "6-21 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-9487-9",
size = "6 pages",
abstract = "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.",
notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
the IEE.
IEEE Catalog Number: 06TH8846D IEEE Xplore gives pages
= {"}356--361{"},",
}
@InProceedings{1277275,
author = "Steffen Christensen and Franz Oppacher",
title = "Solving the artificial ant on the Santa Fe trail
problem in 20,696 fitness evaluations",
booktitle = "GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation",
year = "2007",
editor = "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",
volume = "2",
isbn13 = "978-1-59593-697-4",
pages = "1574--1579",
address = "London",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1574.pdf",
doi = "doi:10.1145/1276958.1277275",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
month = "7-11 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming,
representation, runtime analysis, speedup technique",
abstract = "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.",
notes = "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",
}
@PhdThesis{Christensen:thesis,
author = "Steffen Moffatt Christensen",
title = "Towards scalable genetic programming",
year = "2007",
school = "Carleton University",
address = "Ottawa, Canada",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-0-494-23290-3",
order_no = "AAINR23290",
abstract = "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.",
notes = "http://portal.acm.org/citation.cfm?id=1292850
http://www.tamale.uottawa.ca/winter2007/300107.html
Tuesday, Jan. 30, 2007 Also known as \cite{1292850},",
}
@InProceedings{chu:1999:DDCNDAGAA,
author = "Chao-Hsien Chu and G. Premkumar and Carey Chou and
Jianzhong Sun",
title = "Dynamic Degree Constrained Network Design: {A} Genetic
Algorithm Approach",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "141--148",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-846.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-846.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{Chu:2008:cec,
author = "Dominique Chu and Jonathan E. Rowe",
title = "Crossover Operators to Control Size Growth in Linear
{GP} and Variable Length {GA}s",
booktitle = "2008 IEEE World Congress on Computational
Intelligence",
year = "2008",
editor = "Jun Wang",
address = "Hong Kong",
month = "1-6 " # jun,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-1823-7",
file = "EC0096.pdf",
abstract = "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.",
keywords = "genetic algorithms, genetic programming",
notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
EPS and the IET.",
}
@Article{Chuang:2003:UMB,
author = "Louise L. Chuang and Jeng-Yang Hwang and Been Chian
Chien and Jung Yi Lin and Chiung Hsin Chang and Chen
Hsiang Yu and Fong Ming Chang",
title = "Predicting fetal birth weight by ultrasound with the
use of genetic programming",
journal = "Ultrasound in Medicine \& Biology",
year = "2003",
volume = "29",
pages = "S163--S163",
number = "5, Supplement 1",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6TD2-48KKXMV-R0/2/b03751f18c26cc039779c29a58106151",
month = may,
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1016/S0301-5629(03)00653-7",
notes = "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",
}
@InProceedings{Ciesielski:1999:AJ,
author = "Victor Ciesielski and Peter Wilson",
title = "Developing a team of soccer playing robots by genetic
programming",
booktitle = "Proceedings of The Third Australia-Japan Joint
Workshop on Intelligent and Evolutionary Systems",
year = "1999",
editor = "Bob McKay and Yasuhiro Tsujimura and Ruhul Sarker and
Akira Namatame and Xin Yao and Mitsuo Gen",
pages = "101--108",
address = "School of Computer Science Australian Defence Force
Academy, Canberra, Australia",
month = "22-25 " # nov,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.rmit.edu.au/~vc/papers/aus-jap-ec99.ps.gz",
size = "8 pages",
notes = "http://www.cs.adfa.edu.au/archive/conference/aj99/programme.html",
}
@InProceedings{ciesielski:2002:poecigpbrosp,
author = "Vic Ciesielski and Dylan Mawhinney",
title = "Prevention of Early Convergence in Genetic Programming
by Replacement of Similar Programs",
booktitle = "Proceedings of the 2002 Congress on Evolutionary
Computation CEC2002",
editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
and Garry Greenwood and Hitoshi Iba and Paul Marrow and
Mark Shackleton",
pages = "67--72",
year = "2002",
publisher = "IEEE Press",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
organisation = "IEEE Neural Network Council (NNC), Institution of
Electrical Engineers (IEE), Evolutionary Programming
Society (EPS)",
ISBN = "0-7803-7278-6",
month = "12-17 " # may,
notes = "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 \cite{oai:CiteSeerPSU:451316}",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{Ciesielski:2002:GPR,
author = "Vic Ciesielski and Dylan Mawhinney and Peter Wilson",
title = "Genetic Programming for Robot Soccer",
booktitle = "RoboCup 2001: Robot Soccer World Cup V",
year = "2002",
volume = "2377",
pages = "319--324",
editor = "Andreas Birk and Silvia Coradeschi and Satoshi
Tadokoro",
series = "Lecture Notes in Computer Science",
address = "Seattle, Washington, USA",
month = aug # " 2001",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-43912-9",
ISSN = "0302-9743",
CODEN = "LNCSD9",
ISSN = "0302-9743",
bibdate = "Tue Sep 10 19:09:58 MDT 2002",
URL = "http://link.springer-ny.com/link/service/series/0558/bibs/2377/23770319.htm",
URL = "http://link.springer-ny.com/link/service/series/0558/papers/2377/23770319.pdf",
acknowledgement = ack-nhfb,
abstract = "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.",
}
@InProceedings{ciesielski:2003:psfsfdpqigp,
author = "Vic Ciesielski and Xiang Li",
title = "Pyramid search: Finding solutions for deceptive
problems quickly in genetic programming",
booktitle = "Proceedings of the 2003 Congress on Evolutionary
Computation CEC2003",
editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass
and Kay Chen Tan and Bob McKay and Daryl Essam and Tom
Gedeon",
pages = "936--943",
year = "2003",
publisher = "IEEE Press",
address = "Canberra",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "8-12 " # dec,
organisation = "IEEE Neural Network Council (NNC), Engineers Australia
(IEAust), Evolutionary Programming Society (EPS),
Institution of Electrical Engineers (IEE)",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-7804-0",
abstract = "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.",
notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust,
the EPS, and the IEE.",
}
@InProceedings{ciesielski:2004:ewefigp,
title = "Experiments with Explicit For-loops in Genetic
Programming",
author = "Vic Ciesielski and Xiang Li",
pages = "494--501",
booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary
Computation",
year = "2004",
publisher = "IEEE Press",
month = "20-23 " # jun,
address = "Portland, Oregon",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming, Theory of
evolutionary algorithms",
abstract = "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.",
notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and
the IEE.",
}
@InProceedings{eurogp:CiesielskiIJM05,
author = "Victor Ciesielski and Andrew Innes and Sabu John and
John Mamutil",
editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre
Collet and Jano I. {van Hemert} and Marco Tomassini",
title = "Understanding Evolved Genetic Programs for a Real
World Object Detection Problem",
booktitle = "Proceedings of the 8th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3447",
year = "2005",
address = "Lausanne, Switzerland",
month = "30 " # mar # " - 1 " # apr,
organisation = "EvoNet",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-25436-6",
pages = "351--360",
URL = "http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=351",
doi = "doi:10.1007/b107383",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in
conjunction with EvoCOP2005 and EvoWorkshops2005",
}
@InProceedings{Ciesielski:2006:CEC,
author = "Vic Ciesielski and Gayan Wijesinghe and Andrew Innes
and Sabu John",
title = "Analysis of the Superiority of Parameter Optimization
over Genetic Programming for a Difficult Object
Problem",
booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary
Computation",
year = "2006",
editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and
Simon M. Lucas",
pages = "4407--4414",
address = "Vancouver",
month = "6-21 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-9487-9",
size = "8 pages",
abstract = "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.",
notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
the IEE.
IEEE Catalog Number: 06TH8846D IEEE Xplore gives pages
= {"}1264--1271{"},",
}
@InProceedings{eurogp07:ciesielski,
author = "Vic Ciesielski and Xiang Li",
title = "Data Mining of Genetic Programming Run Logs",
editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and
Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar",
booktitle = "Proceedings of the 10th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "4445",
year = "2007",
address = "Valencia, Spain",
month = "11-13 " # apr,
pages = "281--290",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-71602-5",
isbn13 = "978-3-540-71602-0",
doi = "doi:10.1007/978-3-540-71605-1_26",
abstract = "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.",
notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in
conjunction with EvoCOP2007, EvoBIO2007 and
EvoWorkshops2007",
}
@Article{Ciesielski:2008:GPEM,
author = "Vic Ciesielski",
title = "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",
journal = "Genetic Programming and Evolvable Machines",
year = "2008",
volume = "9",
number = "1",
pages = "105--106",
month = mar,
keywords = "genetic algorithms, genetic programming",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-007-9036-8",
size = "2 pages",
notes = "book review of \cite{Brameier:2006:book}",
}
@Article{Ciftci:2009:EAEI,
author = "Ozan Nazim Ciftci and Sibel Fadiloglu and Fahrettin
Gogus and Aytac Guven",
title = "Genetic programming approach to predict a model
acidolysis system",
journal = "Engineering Applications of Artificial Intelligence",
year = "2009",
volume = "22",
pages = "759--766",
number = "4-5",
keywords = "genetic algorithms, genetic programming,
Gene-expression programming, Acidolysis",
doi = "doi:10.1016/j.engappai.2009.01.010",
ISSN = "0952-1976",
URL = "http://www.sciencedirect.com/science/article/B6V2M-4VTVJNC-2/2/5894a9c11ade2e94a1ff09a18b63a062",
abstract = "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.",
}
@Article{Cilibrasi:2004:CMJ,
author = "Rudi Cilibrasi and Paul Vitanyi and Ronald {de Wolf}",
title = "Algorithmic Clustering of Music Based on String
Compression",
journal = "Computer Music Journal",
year = "2004",
volume = "28",
number = "4",
pages = "49--67",
month = "Winter",
keywords = "genetic algorithms, genetic programming, complearn",
URL = "http://homepages.cwi.nl/~paulv/papers/music.pdf",
size = "19 pages",
abstract = "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.",
notes = "C 2004 Massachusetts Institute of Technology
Earlier version at cs.SD/0303025
http://arxiv.org/abs/cs.SD/0303025",
}
@Misc{cs.CL/0412098,
author = "Rudi Cilibrasi and Paul M. B. Vitanyi",
title = "Automatic Meaning Discovery Using Google",
year = "2005",
number = "cs.CL/0412098",
month = "15 " # mar,
note = "v2",
keywords = "genetic algorithms, genetic programming, randomised
hill-climbing, SVM, support vector machines, complearn,
Computation and Language, Artificial Intelligence,
Databases, Information Retrieval, Learning",
URL = "http://www.arxiv.org/abs/cs.CL/0412098",
URL = "http://homepages.cwi.nl/~paulv/papers/amdug.pdf",
abstract = "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.",
notes = "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 \cite{graham-rowe:2005:complearn}
Code http://www.complearn.org/",
size = "31 pages",
}
@Article{Cilibrasi:2005:ITIT,
author = "Rudi Cilibrasi and Paul M. B. Vitanyi",
title = "Clustering by Compression",
journal = "IEEE Transactions on Information Theory",
year = "2005",
volume = "51",
number = "4",
pages = "1523--1545",
month = apr,
keywords = "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",
URL = "http://homepages.cwi.nl/~paulv/papers/cluster.pdf",
size = "21 pages",
abstract = "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.",
notes = "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).{"}",
}
@InProceedings{Cilibrasi:2005:pascal,
author = "Rudi Cilibrasi and Paul Vitanyi",
title = "A New Quartet Tree Heuristic for Hierarchical
Clustering",
booktitle = "Principled methods of trading exploration and
exploitation Workshop",
year = "2005",
address = "London",
month = "6-7 " # jul,
organisation = "PASCAL",
keywords = "genetic algorithms, genetic programming,
Computational, Information-Theoretic Learning with
Statistics, Learning/Statistics, Optimisation, Theory,
Algorithms",
URL = "http://www.cwi.nl/~paulv/papers/quartet.pdf",
size = "22 pages",
abstract = "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.",
notes = "http://eprints.pascal-network.org/archive/00001821/
paper improved after workshop?",
}
@InProceedings{cilibrasi_et_al:DSP:2006:598,
author = "Rudi Cilibrasi and Paul M. B. Vitany",
title = "A New Quartet Tree Heuristic for Hierarchical
Clustering",
booktitle = "Theory of Evolutionary Algorithms",
year = "2006",
editor = "Dirk V. Arnold and Thomas Jansen and Michael D. Vose
and Jonathan E. Rowe",
number = "06061",
series = "Dagstuhl Seminar Proceedings",
ISSN = "1862-4405",
publisher = "Internationales Begegnungs- und Forschungszentrum fuer
Informatik (IBFI), Schloss Dagstuhl, Germany",
address = "Dagstuhl, Germany",
URL = "http://drops.dagstuhl.de/opus/volltexte/2006/598/pdf/06061.VitanyiPaulB.Paper.598.pdf",
URL = "http://drops.dagstuhl.de/opus/volltexte/2006/598",
note = "$<$http://drops.dagstuhl.de/opus/volltexte/2006/598$>$
[date of citation: 2006-01-01]",
month = "5-10 " # feb,
keywords = "genetic algorithms, genetic programming, hierarchical
clustering, quartet tree method",
size = "13 pages",
abstract = "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.",
}
@PhdThesis{Cilibrasi:thesis,
author = "Rudi Langston Cilibrasi",
title = "Statistical Inference Through Data Compression",
school = "Institute for Logic, Language and Computation,
Universiteit van Amsterdam",
year = "2007",
address = "Plantage Muidergracht 24, 1018 TV, Amsterdam,
Holland",
month = "23 " # feb,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.illc.uva.nl/Research/Dissertations/DS-2007-01.text.pdf",
URL = "http://www.lulu.com/shop/search.ep?contributorId=254359",
size = "225 pages",
ISBN = "90-6196-540-3",
abstract = "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.",
notes = "p51 'Our algorithm is essentially randomized
hill-climbing, using parallellized Genetic
Programming,' Promotor: Prof.dr.ir. P.M.B. Vitanyi
(CWI)",
}
@InCollection{Citi:2009:GPTP,
author = "Luca Citi and Riccardo Poli and Caterina Cinel",
title = "High-significance Averages of Event-Related Potential
via Genetic Programming",
booktitle = "Genetic Programming Theory and Practice {VII}",
year = "2009",
editor = "Rick L. Riolo and Una-May O'Reilly and Trent
McConaghy",
series = "Genetic and Evolutionary Computation",
address = "Ann Arbor",
month = "14-16 " # may,
publisher = "Springer",
chapter = "9",
pages = "135--157",
keywords = "genetic algorithms, genetic programming, Event-related
potentials, Register-based GP, Memory-with-Memory",
notes = "part of \cite{Riolo:2009:GPTP}",
}
@TechReport{clack:1996:adca,
author = "Chris Clack and Jonny Farringdon and Peter Lidwell and
Tina Yu",
title = "An Adaptive Document Classification Agent",
institution = "University College London",
year = "1996",
type = "Research Note",
number = "RN/96/45",
address = "Computer Science, Gower Street, London, WC1E 6BT, UK",
month = "21 " # jun,
note = "Submitted to BCS-ES96",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/research/rns/rns96.html",
abstract = "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.",
notes = "3 figures as separte ps files in the same directory",
}
@TechReport{clack:1996:rn38,
author = "Chris D. Clack and S. J. Gould and Peter R. Lidwell
and Janet T. McDonnell",
title = "Advanced Technology Support for Information Management
at Friends of the Earth",
institution = "University College London",
year = "1996",
type = "Research Note",
number = "RN/96/48",
address = "Computer Science, Gower Street, London, WC1E 6BT, UK",
keywords = "genetic algorithms, genetic programming",
URL = "ftp://bells.cs.ucl.ac.uk/functional/papers/Published/rn_96_38pagenums.pdf.gz",
URL = "http://citeseer.ist.psu.edu/92899.html",
abstract = "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.",
size = "6 pages",
}
@TechReport{clack:1996:adcb,
author = "Chris Clack and Jonny Farringdon and Peter Lidwell and
Tina Yu",
title = "Autonomous Document Classification for Business",
institution = "University College London",
year = "1996",
type = "Research Note",
number = "RN/96/48",
address = "Computer Science, Gower Street, London, WC1E 6BT, UK",
month = jun,
note = "Appears in Autonomous Agents '97",
keywords = "genetic algorithms, genetic programming, Softbot,
agent architecture, pattern recognition, long term
adaptation and learning",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/clack_1997_adcb.pdf",
abstract = "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.",
notes = "see also \cite{clack:1997:adcb}",
size = "8 pages",
}
@InProceedings{clack:1997:adcb,
author = "Chris Clack and Jonny Farringdon and Peter Lidwell and
Tina Yu",
title = "Autonomous Document Classification for Business",
booktitle = "The First International Conference on Autonomous
Agents (Agents '97)",
year = "1997",
editor = "W. Lewis Johnson",
pages = "201--208",
address = "Marina del Rey, California, USA",
publisher_address = "1515 Broadway, New York, NY 10036, USA",
month = feb # " 5-8",
organisation = "ACM SIGART",
publisher = "ACM Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-89791-877-0",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/clack_1997_adcb.pdf",
doi = "doi:10.1145/267658.267716",
size = "11 pages",
abstract = "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.",
notes = "http://www.isi.edu/isd/AA97/info.html see also
\cite{clack:1996:adcb}",
}
@TechReport{clack:1997:edc,
author = "Chris Clack",
title = "Software -- The Next Generation: Evolving Document
Classification",
institution = "UCL, Andersen Consulting",
year = "1997",
type = "white paper",
address = "University College London, Gower Street, London",
month = apr,
keywords = "genetic algorithms, genetic programming",
pages = "55--67",
notes = "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.",
size = "13 pages",
}
@InCollection{clark:1995:PISW,
author = "Adam Clark",
title = "Predator-Prey Interactions in a Simulated World",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1995",
year = "1995",
editor = "John R. Koza",
pages = "59--64",
address = "Stanford, California, 94305-3079 USA",
month = "11 " # dec,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-195720-5",
notes = "part of \cite{koza:1995:gagp}",
}
@MastersThesis{cleary:2005:EGEWAGAATKP,
title = "Extending Grammatical Evolution with Attribute
Grammars: An Application to Knapsack Problems",
author = "Robert Cleary",
school = "University of Limerick",
year = "2005",
type = "Master of Science in Computer Science",
address = "University of Limerick, Ireland",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, grammatical swarm, attribute grammars",
URL = "http://ncra.ucd.ie/downloads/pub/thesisExtGEwithAGs-CRC.pdf",
size = "197 pages",
abstract = "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.",
language = "en",
}
@InProceedings{cleary:2005:AAGDFR0MKP,
author = "Robert Cleary and Michael O'Neill",
title = "An Attribute Grammar Decoder for the 01
MultiConstrained Knapsack Problem",
booktitle = "Evolutionary Computation in Combinatorial Optimization
-- {EvoCOP}~2005",
year = "2005",
month = "30 " # mar # "-1 " # apr,
editor = "G{\"{u}}nther R. Raidl and Jens Gottlieb",
series = "LNCS",
volume = "3448",
publisher = "Springer Verlag",
address = "Lausanne, Switzerland",
publisher_address = "Berlin",
pages = "34--45",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, evolutionary computation, attribute
grammar",
ISSN = "0302-9743",
abstract = "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.",
notes = "EvoCOP2005 Also known as \cite{cleary:evocop05}",
}
@InProceedings{1277276,
author = "Janet Clegg and James Alfred Walker and Julian Francis
Miller",
title = "A new crossover technique for Cartesian genetic
programming",
booktitle = "GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation",
year = "2007",
editor = "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",
volume = "2",
isbn13 = "978-1-59593-697-4",
pages = "1580--1587",
address = "London",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1580.pdf",
doi = "doi:10.1145/1276958.1277276",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
month = "7-11 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, Cartesian
Genetic Programming, crossover techniques,
optimisation",
abstract = "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.",
notes = "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",
}
@InProceedings{Clegg:2008:gecco,
author = "Janet Clegg",
title = "Combining cartesian genetic programming with an
estimation of distribution algorithm",
booktitle = "GECCO '08: Proceedings of the 10th annual conference
on Genetic and evolutionary computation",
year = "2008",
editor = "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",
isbn13 = "978-1-60558-130-9",
pages = "1333--1334",
address = "Atlanta, GA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1333.pdf",
doi = "doi:10.1145/1389095.1389350",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "12-16 " # jul,
keywords = "genetic algorithms, genetic programming, cartesian
genetic programming, crossover techniques,
optimisation: Poster",
notes = "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 \cite{1389350}",
}
@InProceedings{conf/evoW/CleggS08,
title = "Analogue Circuit Control through Gene Expression",
author = "Kester Clegg and Susan Stepney",
bibdate = "2008-04-15",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/evoW/evoW2008.html#CleggS08",
booktitle = "Proceedings of Evo{COMNET}, Evo{FIN}, Evo{HOT},
Evo{IASP}, Evo{MUSART}, Evo{NUM}, Evo{STOC}, and
EvoTransLog, Applications of Evolutionary Computing,
EvoWorkshops",
publisher = "Springer",
year = "2008",
volume = "4974",
editor = "Mario Giacobini and Anthony Brabazon and Stefano
Cagnoni and Gianni {Di Caro} and Rolf Drechsler and
Anik{\'o} Ek{\'a}rt and Anna Esparcia-Alc{\'a}zar 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",
isbn13 = "978-3-540-78760-0",
pages = "154--163",
series = "Lecture Notes in Computer Science",
doi = "doi:10.1007/978-3-540-78761-7_16",
address = "Naples",
month = "26-28 " # mar,
keywords = "genetic algorithms, genetic programming, Cartesian
genetic programming",
abstract = "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).",
}
@PhdThesis{Clegg:thesis,
author = "Kester Dean Clegg",
title = "Evolving gene expression to reconfigure analogue
devices",
school = "University of York",
year = "2008",
month = May,
keywords = "genetic algorithms, genetic programming, Cartesian
genetic programming",
URL = "http://www-users.cs.york.ac.uk/susan/teach/theses/clegg.htm",
URL = "http://www.cs.york.ac.uk/ftpdir/reports/2008/YCST/05/YCST-2008-05.pdf",
size = "203 pages",
abstract = "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.",
}
@InProceedings{Clemente:evoapps12,
author = "Eddie Clemente and Gustavo Olague and Leon Dozal and
Martin Mancilla",
title = "Object Recognition with an Optimized Visual Cortex
Model using Genetic Programming",
booktitle = "Applications of Evolutionary Computing,
EvoApplications 2012: EvoCOMNET, EvoCOMPLEX, EvoFIN,
EvoGAMES, EvoHOT, EvoIASP, EvoNUM, EvoPAR, EvoRISK,
EvoSTIM, EvoSTOC",
year = "2011",
month = "11-13 " # apr,
editor = "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",
series = "LNCS",
volume = "7248",
publisher = "Springer Verlag",
address = "Malaga, Spain",
publisher_address = "Berlin",
pages = "315--325",
organisation = "EvoStar",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-29177-7",
doi = "doi:10.1007/978-3-642-29178-4_32",
abstract = "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.",
notes = "EvoIASP Part of \cite{DiChio:2012:EvoApps}
EvoApplications2012 held in conjunction with
EuroGP2012, EvoCOP2012, EvoBio'2012 and EvoMusArt2012",
}
@InProceedings{clergue:2002:gecco,
author = "Manuel Clergue and Philippe Collard and Marco
Tomassini and Leonardo Vanneschi",
title = "Fitness Distance Correlation And Problem Difficulty
For Genetic Programming",
booktitle = "GECCO 2002: Proceedings of the Genetic and
Evolutionary Computation Conference",
editor = "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",
year = "2002",
pages = "724--732",
address = "New York",
publisher_address = "San Francisco, CA 94104, USA",
month = "9-13 " # jul,
publisher = "Morgan Kaufmann Publishers",
keywords = "genetic algorithms, genetic programming, distance
between genotypes, fitness distance correlation,
problem difficulty, royal trees, trap functions",
ISBN = "1-55860-878-8",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/GP072.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/GP072.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf",
notes = "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.",
}
@InProceedings{coates:1997:GPx3dw,
author = "T. Broughton and A. Tan and Paul S. Coates",
title = "The use of Genetic programing in Exploring 3{D} Design
Worlds",
booktitle = "CAAD Futures 97",
year = "1997",
editor = "Richard Junge",
pages = "885--917",
address = "Technical University Munich, Germany",
month = "4-6 " # aug,
publisher = "Kluwer Academic Publishers",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7923-4726-9",
URL = "http://www.caadfutures.org/proceedings_97.htm",
URL = "http://roar.uel.ac.uk/jspui/bitstream/10552/854/1/Broughton%2c%20T%20%281997%29%20CAAD%20Futures%20pp.%20885.pdf",
size = "32 pages",
abstract = "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.",
notes = "University of East London, GB",
}
@InProceedings{Coates:1999:AISBces,
author = "Paul Coates and Dimitrios Makris",
title = "Genetic Programming and Spatial Morphogenesis",
booktitle = "AISB Symposium on Creative Evolutionary Systems",
year = "1999",
pages = "105--114",
address = "Edinburgh College of Art and Division of Informatics,
University of Edinburgh",
publisher_address = "COGS, University of Sussex",
month = "6-9 " # apr,
organisation = "AISB",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-902956-03-6",
URL = "http://www.aisb.org.uk/publications/proceedings/proc1999/aisb1999/AISB99_Evolutionary.pdf",
notes = "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",
}
@Book{Coates:2010:PA,
author = "Paul Coates",
title = "Programming.Architecture",
publisher = "Routledge",
year = "2010",
month = jan # " 29th",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-0-415-45188-8",
URL = "http://www.routledge.com/books/details/9780415451888/",
abstract = "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",
notes = "Reviewed by \cite{Medjdoub:2011:GPEM}",
size = "200 pages",
}
@Article{Coelho2010494,
author = "Andre L. V. Coelho and Everlandio Fernandes and Katti
Faceli",
title = "Inducing multi-objective clustering ensembles with
genetic programming",
journal = "Neurocomputing",
volume = "74",
number = "1-3",
pages = "494--498",
year = "2010",
note = "Artificial Brains",
ISSN = "0925-2312",
doi = "doi:10.1016/j.neucom.2010.09.014",
URL = "http://www.sciencedirect.com/science/article/B6V10-517YN4X-P/2/7322b78e25061d5ecbaa12f058216cd0",
keywords = "genetic algorithms, genetic programming, Cluster
analysis, Ensembles, Multi-objective optimization",
abstract = "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.",
}
@Article{Coelho2011,
author = "Andre L. V. Coelho and Everlandio Fernandes and Katti
Faceli",
title = "Multi-objective design of hierarchical consensus
functions for clustering ensembles via genetic
programming",
journal = "Decision Support Systems",
note = "In Press, Corrected Proof",
year = "2011",
ISSN = "0167-9236",
doi = "doi:10.1016/j.dss.2011.01.014",
URL = "http://www.sciencedirect.com/science/article/B6V8S-5230PKR-5/2/797124c3ee3c0a2f623dd92203d4042a",
keywords = "genetic algorithms, genetic programming, Cluster
analysis, Clustering ensembles, Multi-objective
clustering, Hierarchical fusion, Partition selection",
abstract = "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.",
}
@InProceedings{Coelho:2009:ICCI,
author = "Lucio Coelho and Ben Goertzel and Cassio Pennachin and
Chris Heward",
title = "Classifier ensemble based analysis of a genome-wide
{SNP} dataset concerning Late-Onset Alzheimer Disease",
booktitle = "8th IEEE International Conference on Cognitive
Informatics, ICCI '09",
year = "2009",
month = jun,
pages = "469--475",
keywords = "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",
doi = "doi:10.1109/COGINF.2009.5250695",
abstract = "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.",
notes = "Also known as \cite{5250695}",
}
@Article{coello:2004:GPEM,
author = "Carlos A. {Coello Coello} and Nareli Cruz Cortes",
title = "Solving Multiobjective Optimization Problems Using an
Artificial Immune System",
journal = "Genetic Programming and Evolvable Machines",
year = "2005",
volume = "6",
number = "2",
pages = "163--190",
month = jun,
keywords = "AIS, artificial immune system, multiobjective
optimization, clonal selection",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-005-6164-x",
abstract = "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.",
}
@MastersThesis{Coia:mastersthesis,
author = "Corrado Coia",
title = "Automatic Evolution of Conceptual Building
Architectures",
school = "Brock University",
year = "2011",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{Coia:2011:AEoCBA,
title = "Automatic Evolution of Conceptual Building
Architectures",
author = "Corrado Coia and Brian Ross",
pages = "1145--1152",
booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary
Computation",
year = "2011",
editor = "Alice E. Smith",
month = "5-8 " # jun,
address = "New Orleans, USA",
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming, Real-world
applications, Art and music",
abstract = "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.",
notes = "CEC2011 sponsored by the IEEE Computational
Intelligence Society, and previously sponsored by the
EPS and the IET.",
}
@Article{Colak2007657,
author = "Oguz Colak and Cahit Kurbanoglu and M. Cengiz
Kayacan",
title = "Milling surface roughness prediction using
evolutionary programming methods",
journal = "Materials \& Design",
volume = "28",
number = "2",
pages = "657--666",
year = "2007",
ISSN = "0261-3069",
doi = "DOI:10.1016/j.matdes.2005.07.004",
URL = "http://www.sciencedirect.com/science/article/B6TX5-4GYNXVH-3/2/9f33fbb56f37b01600d2773bc207696f",
keywords = "genetic algorithms, genetic programming, gene
expression programming, Surface roughness, CNC end
milling, Genetic expression programming",
abstract = "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.",
}
@InProceedings{DBLP:conf/itng/Coleman06,
author = "Ron Coleman",
title = "Boosting Blackjack Returns with Machine Learned
Betting Criteria",
year = "2006",
publisher = "IEEE Computer Society",
booktitle = "Third International Conference on Information
Technology: New Generations (ITNG 2006)",
pages = "669--673",
address = "Las Vegas, Nevada, USA",
month = "10-12 " # apr,
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7695-2497-4",
bibsource = "DBLP, http://dblp.uni-trier.de",
doi = "doi:10.1109/ITNG.2006.40",
bibsource = "DBLP, http://dblp.uni-trier.de",
}
@InProceedings{coletti:1999:CPLEMGA,
author = "Mark Coletti and Thomas D. Lash and Ryszard Michalski
and Craig Mandsager and Rida Moustafa",
title = "Comparing Performance of the Learnable Evolution Model
and Genetic Algorithms",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "779",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems, poster
papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-386.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-386.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@Article{colin:1997:DMGP,
author = "Andre Colin",
title = "Data-Mining and Genetic Programming",
journal = "PC AI",
year = "1997",
volume = "11",
number = "5",
pages = "23",
month = sep # "/" # oct,
publisher = "Knowledge Technology, Inc.",
address = "Phoenix, AZ, USA",
email = "acolin@zurich.com.au",
keywords = "genetic algorithms, genetic programming, data mining",
ISSN = "0894-0711",
URL = "http://www.pcai.com/web/issues/pcai_11_5_toc.html",
size = "3 pages",
abstract = "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.",
notes = "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",
}
@InProceedings{collet:1999:IGAVRCP,
author = "Pierre Collet and Evelyne Lutton and Frederic Raynal
and Marc Schoenauer",
title = "Individual {GP}: an Alternative Viewpoint for the
Resolution of Complex Problems",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "974--981",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, IFS,
fractals",
ISBN = "1-55860-611-4",
URL = "http://minimum.inria.fr/evo-lab/Publications/GP-467.ps.gz",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-467.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-467.ps",
abstract = "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.",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@TechReport{collet:1999:RR-3849,
author = "Pierre Collet and Evelyne Lutton and Frederic Raynal
and Marc Schoenauer",
title = "Polar {IFS} + Individual Genetic Programming =
Efficient {IFS} Inverse Problem Solving",
institution = "INRIA",
year = "1999",
number = "RR-3849",
address = "Domaine de Voluceau - Rocquencourt - B.P. 105 78153 Le
Chesnay Cedex France",
month = dec,
keywords = "genetic algorithms, genetic programming",
URL = "http://minimum.inria.fr/evo-lab/Publications/RR-PolarIFS.ps.gz",
abstract = "The inverse problem for Iterated Functions Systems
(finding an IFS whose attractor is a target 2D shape)
with non-affine IFS is a very complex task. Successful
approaches have been made using Genetic Programming,
but there is still room for improvement in both the IFS
and the GP parts. The main difficulty with non-linear
IFS is the efficient handling of contractance
constraints. This paper introduces Polar IFS, a
specific representation of IFS functions that shrinks
the search space to mostly contractive functions.
Moreover, the Polar representation gives direct access
to the fixed points of the functions, whereas the fixed
point of general non-linear IFS can only be numerically
estimated. On the evolutionary side, the
{"}individual{"} approach is similar to the Michigan
approach of Classifier Systems: each individual of the
population embodies a single function rather than the
whole IFS. A solution to the inverse problem is then
built from a set of individuals. Both improvements show
a drastic cut-down on CPU-time: good results are
obtained with small populations in few generations.",
abstract = "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\c{c}on 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.",
notes = "in english",
size = "30 pages",
}
@InProceedings{ColletPPSN2000,
author = "Pierre Collet and Evelyne Lutton and Marc Schoenauer
and Jean Louchet",
title = "Take it {EASEA}",
booktitle = "Parallel Problem Solving from Nature - PPSN VI 6th
International Conference",
editor = "Marc Schoenauer and Kalyanmoy Deb and G{\"u}nter
Rudolph and Evelyne Lutton Xin Yao and Juan Julian
Merelo and Hans-Paul Schwefel",
year = "2000",
publisher = "Springer-Verlag",
address = "Paris, France",
month = sep # " 16-20",
volume = "1917",
series = "LNCS",
pages = "891--901",
keywords = "genetic algorithms, genetic programming",
URL = "http://minimum.inria.fr/evo-lab/Publications/PPSNVI.ps.gz",
abstract = "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.",
notes = "online
(http://minimum.inria.fr/evo-lab/Publications/PPSNVI.ps.gz)
not identical format to published",
}
@Article{collet:2000:IFSpGP,
author = "Pierre Collet and Evelyne Lutton and Frederic Raynal
and Marc Schoenauer",
title = "Polar {IFS}+Parisian Genetic Programming=Efficient
{IFS} Inverse Problem Solving",
journal = "Genetic Programming and Evolvable Machines",
year = "2000",
volume = "1",
number = "4",
pages = "339--361",
month = oct,
keywords = "genetic algorithms, genetic programming, fractals,
Iterated Functions System, inverse problem for IFS,
polar IFS",
ISSN = "1389-2576",
URL = "http://minimum.inria.fr/evo-lab/Publications/PolarIFS-GPEM-New.ps.gz",
URL = "http://www.lri.fr/~marc/EEAAX/papers/marc/gpem2000.ps.gz",
doi = "doi:10.1023/A:1010065123132",
URL = "http://citeseer.ist.psu.edu/374242.html",
abstract = "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.",
notes = "Article ID: 273811",
}
@TechReport{collet:2001:RR4421,
author = "Pierre Collet and Marc Schoenauer and Evelyne Lutton
and Jean Louchet",
title = "{EASEA} : un langage de specification pour les
algorithmes evolutionnaires",
institution = "INRIA",
year = "2001",
number = "RR4218",
address = "Domaine de Voluceau - Rocquencourt - B.P. 105 78153 Le
Chesnay Cedex France",
month = jun,
keywords = "genetic algorithms, genetic programming, EASEA, Java",
URL = "ftp://ftp.inria.fr/INRIA/publication/publi-pdf/RR/RR-4218.pdf",
abstract = "Contrairement aux apparences, il n'est pas simple
d'ecrire un programme informatique realisant un
algorithme evolutionnaire, d'autant que le manque de
langage specialise oblige l'utilisateur a utiliser C,
C++ ou JAVA. La plupart des algorithmes
evolutionnaires, cependant, possedent une structure
commune, et la part reellement specifique est
constituee par une faible portion du code. Ainsi, il
semble que rien ne s'oppose en theorie a ce qu'un
utilisateur puisse construire, puis faire tourner son
algorithme evolutionnaire a partir d'une interface
graphique, afin de limiter son effort de programmation
a la fonction a optimiser. L'ecriture d'une telle
interface graphique pose tout d'abord le probleme de
sauver et de recharger l'algorithme evolutionnaire sur
lequel l'utilisateur travaille, puis celui de
transformer ces informations en code compilable. Cela
ressemble fort a un language de specification et son
compilateur. Le logiciel EASEA a ete cree dans ce but,
et a notre connaissance, il est actuellement le seul et
unique compilateur de langage specifique aux
algorithmes evolutionnaires. Ce rapport decrit comment
EASEA a ete construit et quels sont les problemes qui
restent a resoudre pour achever son implantation
informatique.",
abstract = "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.",
notes = "in english",
size = "17 pages",
}
@InProceedings{Collet:2002:IotOoEAC,
author = "Pierre Collet and Jean Louchet and Evelyne Lutton",
title = "Issues on the Optimisation of Evolutionary Algorithms
Code",
booktitle = "Proceedings of the 2002 Congress on Evolutionary
Computation CEC2002",
editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
and Garry Greenwood and Hitoshi Iba and Paul Marrow and
Mark Shackleton",
pages = "1103--1108",
year = "2002",
publisher = "IEEE Press",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
organisation = "IEEE Neural Network Council (NNC), Institution of
Electrical Engineers (IEE), Evolutionary Programming
Society (EPS)",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-7278-6",
month = "12-17 " # may,
notes = "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)",
}
@InProceedings{collet:2003:EA,
author = "Pierre Collet and Marc Schoenauer",
title = "{GUIDE}: Unifying Evolutionary Engines through a
Graphical User Interface",
booktitle = "Evolution Artificielle, 6th International Conference",
year = "2003",
editor = "Pierre Liardet and Pierre Collet and Cyril Fonlupt and
Evelyne Lutton and Marc Schoenauer",
volume = "2936",
series = "Lecture Notes in Computer Science",
pages = "203--215",
address = "Marseilles, France",
month = "27-30 " # oct,
publisher = "Springer",
note = "Revised Selected Papers",
keywords = "genetic algorithms, genetic programming, Artificial
Evolution",
ISBN = "3-540-21523-9",
URL = "http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=2936&spage=229",
doi = "doi:10.1007/b96080",
abstract = "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.",
bibsource = "DBLP, http://dblp.uni-trier.de",
notes = "EA'03
general tool not specifically for GP",
}
@Proceedings{collet:2006:GP,
title = "Proceedings of the 9th European Conference on Genetic
Programming",
year = "2006",
editor = "Pierre Collet and Marco Tomassini and Marc Ebner and
Steven Gustafson and Anik\'o Ek\'art",
volume = "3905",
series = "Lecture Notes in Computer Science",
address = "Budapest, Hungary",
publisher = "Springer",
month = "10 - 12 " # apr,
organisation = "EvoNet",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-33143-3",
URL = "http://www.springerlink.com/openurl.asp?genre=issue&issn=0302-9743&volume=3905",
doi = "doi:10.1007/11729976",
size = "361 pages",
notes = "EuroGP'2006 held in conjunction with EvoCOP2006 and
EvoWorkshops2006",
}
@InCollection{collet:2007:nicem,
author = "Pierre Collet",
title = "Genetic Programming",
booktitle = "Handbook of Research on Nature-Inspired Computing for
Economics and Management",
editor = "Jean-Philippe Rennard",
publisher = "Idea Group Inc.",
year = "2007",
volume = "I",
chapter = "V",
pages = "59--73",
address = "1200 E. Colton Ave",
keywords = "genetic algorithms, genetic programming, GP-std/same,
homologous crossover, interval arithmetic, problem
dependence, over fitting and bloat",
ISBN = "1-59140-984-5",
abstract = "GP is now mature and can routinely yeild results on
par with or better than human intelligence",
size = "15 pages",
}
@Article{Collet:2009:GPEM,
author = "Pierre Collet",
title = "Husbands, Holland, and Wheeler (eds): Review of the
book {"}The Mechanical Mind in History{"} {MIT} Press,
2008, {ISBN} 978-0-262-08377-5",
journal = "Genetic Programming and Evolvable Machines",
year = "2009",
volume = "10",
number = "1",
pages = "91--93",
month = mar,
keywords = "genetic algorithms, genetic programming",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-008-9070-1",
size = "2.1 pages",
notes = "Book Review",
}
@Article{Collet:2012:GPEM,
author = "Pierre Collet and Man Leung Wong",
title = "Evolutionary algorithms for data mining",
journal = "Genetic Programming and Evolvable Machines",
year = "2012",
volume = "13",
number = "1",
pages = "69--70",
month = mar,
note = "Editorial Introduction to Special Section on
Evolutionary Algorithms for Data Mining",
keywords = "genetic algorithms, genetic programming",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-011-9156-z",
size = "2 pages",
affiliation = "Universite de Strasbourg, Alsace, France",
}
@InProceedings{collins:1998:mbiaia,
author = "J. J. Collins",
title = "Modeling the Behaviour of Interacting Autonomous
Intelligent Agents",
booktitle = "Late Breaking Papers at the Genetic Programming 1998
Conference",
year = "1998",
editor = "John R. Koza",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "Stanford, CA, USA",
month = "22-25 " # jul,
publisher = "Stanford University Bookstore",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.csis.ul.ie/staff/jjcollins/gp98.html",
notes = "GP-98LB, GP-98PhD Student Workshop",
}
@InProceedings{collins:1999:GPMRNS,
author = "J. J. Collins and Lucia Sheehan and Conor Casey",
title = "Genetic Planner for a Mobile Robot Navigation System",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "782",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems, poster
papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-399.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-399.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{collins:1999:NFOPI,
author = "J. J. Collins and Conor Ryan",
title = "Non-stationary Function Optimization using Polygenic
Inheritance",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "781",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems, poster
papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-398.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-398.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{collins:2004:nue:mcol,
author = "M. Collins",
title = "Counting Solutions in Reduced {Boolean} Parity",
editor = "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",
booktitle = "GECCO 2004 Workshop Proceedings",
year = "2004",
month = "26-30 " # jun,
address = "Seattle, Washington, USA",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/WNUE001.pdf",
notes = "GECCO-2004WKS Distributed on CD-ROM at GECCO-2004
See also \cite{Collins:2006:GPEM}",
}
@InProceedings{1068282,
author = "M. Collins",
title = "Finding needles in haystacks is harder with
neutrality",
booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on
Genetic and evolutionary computation",
year = "2005",
editor = "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",
volume = "2",
ISBN = "1-59593-010-8",
pages = "1613--1618",
address = "Washington DC, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1613.pdf",
doi = "doi:10.1145/1068009.1068282",
publisher = "ACM Press",
publisher_address = "New York, NY, 10286-1405, USA",
month = "25-29 " # jun,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, Cartesian
genetic programming, reduced Boolean parity, search
space",
notes = "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 \cite{Collins:2006:GPEM}",
}
@Article{Collins:2006:GPEM,
author = "Mark Collins",
title = "Finding needles in haystacks is harder with
neutrality",
journal = "Genetic Programming and Evolvable Machines",
year = "2006",
volume = "7",
number = "2",
pages = "131--144",
month = aug,
note = "Special Issue: Best of GECCO 2005",
keywords = "genetic algorithms, genetic programming, Cartesian
genetic programming, random sampling, solution
density",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-006-9001-y",
abstract = "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.",
notes = "Reduced parity=given XOR and EQ only.",
}
@PhdThesis{Collins:thesis,
author = "Mark Collins",
title = "An Algorithm for Evolving Protocol Constraints",
school = "Artificial Intelligence Applications Institute, School
of Informatics, University of Edinburgh",
year = "2006",
keywords = "genetic algorithms",
URL = "http://www.cisa.informatics.ed.ac.uk/ssp/pubs/collins_phd.pdf",
size = "approx 220 pages",
abstract = "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.",
}
@InProceedings{collins:1999:ACSSVT,
author = "Trevor D. Collins",
title = "A Comparison of Search Space Visualization
Techniques",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "780",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems, poster
papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-395.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-395.pdf",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{Colmenar:2010:gecco,
author = "J. Manuel Colmenar and Jose L. Risco-Martin and David
Atienza and Oscar Garnica and J. Ignacio Hidalgo and
Juan Lanchares",
title = "Improving reliability of embedded systems through
dynamic memory manager optimization using grammatical
evolution",
booktitle = "GECCO '10: Proceedings of the 12th annual conference
on Genetic and evolutionary computation",
year = "2010",
editor = "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",
isbn13 = "978-1-4503-0072-8",
pages = "1227--1234",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, SBSE",
month = "7-11 " # jul,
organisation = "SIGEVO",
address = "Portland, Oregon, USA",
doi = "doi:10.1145/1830483.1830705",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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",
notes = "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.
\cites{DBLP:journals/todaes/AtienzaMMSC06}
Also known as \cite{1830705} GECCO-2010 A joint meeting
of the nineteenth international conference on genetic
algorithms (ICGA-2010) and the fifteenth annual genetic
programming conference (GP-2010)",
}
@InProceedings{Colmenar:2011:GECCO,
author = "J. Manuel Colmenar and Jose L. Risco-Martin and David
Atienza and J. Ignacio Hidalgo",
title = "Multi-objective optimization of dynamic memory
managers using grammatical evolution",
booktitle = "GECCO '11: Proceedings of the 13th annual conference
on Genetic and evolutionary computation",
year = "2011",
editor = "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",
isbn13 = "978-1-4503-0557-0",
pages = "1819--1826",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, SBSE, Real world applications",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001576.2001820",
publisher = "ACM",
publisher_address = "New York, NY, USA",
size = "8 pages",
abstract = "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.",
notes = "Garbage collector. Energy consumption. NSGA-2.
Pop=40.
Also known as \cite{2001820} GECCO-2011 A joint meeting
of the twentieth international conference on genetic
algorithms (ICGA-2011) and the sixteenth annual genetic
programming conference (GP-2011)",
}
@InProceedings{Colton:evows09,
author = "Simon Colton and Cameron Browne",
title = "Evolving Simple Art-based Games",
booktitle = "Applications of Evolutionary Computing,
EvoWorkshops2009: {EvoCOMNET}, {EvoENVIRONMENT},
{EvoFIN}, {EvoGAMES}, {EvoHOT}, {EvoIASP},
{EvoINTERACTION}, {EvoMUSART}, {EvoNUM}, {EvoPhD},
{EvoSTOC}, {EvoTRANSLOG}",
year = "2009",
month = "15-17 " # apr,
editor = "Mario Giacobini and Ivanoe {De Falco} and Marc Ebner",
series = "LNCS",
publisher = "Springer Verlag",
address = "Tubingen, Germany",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "EvoWorkshops2009",
}
@Article{Comellas:1998:GPD,
author = "F. Comellas and G. Gim{\'e}nez",
title = "Genetic Programming to Design Communication Algorithms
for Parallel Architectures",
journal = "Parallel Processing Letters",
year = "1998",
volume = "8",
number = "4",
pages = "549--560",
keywords = "genetic algorithms, genetic programming, broadcasting,
networks, butterfly graph",
ISSN = "0129-6264",
URL = "http://www-mat.upc.es/~comellas/genprog/genprog_f.pdf",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/173/http:zSzzSzwww-mat.upc.eszSz~comellaszSzgenprogzSzgenprog_f.pdf/comellas98genetic.pdf",
URL = "http://citeseer.ist.psu.edu/comellas98genetic.html",
doi = "doi:10.1142/S0129626498000547",
size = "12 pages",
abstract = "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.",
notes = "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",
CODEN = "PPLTEE",
ISSN = "0129-6264",
URL = "http://www-mat.upc.es/~comellas/genprog/genprog.html",
acknowledgement = ack-nhfb,
bibdate = "Mon Nov 09 07:22:43 1998",
}
@InProceedings{comellas:evows04,
author = "Francesc Comellas and Cristina Dalf\'o",
title = "Using Genetic Programming to Design Broadcasting
Algorithms for Manhattan Street Networks",
booktitle = "Applications of Evolutionary Computing,
EvoWorkshops2004: {EvoBIO}, {EvoCOMNET}, {EvoHOT},
{EvoIASP}, {EvoMUSART}, {EvoSTOC}",
year = "2004",
month = "5-7 " # apr,
editor = "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",
series = "LNCS",
volume = "3005",
address = "Coimbra, Portugal",
publisher = "Springer Verlag",
publisher_address = "Berlin",
pages = "170--177",
keywords = "genetic algorithms, genetic programming, evolutionary
computation",
ISBN = "3-540-21378-3",
abstract = "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.",
notes = "EvoWorkshops2004",
}
@InProceedings{Comte:2009:CIGPU,
author = "Pascal Comte",
title = "Design \& Implementation of Parallel Linear {GP} for
the {IBM} Cell Processor",
booktitle = "GECCO '09: Proceedings of the 11th Annual conference
on Genetic and evolutionary computation",
year = "2009",
editor = "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",
address = "Montreal",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "8-12 " # jul,
organisation = "SigEvo",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-60558-325-9",
doi = "doi:10.1145/1569901.1596274",
abstract = "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.",
notes = "Also known as \cite{1596274}. 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).",
}
@InProceedings{Compte:2009:CIGPU2,
author = "Pascal Comte",
title = "Design \& Implementation of Real-time Parallel {GA}
Operators on the {IBM} Cell Processor",
booktitle = "GECCO '09: Proceedings of the 11th Annual conference
on Genetic and evolutionary computation",
year = "2009",
editor = "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",
address = "Montreal",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "8-12 " # jul,
organisation = "SigEvo",
keywords = "genetic algorithms",
isbn13 = "978-1-60558-325-9",
doi = "doi:10.1145/1569901.1596275",
abstract = "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.",
notes = "Also known as \cite{1596275}. 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).",
}
@Article{ga95aCona:1995:dGPs,
author = "John Cona",
title = "Developing a Genetic Programming System",
journal = "AI Expert",
year = "1995",
pages = "20--29",
month = feb,
keywords = "genetic algorithms, genetic programming, C++, Object
Orientated",
ISSN = "0888-3785",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/ga95aCona_1995_dGPs.pdf",
size = "10 pages",
abstract = "We can use an object-oriented C++ approach to develop
gentic base classes. Discusses practical speed/memory
tradeoffs for an (IBM) PC environment.",
notes = "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.
",
}
@InProceedings{Conca:2009:AHS,
author = "Piero Conca and Giuseppe Nicosia and Giovanni
Stracquadanio and Jon Timmis",
title = "Nominal-Yield-Area Tradeoff in Automatic Synthesis of
Analog Circuits: {A} Genetic Programming Approach using
Immune-Inspired Operators",
booktitle = "NASA/ESA Conference on Adaptive Hardware and Systems
(AHS-2009)",
year = "2009",
editor = "Tughrul Arslan and Didier Keymeulen",
pages = "399--406",
address = "San Francisco, California, USA",
month = jul # " 29-" # aug # " 1",
keywords = "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",
doi = "doi:10.1109/AHS.2009.32",
abstract = "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).",
notes = "Co-located with Design Automation Conference
(DAC-2009) http://www.see.ed.ac.uk/~ahs2009/ Also known
as \cite{5325428}",
}
@InProceedings{congdon:2000:GA,
author = "Clare Bates Congdon and Emily F. Greenfest",
title = "Gaphyl: {A} genetic algorithm approach to cladistics",
booktitle = "Data Mining with Evolutionary Algorithms",
year = "2000",
editor = "Alex A. Freitas and William Hart and Natalio Krasnogor
and Jim Smith",
pages = "85--88",
address = "Las Vegas, Nevada, USA",
month = "8 " # jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.colby.edu/~congdon/Publications/gaphyl-gecco00.ps",
URL = "http://citeseer.ist.psu.edu/426598.html",
abstract = "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...",
size = "4 pages",
notes = "GECCO-2000WKS Part of \cite{wu:2000:GECCOWKS}",
}
@InProceedings{congdon:2003:ptuesipiegtwwgd,
author = "Congdon and Septor",
title = "Phylogenetic trees using evolutionary search: Initial
progress in extending gaphyl to work with genetic
data",
booktitle = "Proceedings of the 2003 Congress on Evolutionary
Computation CEC2003",
editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass
and Kay Chen Tan and Bob McKay and Daryl Essam and Tom
Gedeon",
pages = "320--326",
year = "2003",
publisher = "IEEE Press",
address = "Canberra",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "8-12 " # dec,
organisation = "IEEE Neural Network Council (NNC), Engineers Australia
(IEAust), Evolutionary Programming Society (EPS),
Institution of Electrical Engineers (IEE)",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-7804-0",
abstract = "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.",
notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust,
the EPS, and the IEE.",
}
@Article{Connolly:2004:MSDN,
author = "Brian Connolly",
title = "Genetic Algorithms Survival of the Fittest: Natural
Selection with Windows Forms",
journal = "MSDN Magazine",
year = "2004",
volume = "19",
number = "8",
month = aug,
publisher = "Microsoft",
keywords = "genetic algorithms, genetic programming",
URL = "http://msdn.microsoft.com/msdnmag/issues/04/08/GeneticAlgorithms/default.aspx",
abstract = "This article discusses:
* Genetic programming definition
* Breeding new algorithm generations
* Cross breeding
* Mutations
* Increasing fitness",
notes = "Santa Fe ant trail. .net Reflection CodeDOM",
}
@InCollection{Con88,
author = "Michael Conrad",
title = "The Price of Programmability",
booktitle = "The Universal {Turing} Machine A Half-Century Survey",
publisher = "Oxford University Press",
year = "1988",
editor = "Rolf Herken",
pages = "285--307",
keywords = "genetic algorithms, genetic programming, cellular
automata, evolvable hardware, quantum computing, DNA
and molecular computing",
size = "23 pages",
ISBN = "0-19-853741-7",
notes = "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.",
}
@InProceedings{conrads:1998:ssdGP,
author = "Markus Conrads and Peter Nordin and Wolfgang Banzhaf",
title = "Speech Sound Discrimination With Genetic Programming",
booktitle = "Proceedings of the First European Workshop on Genetic
Programming",
year = "1998",
editor = "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer
and Terence C. Fogarty",
volume = "1391",
series = "LNCS",
pages = "113--129",
address = "Paris",
publisher_address = "Berlin",
month = "14-15 " # apr,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-64360-5",
abstract = "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.",
notes = "EuroGP'98",
}
@InProceedings{Cook:2008:DAC,
author = "Henry Cook and Kevin Skadron",
title = "Predictive design space exploration using genetically
programmed response surfaces",
booktitle = "45th ACM/IEEE Design Automation Conference, DAC 2008",
year = "2008",
month = jun,
pages = "960--965",
keywords = "genetic algorithms, genetic programming, genetically
programmed response surfaces, microarchitectural design
space exploration, optimization process, predictive
design space exploration, aircraft computers, computer
architecture",
URL = "http://www.cs.virginia.edu/~skadron/Papers/gprs_dac08.pdf",
doi = "doi:10.1145/1391469.1391711",
ISSN = "0738-100X",
abstract = "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.",
notes = "Also known as \cite{4555958} \cite{1391711}",
}
@InProceedings{Cook:2011:CIG,
author = "Michael Cook and Simon Colton",
title = "Multi-Faceted Evolution Of Simple Arcade Games",
booktitle = "Proceedings of the 2011 IEEE Conference on
Computational Intelligence and Games",
year = "2011",
address = "Seoul, South Korea",
pages = "289--296",
month = "31 " # aug # " - 3 " # sep,
publisher = "IEEE",
keywords = "genetic algorithms",
URL = "http://cilab.sejong.ac.kr/cig2011/proceedings/CIG2011/papers/paper64.pdf",
size = "8 pages",
abstract = "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",
notes = "fixed representation",
}
@InCollection{coon:1994:csgp,
author = "Brett W. Coon",
title = "Circuit Synthesis through Genetic Programming",
booktitle = "Genetic Algorithms at Stanford 1994",
year = "1994",
editor = "John R. Koza",
pages = "11--20",
address = "Stanford, California, 94305-3079 USA",
month = dec,
organisation = "Stanford University",
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-187263-3",
notes = "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{"}.",
}
@InProceedings{cooper:2002:gecco,
author = "Jason Cooper and Chris Hinde",
title = "Comparison Of Evolving Against Peers And Fixed
Opponents Using Corewars",
booktitle = "GECCO 2002: Proceedings of the Genetic and
Evolutionary Computation Conference",
editor = "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",
year = "2002",
pages = "887",
address = "New York",
publisher_address = "San Francisco, CA 94104, USA",
month = "9-13 " # jul,
publisher = "Morgan Kaufmann Publishers",
keywords = "genetic algorithms, genetic programming, poster paper,
Corewars",
ISBN = "1-55860-878-8",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/GP082.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/GP082.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-15.pdf",
notes = "GECCO-2002. A joint meeting of the eleventh
International Conference on Genetic Algorithms
(ICGA-2002) and the seventh Annual Genetic Programming
Conference (GP-2002)",
}
@InProceedings{cordella:evocop05,
author = "Luigi Pietro Cordella and Claudio {De Stefano} and
Francesco Fontanella and Angelo Marcelli",
title = "EvoGene{S}, a New Evolutionary Approach to Graph
Generation",
booktitle = "Evolutionary Computation in Combinatorial Optimization
-- {EvoCOP}~2005",
year = "2005",
month = "30 " # mar # "-1 " # apr,
editor = "G{\"{u}}nther R. Raidl and Jens Gottlieb",
series = "LNCS",
volume = "3448",
publisher = "Springer Verlag",
address = "Lausanne, Switzerland",
publisher_address = "Berlin",
pages = "46--57",
keywords = "evolutionary computation",
ISSN = "0302-9743",
abstract = "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.",
notes = "EvoCOP2005
Claims to be significantly better than
\cite{hu:2004:wapcbgp}",
}
@InProceedings{cordella:2005:CEC,
author = "L. P. Cordella and C. {De Stefano} and F. Fontanella
and A. Marcelli",
title = "Genetic Programming for Generating Prototypes in
Classification Problems",
booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary
Computation",
year = "2005",
editor = "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",
volume = "2",
pages = "1149--1155",
address = "Edinburgh, UK",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "2-5 " # sep,
organisation = "IEEE Computational Intelligence Society, Institution
of Electrical Engineers (IEE), Evolutionary Programming
Society (EPS)",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-9363-5",
abstract = "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.",
notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and
the EPS.",
}
@InProceedings{conf/iciap/CordellaSFM05,
title = "A Novel Genetic Programming Based Approach for
Classification Problems",
author = "Luigi P. Cordella and Claudio {De Stefano} and
Francesco Fontanella and Angelo Marcelli",
year = "2005",
pages = "727--734",
editor = "Fabio Roli and Sergio Vitulano",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3617",
booktitle = "Proceedings 13th International Conference Image
Analysis and Processing - ICIAP 2005",
address = "Cagliari, Italy",
month = sep # " 6-8",
bibdate = "2006-02-09",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/iciap/iciap2005.html#CordellaSFM05",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-28869-4",
URL = "http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3617&spage=727",
doi = "doi:10.1007/11553595_89 ?",
size = "8 pages",
abstract = "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.",
}
@InProceedings{DBLP:conf/iwicpas/CordellaSFM06,
author = "Luigi P. Cordella and Claudio {De Stefano} and
Francesco Fontanella and Angelo Marcelli",
title = "Looking for Prototypes by Genetic Programming",
booktitle = "Advances in Machine Vision, Image Processing, and
Pattern Analysis, International Workshop on Intelligent
Computing in Pattern Analysis/Synthesis, IWICPAS 2006,
Proceedings",
year = "2006",
pages = "152--159",
editor = "Nanning Zheng and Xiaoyi Jiang and Xuguang Lan",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "4153",
address = "Xi'an, China",
month = aug # " 26-27",
bibsource = "DBLP, http://dblp.uni-trier.de",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-37597-X",
doi = "doi:10.1007/11821045_16",
abstract = "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.",
}
@Article{cordon:1999:sedpuhedat,
author = "Oscar Cordon and Francisco Herrera and Luciano
Sanchez",
title = "Solving Electrical Distribution Problems Using Hybrid
Evolutionary Data Analysis Techniques",
journal = "Applied Intelligence",
year = "1999",
volume = "10",
number = "1",
pages = "5--24",
month = jan,
keywords = "genetic algorithms, genetic programming, electrical
engineering, data analysis, evolutionary algorithms,
genetic algorithm program, genetic fuzzy rule-based
systems",
ISSN = "0924-669X",
URL = "ftp://decsai.ugr.es/pub/arai/tech_rep/ga-fl/tr-98106.ps.Z",
notes = "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 \cite{howard:1995:GA-P}
tr-98106.ps.Z PScript preliminary version",
}
@InProceedings{DBLP:conf/eusflat/CordonAZ99,
author = "Oscar Cordon and Felix {de Moya Anegon} and Carmen
Zarco",
title = "Learning Queries for a Fuzzy Information Retrieval
System by means of {GA}-{P} Techniques",
booktitle = "Proceedings of the EUSFLAT-ESTYLF Joint Conference",
year = "1999",
editor = "Gaspar Mayor and Jaume Su{\~n}er",
pages = "335--338",
address = "Palma de Mallorca, Spain",
publisher_address = "Palma de Mallorca, Spain",
month = sep # " 22-25",
organisation = "European Society for Fuzzy Logica and Technology",
publisher = "Universitat de les Illes Balears",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.eusflat.org/publications/proceedings/EUSFLAT-ESTYLF_1999/papers/335-cordon.pdf",
notes = "http://www.eusflat.org/publications/proceedings/EUSFLAT-ESTYLF_1999/",
bibsource = "DBLP, http://dblp.uni-trier.de",
}
@Article{Cordon:2000:MSC,
author = "O. Cordon and F. {de Moya} and C. Zarco",
title = "A {GA}-{P} Algorithm to Automatically Formulate
Extended {Boolean} Queries for a Fuzzy Information
Retrieval System",
journal = "Mathware \& Soft Computing",
year = "2000",
volume = "7",
number = "2-3",
pages = "309--322",
organisation = "European Society for Fuzzy Logic and Technology
(EUSFLAT)",
keywords = "genetic algorithms, genetic programming",
ISSN = "1134-5632",
URL = "http://ic.ugr.es/Mathware/index.php/Mathware/article/viewFile/145/124",
size = "14 pages",
abstract = "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.",
notes = "http://ic.ugr.es/Mathware/index.php/Mathware",
}
@InProceedings{Cordon:2002:ISKO,
author = "O. Cordon and E. Herrera-Viedma and Maria Luque and
Felix Moya and Carmen Zarco",
title = "An Inductive Query by Example Technique for Extended
{Boolean} Queries Based on Simulated-Annealing
Programming",
booktitle = "Challenges in Knowledge Representation and
Organization for the 21st Century. Integration of
Knowledge across Boundaries. Proceedings of the 7th
International ISKO Conference (ISKO'2002)",
year = "2002",
editor = "M. J. Lopez-Huertas",
volume = "8",
series = "Advances in knowledge organization",
pages = "429--436",
address = "Granada, Spain",
publisher_address = "Wuerzburg, Germany",
month = jul # " 10-13",
publisher = "Ergon",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-89913-247-2",
URL = "http://www.ergon-verlag.de/en/start.htm?information-_library_sciences_advances_in_knowledge_organization.htm",
notes = "http://www.isko.org/events.html",
}
@Article{cordon:2002:SC,
author = "O. Cordon and F. Moya and C. Zarco",
title = "A new evolutionary algorithm combining simulated
annealing and genetic programming for relevance
feedback in fuzzy information retrieval systems",
journal = "Soft Computing - A Fusion of Foundations,
Methodologies and Applications",
year = "2002",
volume = "6",
number = "5",
pages = "308--319",
month = aug,
keywords = "genetic algorithms, genetic programming, Fuzzy
information retrieval, Relevance feedback, Evolutionary
algorithms, Simulated annealing",
ISSN = "1432-7643",
doi = "doi:10.1007/s00500-002-0184-8",
abstract = "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.",
}
@InProceedings{cordon:ppsn2002:pp710,
author = "Oscar Cordon and Enrique Herrera-Viedma and Maria
Luque",
title = "Evolutionary Learning of {Boolean} Queries by
Multiobjective Genetic Programming",
booktitle = "Parallel Problem Solving from Nature - PPSN VII",
address = "Granada, Spain",
month = "7-11 " # sep,
pages = "710--719",
year = "2002",
editor = "Juan J. Merelo-Guervos and Panagiotis Adamidis and
Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and
Hans-Paul Schwefel",
number = "2439",
series = "Lecture Notes in Computer Science, LNCS",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, MOGA, Pattern
recognition and classification/datamining,Web services,
Multi-objective",
ISBN = "3-540-44139-5",
annote = "Available from
http://link.springer.de/link/service/series/0558/papers/2439/243900710.pdf",
URL = "http://link.springer.de/link/service/series/0558/bibs/2439/24390710.htm",
URL = "http://link.springer.de/link/service/series/0558/papers/2439/24390710.pdf",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2439&spage=710",
abstract = "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.",
}
@InProceedings{DBLP:conf/ifsa/CordonHLMZ03,
author = "Oscar Cordon and Enrique Herrera-Viedma and Maria
Luque and Felix {de Moya Anegon} and Carmen Zarco",
title = "Analyzing the Performance of a Multiobjective {GA}-{P}
Algorithm for Learning Fuzzy Queries in a Machine
Learning Environment",
booktitle = "Proceedings of the 10th International Fuzzy Systems
Association World Congress, Fuzzy Sets and Systems -
IFSA 2003",
year = "2003",
editor = "Taner Bilgi\c{c} and Bernard De Baets and Okyay
Kaynak",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "2715",
pages = "611--619",
address = "Istanbul, Turkey",
month = jun # " 30 - " # jul # " 2",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-40383-3",
URL = "http://www.scimago.es/publications/ifsa03-cordon.pdf",
URL = "http://springerlink.metapress.com/openurl.asp?genre=article{\&}issn=0302-9743{\&}volume=2715{\&}spage=611",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
}
@InProceedings{cordon:2003:WSC,
author = "Oscar Cordon and Enrique Herrera-Viedma and Maria
Luque and Felix Moya and Carmen Zarco",
title = "A Realistic Information Retrieval Environment to
Validate a Multiobjective {GA}-{P} Algorithm for
Learning Fuzzy Queries",
booktitle = "Proceedings if the 8th Online World Conference on Soft
Computing in Industrial Applications (WSC8)",
year = "2003",
volume = "32",
series = "Advances in Soft Computing",
pages = "299--309",
publisher = "Springer",
note = "published by Springer 2005 as Soft Computing:
Methodologies and Applications",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-540-25726-4",
doi = "doi:10.1007/3-540-32400-3_23",
abstract = "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.",
}
@InProceedings{Cordon:2004:FUZZ-IEEE,
author = "Oscar Cordon and Felix {de Moya} and Carmen Zarco",
title = "Fuzzy logic and multiobjective evolutionary algorithms
as soft computing tools for persistent query learning
in text retrieval environments",
booktitle = "Proceedings of the 2004 IEEE International Conference
on Fuzzy Systems (FUZZ-IEEE 2004)",
year = "2004",
volume = "1",
pages = "571--576",
address = "Budapest, Hungary",
publisher = "IEEE Press",
month = "25-29 " # jul,
keywords = "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",
size = "6 pages",
abstract = "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.",
notes = "also known as \cite{1375799}",
}
@InCollection{Cordon:2004:fzi,
author = "O. Cordon and F. Moya and C. Zarco",
title = "Automatic Learning of Multiple Extended {Boolean}
Queries by Multiobjective {GA}-{P} Algorithms",
booktitle = "Fuzzy Logic and the Internet",
publisher = "PHYSICA-VERLAG",
year = "2004",
editor = "V. Loia and M. Nikravesh and L. A. Zadeh",
volume = "137",
series = "STUDIES IN FUZZINESS AND SOFT COMPUTING",
pages = "47--70",
address = "Germany",
keywords = "genetic algorithms, genetic programming",
URL = "http://direct.bl.uk/research/18/0E/RN143659018.html",
ISSN = "1434-9922",
notes = "English",
}
@InCollection{cordon:2005:SCMA,
author = "Oscar Cordon and Enrique Herrera-Viedma and Maria
Luque and Felix Moya and Carmen Zarco",
title = "A Realistic Information Retrieval Environment to
Validate a Multiobjective {GA}-{P} Algorithm for
Learning Fuzzy Queries",
booktitle = "Soft Computing: Methodologies and Applications",
publisher = "Springer-Verlag",
year = "2005",
editor = "F. Hoffmann and M. Koppen and F. Klawonn and R. Roy",
volume = "32",
series = "Advances in Soft Computing",
pages = "299--309",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-540-25726-4",
doi = "doi:10.1007/3-540-32400-3_23",
abstract = "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.",
}
@Article{CHL:IPM:06,
title = "Improving the learning of {Boolean} queries by means
of a multiobjective {IQBE} evolutionary algorithm",
author = "O. Cordon and E. Herrera-Viedma and M. Luque",
journal = "Information Processing and Management",
year = "2006",
volume = "42",
number = "3",
pages = "615--632",
month = may,
keywords = "genetic algorithms, genetic programming, Boolean
information retrieval systems, Inductive query by
example, Multiobjective evolutionary algorithms, Query
learning",
doi = "doi:10.1016/j.ipm.2005.02.006",
abstract = "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
\cite{MartinPSmith: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.",
}
@InProceedings{corney:1999:NSMUGP,
author = "David Corney and Ian Parmee",
title = "{N}-Dimensional Surface Mapping Using Genetic
Programming",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1230",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, poster
papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-424.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-424.ps",
notes = "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.",
}
@InProceedings{cad_sac98,
author = "F. Corno and M. {Sonza Reorda} and G. Squillero",
title = "The Selfish Gene Algorithm: a New Evolutionary
Optimization Strategy",
booktitle = "SAC: ACM Symposium on Applied Computing",
year = "1998",
pages = "349--355",
keywords = "Genetic Algorithms, Approximate Methods, Equivalence
Checking, Evolutionary Algorithms, Gate-Level,
Simulation-Based Approaches",
URL = "http://www.cad.polito.it/FullDB/exact/sac98.html",
URL = "http://www.cad.polito.it/pap/db/sac98.pdf",
keywords = "Approximate Methods, Evolutionary Algorithms, Selfish
Gene",
abstract = "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.",
}
@InProceedings{cad_iccd98a,
author = "F. Corno and M. {Sonza Reorda} and G. Squillero",
title = "{VEGA}: {A} Verification Tool Based on Genetic
Algorithms",
booktitle = "ICCD: International Conference on Circuit Design",
year = "1998",
pages = "321--326",
URL = "http://www.cad.polito.it/FullDB/exact/iccd98a.html",
URL = "http://www.cad.polito.it/pap/db/iccd98a.pdf",
abstract = "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.",
}
@InProceedings{corno:2000:avpi,
author = "Fulvio Corno and Matteo {Sonza Reorda} and Giovanni
Squillero",
title = "Automatic Validation of Protocol Interfaces Described
in {VHDL}",
booktitle = "Real-World Applications of Evolutionary Computing",
year = "2000",
editor = "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",
volume = "1803",
series = "LNCS",
pages = "205--213",
address = "Edinburgh",
publisher_address = "Berlin",
month = "17 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, ASIC, Approximate Methods,
Evolutionary Algorithms, Gate-Level, Low Power, Selfish
Gene, Simulation-Based Approaches",
ISBN = "3-540-67353-9",
URL = "http://www.cad.polito.it/FullDB/exact/evotel2000a.html",
size = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1803&spage=205",
abstract = "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.",
notes = "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",
}
@InProceedings{Corno:2001:DATE,
author = "F. Corno and M. {Sonza Reorda} and G. Squillero and M.
Violante",
title = "On the test of microprocessor {IP} cores",
booktitle = "Proceedings of Design, Automation and Test in Europe
Conference and Exhibition 2001",
year = "2001",
pages = "209--213",
address = "Munich, Germany",
month = "13-16 " # mar,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.date-conference.com/conference/instructions/gl_paper04c_2.pdf",
doi = "doi:10.1109/DATE.2001.915026",
size = "5 pages",
abstract = "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",
notes = "Posted online: 2002-08-07
00:20:42.0
\cite{squillero: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.'",
}
@InProceedings{corno:2002:emctpi,
author = "F. Corno and G. Cumani and M. {Sonza Reorda} and G.
Squillero",
title = "Efficient Machine-Code Test-Program Induction",
booktitle = "Proceedings of the 2002 Congress on Evolutionary
Computation CEC2002",
year = "2002",
editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
and Garry Greenwood and Hitoshi Iba and Paul Marrow and
Mark Shackleton",
pages = "1486--1491",
address = "Honolulu, Hawaii, USA",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "12-17 " # may,
organisation = "IEEE Neural Network Council (NNC), Institution of
Electrical Engineers (IEE), Evolutionary Programming
Society (EPS)",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, DAG, ATPG,
Approximate Methods, Evolutionary Algorithms,
Micro-Processors, Simulation-Based Approaches",
ISBN = "0-7803-7278-6",
URL = "http://www.cad.polito.it/pap/db/cec2002.pdf",
URL = "http://citeseer.ist.psu.edu/502344.html",
size = "6 pages",
abstract = "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.",
notes = "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 \cite{Corno:2002:EMTI}
\cite{squillero:2005:GPEM} says corno:2002:emctpi uses
straightforward mu+lambda evolution.",
}
@InProceedings{corno:2002:ATS,
author = "Fulvio Corno and Gianluca Cumani and Matteo {Sonza
Reorda} and Giovanni Squillero",
title = "Evolutionary test program induction for microprocessor
design verification",
booktitle = "Proceedings of the 11th Asian Test Symposium (ATS
'02)",
year = "2002",
pages = "368--373",
month = "18-20 " # nov,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
ISSN = "1081-7735",
URL = "http://www.cad.polito.it/pap/db/ats02.pdf",
URL = "http://citeseer.ist.psu.edu/574157.html",
doi = "doi:10.1109/ATS.2002.1181739",
size = "6 pages",
abstract = "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.",
notes = "Posted online: 2003-02-28 18:15:31.0
Cited by \cite{squillero:2005:GPEM}",
}
@InProceedings{oai:CiteSeerPSU:573140,
title = "Automatic Test Program Generation for Pipelined
Processors",
author = "F. Corno and G. Cumani and M. {Sonza Reorda} and G.
Squillero",
publisher = "ACM",
year = "2003",
bibsource = "DBLP, http://dblp.uni-trier.de",
booktitle = "Proceedings of the 2003 ACM Symposium on Applied
Computing (SAC)",
address = "Melbourne, FL, USA",
month = "9-12 " # mar,
keywords = "genetic algorithms, genetic programming",
citeseer-isreferencedby = "oai:CiteSeerPSU:219188;
oai:CiteSeerPSU:183962; oai:CiteSeerPSU:139723;
oai:CiteSeerPSU:98608",
citeseer-references = "oai:CiteSeerPSU:472349; oai:CiteSeerPSU:276822;
oai:CiteSeerPSU:303540; oai:CiteSeerPSU:212034;
oai:CiteSeerPSU:186935",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:573140",
rights = "unrestricted",
URL = "http://www.cad.polito.it/pap/db/sac03.pdf",
URL = "http://citeseer.ist.psu.edu/573140.html",
abstract = "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.",
}
@InProceedings{corno:2003:ICES,
author = "F. Corno and F. Cumani and G. Squillero",
title = "Exploiting Auto-adaptive $\mu$-{GP} for Highly
Effective Test Programs Generation",
booktitle = "Evolvable Systems: From Biology to Hardware, Fifth
International Conference, ICES 2003",
year = "2003",
editor = "Andy M. Tyrrell and Pauline C. Haddow and Jim
Torresen",
volume = "2606",
series = "LNCS",
pages = "262--273",
address = "Trondheim, Norway",
month = "17-20 " # mar,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-00730-X",
doi = "doi:10.1007/3-540-36553-2_24",
abstract = "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.",
notes = "ICES-2003",
}
@InProceedings{corno03,
author = "F. Corno and G. Squillero",
title = "An Enhanced Framework for Microprocessor Test-Program
Generation",
booktitle = "Genetic Programming, Proceedings of EuroGP'2003",
year = "2003",
editor = "Conor Ryan and Terence Soule and Maarten Keijzer and
Edward Tsang and Riccardo Poli and Ernesto Costa",
volume = "2610",
series = "LNCS",
pages = "307--316",
address = "Essex",
publisher_address = "Berlin",
month = "14-16 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-00971-X",
URL = "http://www.cad.polito.it/pap/db/eurogp03.pdf",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=307",
abstract = "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.",
notes = "EuroGP'2003 held in conjunction with EvoWorkshops
2003",
size = "10 pages",
}
@InProceedings{corno:2004:oteocw,
title = "On The Evolution of Corewar Warriors",
author = "Fulvio Corno and Ernesto Sanchez and Giovanni
Squillero",
pages = "133--138",
booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary
Computation",
year = "2004",
publisher = "IEEE Press",
month = "20-23 " # jun,
address = "Portland, Oregon",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming, Evolutionary
Computation and Games",
URL = "http://www.cad.polito.it/pap/db/cec2004b.pdf",
size = "6 page",
abstract = "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.",
notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and
the IEE.",
}
@Article{Corno:2005:ieeeP,
author = "F. Corno and E. Sanchez and M. S. Reorda and G.
Squillero",
title = "Automatic test generation for verifying
microprocessors",
journal = "IEEE Potentials",
year = "2005",
volume = "24",
number = "1",
pages = "34--37",
month = feb # "-" # mar,
keywords = "genetic algorithms, genetic programming",
ISSN = "0278-6648",
doi = "doi:10.1109/MP.2005.1405800",
abstract = "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.",
}
@InProceedings{Corns:2006:CEC,
author = "Steven M. Corns and Daniel A. Ashlock and Douglas S.
McCorkle and Kenneth Mark Bryden",
title = "Improving Design Diversity Using Graph Based
Evolutionary Algorithms",
booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary
Computation",
year = "2006",
editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and
Simon M. Lucas",
pages = "1037--1043",
address = "Vancouver",
month = "6-21 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-9487-9",
size = "7 pages",
abstract = "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.",
notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
the IEE.
IEEE Catalog Number: 06TH8846D. IEEE Xplore gives pages
= {"}333--339{"}",
}
@InProceedings{conf/issre/CostaVPS05,
title = "Modeling Software Reliability Growth with Genetic
Programming",
author = "Eduardo Oliveira Costa and Silvia Regina Vergilio and
Aurora Trinidad Ramirez Pozo and Gustavo A. {de
Souza}",
year = "2005",
pages = "171--180",
booktitle = "16th International Symposium on Software Reliability
Engineering (ISSRE 2005)",
address = "Chicago, IL, USA",
month = "8-11 " # nov,
publisher = "IEEE Computer Society",
bibdate = "2006-01-03",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/issre/issre2005.html#CostaVPS05",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7695-2482-6",
doi = "doi:10.1109/ISSRE.2005.29",
abstract = "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.",
}
@InProceedings{conf/ictai/CostaPV06,
title = "Using Boosting Techniques to Improve Software
Reliability Models Based on Genetic Programming",
author = "Eduardo Oliveira Costa and Aurora Pozo and Silvia
Regina Vergilio",
year = "2006",
booktitle = "18th IEEE International Conference on Tools with
Artificial Intelligence (ICTAI'06)",
pages = "643--650",
address = "Washington, D.C, USA",
month = nov # " 13-15",
publisher = "IEEE Computer Society",
bibdate = "2007-01-04",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/ictai/ictai2006.html#CostaPV06",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1109/ICTAI.2006.117",
abstract = "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.",
}
@InProceedings{Costa:2006:ICSMC,
author = "Eduardo Oliveira Costa and Aurora Pozo",
title = "A New Approach to Genetic Programming based on
Evolution Strategies",
booktitle = "IEEE International Conference on Systems, Man and
Cybernetics, ICSMC '06",
year = "2006",
volume = "6",
pages = "4832--4837",
address = "Taipei, Taiwan",
month = "8-11 " # oct,
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-4244-0100-3",
doi = "doi:10.1109/ICSMC.2006.385070",
abstract = "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.",
notes = "Computer Science Department, Federal University of
Parana (UFPR), PO Box 19081, 81531-970, Curitiba,
Brazil,",
}
@InProceedings{Costa:2006:ICTAI,
author = "E. O. Costa and A. Pozo",
title = "A (mu + lambda) - {GP} Algorithm and its use for
Regression Problems",
booktitle = "8th IEEE International Conference on Tools with
Artificial Intelligence, ICTAI '06",
year = "2006",
pages = "10--17",
address = "Arlington, VA, USA",
month = nov,
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7695-2728-0",
doi = "doi:10.1109/ICTAI.2006.6",
abstract = "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",
notes = "Dept. of Comput. Sci., Fed. Univ. of Parana,
Curitiba",
}
@Article{Costa:2007:ieeeTR,
author = "Eduardo Oliveira Costa and Gustavo Alexandre {de
Souza} and Aurora Trinidad Ramirez Pozo and Silvia
Regina Vergilio",
title = "Exploring Genetic Programming and Boosting Techniques
to Model Software Reliability",
journal = "IEEE Transactions on Reliability",
year = "2007",
volume = "56",
number = "3",
pages = "422--434",
month = sep,
keywords = "genetic algorithms, genetic programming, Fault
prediction, machine learning techniques, software
reliability models",
doi = "doi:10.1109/TR.2007.903269",
ISSN = "0018-9529",
abstract = "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.",
}
@Article{Costa:2010:ieeeTR,
author = "Eduardo Oliveira Costa and Aurora Trinidad Ramirez
Pozo and Silvia Regina Vergilio",
title = "A Genetic Programming Approach for Software
Reliability Modeling",
journal = "IEEE Transactions on Reliability",
year = "2010",
keywords = "genetic algorithms, genetic programming, Fault
prediction, machine learning techniques, software
reliability models, SBSE",
abstract = "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.",
doi = "doi:10.1109/TR.2010.2040759",
ISSN = "0018-9529",
notes = "Also known as \cite{5409534}",
}
@InProceedings{costa:1999:GGOMINP,
author = "Lino Costa and Pedro Oliveira",
title = "{GA}s in Global Optimization of Mixed Integer
Non-Linear Problems",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1773",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "real world applications, poster papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-740.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-740.pdf",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{Costa:1997:acsqrscgp,
author = "Paolo Costa",
title = "A Methodology for the Analysis of Complex Systems
based on Qualitative Reasoning, Stochastic Complexity
and Genetic Programming",
booktitle = "Late Breaking Papers at the 1997 Genetic Programming
Conference",
year = "1997",
editor = "John R. Koza",
pages = "35--41",
address = "Stanford University, CA, USA",
publisher_address = "Stanford University, Stanford, California,
94305-3079, USA",
month = "13--16 " # jul,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-206995-8",
notes = "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",
}
@InProceedings{costelloe:2004:eurogp,
author = "Dan Costelloe and Conor Ryan",
title = "Genetic Programming for Subjective Fitness Function
Identification",
booktitle = "Genetic Programming 7th European Conference, EuroGP
2004, Proceedings",
year = "2004",
editor = "Maarten Keijzer and Una-May O'Reilly and Simon M.
Lucas and Ernesto Costa and Terence Soule",
volume = "3003",
series = "LNCS",
pages = "259--268",
address = "Coimbra, Portugal",
publisher_address = "Berlin",
month = "5-7 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-21346-5",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=259",
abstract = "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.",
notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in
conjunction with EvoCOP2004 and EvoWorkshops2004",
}
@InProceedings{1277389,
author = "Dan Costelloe and Conor Ryan",
title = "Towards models of user preferences in interactive
musical evolution",
booktitle = "GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation",
year = "2007",
editor = "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",
volume = "2",
isbn13 = "978-1-59593-697-4",
pages = "2254--2254",
address = "London",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2254.pdf",
doi = "doi:10.1145/1276958.1277389",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
month = "7-11 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, Real-World Applications: Poster, human
factors, interactive evolution",
abstract = "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.",
notes = "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",
}
@InProceedings{Costelloe:2009:eurogp,
author = "Dan Costelloe and Conor Ryan",
title = "On Improving Generalisation in Genetic Programming",
booktitle = "Proceedings of the 12th European Conference on Genetic
Programming, EuroGP 2009",
year = "2009",
editor = "Leonardo Vanneschi and Steven Gustafson and Alberto
Moraglio and Ivanoe {De Falco} and Marc Ebner",
volume = "5481",
series = "LNCS",
pages = "61--72",
address = "Tuebingen",
month = apr # " 15-17",
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-01180-1",
doi = "doi:10.1007/978-3-642-01181-8_6",
size = "12 pages",
abstract = "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.",
notes = "overfitting, Part of \cite{conf/eurogp/2009}
EuroGP'2009 held in conjunction with EvoCOP2009,
EvoBIO2009 and EvoWorkshops2009",
}
@PhdThesis{Costelloe:thesis,
author = "Dan Costelloe",
title = "Evolutionary Optimisation and Prediction in Subjective
Problem Domains",
school = "University of Limerick",
year = "2009",
address = "Limerick, Ireland",
month = nov,
keywords = "genetic algorithms, genetic programming",
URL = "https://digitary.ul.ie/verifier/servlet/DocumentVerifierApp/template/VerifyDAT.vm?datid=k7aahpcxm1",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Costelloe_thesis.pdf",
size = "158 pages",
abstract = "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.",
notes = "Supervisor: Dr. Conor Ryan",
}
@InProceedings{cotillon:2012:EuroGP,
author = "Alban Cotillon and Philip Valencia and Raja Jurdak",
title = "Android Genetic Programming Framework",
booktitle = "Proceedings of the 15th European Conference on Genetic
Programming, EuroGP 2012",
year = "2012",
month = "11-13 " # apr,
editor = "Alberto Moraglio and Sara Silva and Krzysztof Krawiec
and Penousal Machado and Carlos Cotta",
series = "LNCS",
volume = "7244",
publisher = "Springer Verlag",
address = "Malaga, Spain",
pages = "13--24",
organisation = "EvoStar",
isbn13 = "978-3-642-29138-8",
URL = "http://jurdak.com/eurogp12.pdf",
doi = "doi:10.1007/978-3-642-29139-5_2",
size = "12 pages",
keywords = "genetic algorithms, genetic programming, AGP,
Embedded, Smartphone",
abstract = "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.",
notes = "Android open source, Java code. RSS reader. Online
fitness monitoring. Online GP.
Part of \cite{Moraglio:2012:GP} EuroGP'2012 held in
conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012
and EvoApplications2012",
affiliation = "Autonomous Systems Laboratory, CSIRO ICT Centre,
Brisbane, Australia",
}
@InProceedings{cotta:1996:edflc,
author = "Carlos Cotta and E. Alba and J. M. Troya",
title = "Evolutionary Design of Fuzzy Logic Controllers",
booktitle = "Proceedings of the 1996 IEEE International Symposium
on Intelligent Control",
year = "1996",
pages = "127--132",
address = "Dearborn MI, USA",
month = "15-18 Septmeber",
publisher = "IEEE Control Systems Society",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.lcc.uma.es/~ccottap/papers/isic96flc.pdf",
size = "6 pages",
abstract = "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.",
}
@InProceedings{cotta:1999:ISDOFRTGR,
author = "Carlos Cotta and Enrique Alba and Jose M. Troya",
title = "Improving the Scalability of Dynastically Optimal
Forma Recombination by Tuning the Granularity of the
Representation",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "783",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems, poster
papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/Ga-800.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-800.PS",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{cotta:ppsn2002:pp720,
author = "Carlos Cotta and Pablo Moscato",
title = "Inferring Phylogenetic Trees Using Evolutionary
Algorithms",
booktitle = "Parallel Problem Solving from Nature - PPSN VII",
address = "Granada, Spain",
month = "7-11 " # sep,
pages = "720--729",
year = "2002",
editor = "Juan J. Merelo-Guervos and Panagiotis Adamidis and
Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and
Hans-Paul Schwefel",
number = "2439",
series = "Lecture Notes in Computer Science, LNCS",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, Biology and
chemistry, Comparisons of representations",
ISBN = "3-540-44139-5",
annote = "Available from
http://link.springer.de/link/service/series/0558/papers/2439/243900720.pdf",
ISBN = "3-540-44139-5",
URL = "http://link.springer.de/link/service/series/0558/papers/2439/24390720.pdf",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2439&spage=720",
abstract = "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.",
}
@Article{Cotta:2007:GPEM,
author = "Carlos Cotta and Juan-Julian Merelo",
title = "Where is evolutionary computation going? {A} temporal
analysis of the {EC} community",
journal = "Genetic Programming and Evolvable Machines",
year = "2007",
volume = "8",
number = "3",
pages = "239--253",
month = sep,
keywords = "genetic algorithms, genetic programming, evolvable
hardware, Complex networks, Evolutionary computation,
Social network analysis",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-007-9031-0",
size = "15 pages",
abstract = "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.",
}
@Article{journals/soco/CouchetMRR07,
author = "Jorge Couchet and Daniel Manrique and Juan Rios and
Alfonso Rodriguez-Paton",
title = "Crossover and mutation operators for grammar-guided
genetic programming",
journal = "Soft Computing",
year = "2007",
volume = "11",
number = "10",
pages = "943--955",
month = aug,
keywords = "genetic algorithms, genetic programming,
Grammar-guided genetic programming, Crossover,
Mutation, Breast cancer prognosis",
doi = "doi:10.1007/s00500-006-0144-9",
abstract = "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.",
notes = "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).",
bibdate = "2008-03-11",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/soco/soco11.html#CouchetMRR07",
}
@InProceedings{Courte:2007:ANNIE,
author = "Dale E. Courte",
title = "Hybrid Evolutionary Code Generation Optimizing Both
Functional Form and Parameter Values",
booktitle = "ANNIE 2007, Intelligent Engineering Systems through
Artificial Neural Networks",
year = "2007",
editor = "Cihan H. Dagli",
volume = "17",
address = "St. Louis, MO, USA",
note = "Part III: Evolutionary Computation",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
doi = "doi:10.1115/1.802655.paper35",
abstract = "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.",
}
@Book{Cowan:book,
author = "George S. Cowan and Robert G. Reynolds",
title = "Acquisition of Software Engineering Knowledge {SWEEP}:
An Automatic Programming System Based on Genetic
Programming and Cultural Algorithms",
publisher = "World Scientific",
year = "2003",
volume = "14",
series = "Software Engineering and Knowledge Engineering",
address = "Singapore",
month = aug,
keywords = "genetic algorithms, genetic programming",
ISBN = "981-02-2920-8",
URL = "http://www.worldscibooks.com/compsci/3338.html",
abstract = "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",
notes = "http://www.worldscibooks.com/compsci/3338.html
Wayne State University, USA",
size = "164 pages",
}
@Article{craig:1999:gpds,
author = "Iain Craig",
title = "Genetic Programming and Data Structures",
journal = "Robotica",
year = "1999",
volume = "17",
number = "4",
pages = "462",
note = "Review",
keywords = "genetic algorithms, genetic programming",
size = "0.25 pages",
notes = "Review of \cite{langdon:book}",
}
@InProceedings{icga85:cramer,
author = "Nichael Lynn Cramer",
title = "A representation for the Adaptive Generation of Simple
Sequential Programs",
year = "1985",
booktitle = "Proceedings of an International Conference on Genetic
Algorithms and the Applications",
address = "Carnegie-Mellon University, Pittsburgh, PA, USA",
month = "24-26 " # jul,
editor = "John J. Grefenstette",
pages = "183--187",
size = "5 pages",
URL = "http://www.sover.net/~nichael/nlc-publications/icga85/index.html",
keywords = "genetic algorithms, genetic programming, memory",
abstract = "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.",
notes = "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.",
}
@InCollection{crane:2005:GPTP,
author = "Ellery Fussell Crane and Nicholas Freitag McPhee",
title = "The effects of size and depth limits on tree based
genetic programming",
booktitle = "Genetic Programming Theory and Practice {III}",
year = "2005",
editor = "Tina Yu and Rick L. Riolo and Bill Worzel",
volume = "9",
series = "Genetic Programming",
chapter = "15",
pages = "223--240",
address = "Ann Arbor",
month = "12-14 " # may,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, Size limits,
Depth limits, Population distributions, Tree Shape,
bloat",
ISBN = "0-387-28110-X",
size = "18 pages",
abstract = "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.",
notes = "part of \cite{yu:2005:GPTP} Published Jan 2006 after
the workshop",
}
@Article{Cranganu2010243,
author = "Constantin Cranganu and Elena Bautu",
title = "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",
journal = "Journal of Petroleum Science and Engineering",
volume = "70",
number = "3-4",
pages = "243--255",
year = "2010",
ISSN = "0920-4105",
doi = "doi:10.1016/j.petrol.2009.11.017",
URL = "http://www.sciencedirect.com/science/article/B6VDW-4XTNG6D-7/2/f3e31340cb8a863475bff4f643de28a9",
keywords = "genetic algorithms, genetic programming, Gene
Expression Programming, soft computing, sonic log,
Anadarko Basin, overpressured zones",
abstract = "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.",
}
@InProceedings{cranmer-2003,
author = "Kyle S. Cranmer",
title = "Multivariate Analysis from a Statistical Point of
View",
booktitle = "Phystat2003",
year = "2003",
editor = "Louis Lyons and Richard Mount and Rebecca Reitmeyer",
pages = "211--214",
address = "SLAC, Stanford, USA",
month = sep # " 8-11",
keywords = "genetic algorithms, genetic programming, VC
dimension",
URL = "http://www.slac.stanford.edu/econf/C030908/papers/WEJT002.pdf",
URL = "http://www.citebase.org/abstract?id=oai:arXiv.org:physics/0310110",
oai = "oai:arXiv.org:physics/0402030",
size = "4 pages",
abstract = "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.",
notes = "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",
}
@Misc{oai:arXiv.org:physics/0402030,
title = "Physics{GP}: {A} Genetic Programming Approach to Event
Selection",
author = "Kyle Cranmer and R. Sean Bowman",
year = "2004",
month = feb # "~05",
abstract = "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",
note = "Comment: 16 pages 9 figures, 1 table. Submitted to
Comput. Phys. Commun",
oai = "oai:arXiv.org:physics/0402030",
URL = "http://arXiv.org/abs/physics/0402030",
keywords = "genetic algorithms, genetic programming, Triggering,
Classification, VC Dimension, Neural Networks, Support
Vector Machines",
size = "pages",
notes = "Published as \cite{cranmer:2005:CPC}.
cites \cite{luke: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.",
}
@Article{cranmer:2005:CPC,
author = "Kyle Cranmer and R. Sean Bowman",
title = "{PhysicsGP}: {A} Genetic Programming approach to event
selection",
journal = "Computer Physics Communications",
year = "2005",
volume = "167",
number = "3",
pages = "165--176",
month = "1 " # may,
keywords = "genetic algorithms, genetic programming, Triggering,
Classification, VC dimension, Genetic algorithms,
Neural networks, Support vector machines",
ISSN = "0010-4655",
URL = "http://arxiv.org/abs/physics/0402030",
doi = "doi:10.1016/j.cpc.2004.12.006",
abstract = "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.",
notes = "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{"}.",
}
@PhdThesis{cranmer:thesis,
author = "Kyle S. Cranmer",
title = "Searching for New Physics: Contributions to {LEP} and
the {LHC}",
school = "University of Wisconsin-Madison",
year = "2005",
keywords = "genetic algorithms, genetic programming, PhysicsGP",
URL = "http://www.theoryandpractice.org/kyle/Files/cranmer_thesis.pdf",
size = "233 pages",
abstract = "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",
notes = "'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.",
}
@InProceedings{crapper:1997:mrrr,
author = "P. F. Crapper and P. A. Whigham",
title = "Modelling Rainfall-runoff Relationships",
booktitle = "24th Hydrology and Water Resources Symposium",
year = "1997",
address = "Auckland, New Zealand",
keywords = "genetic algorithms, genetic programming",
URL = "http://trove.nla.gov.au/work/24556122",
URL = "http://books.google.co.uk/books/about/24th_Hydrology_Water_resources_Symposium.html?id=bLC5PwAACAAJ&redir_esc=y",
notes = "
",
}
@InProceedings{crawford:1999:MGTNSV,
author = "Kelly D. Crawford and Michael D. McCormack and Donald
J. MacAllister",
title = "Modified Gradient Techniques for Normalized Solution
Vectors",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1498--1503",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "real world applications",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-720.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-720.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{crawford-marks:2002:gecco,
author = "Raphael Crawford-Marks and Lee Spector",
title = "Size Control Via Size Fair Genetic Operators In The
{PushGP} Genetic Programming System",
booktitle = "GECCO 2002: Proceedings of the Genetic and
Evolutionary Computation Conference",
editor = "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",
year = "2002",
pages = "733--739",
address = "New York",
publisher_address = "San Francisco, CA 94104, USA",
month = "9-13 " # jul,
publisher = "Morgan Kaufmann Publishers",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-878-8",
URL = "http://alum.hampshire.edu/~rpc01/gp234.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/gp234.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf",
notes = "GECCO-2002. A joint meeting of the eleventh
International Conference on Genetic Algorithms
(ICGA-2002) and the seventh Annual Genetic Programming
Conference (GP-2002)",
}
@Article{crawford-marks:2004:ascribe,
key = "Crawford-Marks",
title = "Hampshire College Student Uses {J}.{K}. Rowling's
Quidditch as Basis for Artificial Intelligence
Experiment",
journal = "AScribe Newswire",
year = "2004",
month = "4 " # may,
note = "online",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.ascribe.org/cgi-bin/spew4th.pl?ascribeid=20040504.114704&time=12%2033%20PDT&year=2004&public=1",
abstract = "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.",
notes = "references \cite{spector:2001:vqacpapsa}",
}
@Misc{crawford-marks:2004:senior,
author = "Raphael Crawford-Marks",
title = "Virtual Witches and Warlocks: Computational Evolution
of Teamwork and Strategy in a Dynamic, Heterogeneous
and Noisy 3{D} Environment",
school = "School of Cognitive Science, Hampshire College",
year = "2004",
type = "Division III (senior) Thesis",
month = "18 " # may,
keywords = "genetic algorithms, genetic programming, Coevolution,
breve, Push",
URL = "http://alum.hampshire.edu/~rpc01/div3.pdf",
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.148.949",
size = "64 pages, (pdf 460KB)",
language = "en",
oai = "oai:CiteSeerXPSU:10.1.1.148.949",
abstract = "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.",
notes = "http://www.spiderland.org/breve.",
}
@InProceedings{crawford-marks:2004:lbp,
author = "Raphael Crawford-Marks and Lee Spector and Jon Klein",
title = "Virtual Witches and Warlocks: {A} Quidditch Simulator
and Quidditch-Playing Teams Coevolved via Genetic
Programming",
booktitle = "Late Breaking Papers at the 2004 Genetic and
Evolutionary Computation Conference",
year = "2004",
editor = "Maarten Keijzer",
address = "Seattle, Washington, USA",
month = "26 " # jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/LBP046.pdf",
abstract = "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.",
notes = "Part of \cite{keijzer:2004:GECCO:lbp}",
}
@InCollection{creighton:2000:SSOGA,
author = "Steven L. Creighton",
title = "Structural Shape Optimization using a Genetic
Algorithm",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2000",
year = "2000",
editor = "John R. Koza",
pages = "108--116",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms",
notes = "part of \cite{koza:2000:gagp}",
}
@InProceedings{crepeau:1995:GEMS,
author = "Ronald L. Crepeau",
title = "Genetic Evolution of Machine Language Software",
booktitle = "Proceedings of the Workshop on Genetic Programming:
From Theory to Real-World Applications",
year = "1995",
editor = "Justinian P. Rosca",
pages = "121--134",
address = "Tahoe City, California, USA",
month = "9 " # jul,
keywords = "genetic algorithms, genetic programming, memory",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/GEMS_Article.pdf",
size = "14 pages",
abstract = "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.",
notes = "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 \cite{spector: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 \cite{rosca:1995:ml}",
}
@Article{Crepinsek:2006:ENTCS,
author = "Matej Crepinsek and Marjan Mernik and Barrett R.
Bryant and Faizan Javed and Alan Sprague",
title = "Inferring Context-Free Grammars for Domain-Specific
Languages",
journal = "Electronic Notes in Theoretical Computer Science",
year = "2005",
volume = "141",
number = "4",
pages = "99--116",
month = "12 " # dec,
note = "Proceedings of the Fifth Workshop on Language
Descriptions, Tools, and Applications (LDTA 2005)",
keywords = "genetic algorithms, genetic programming, Grammar
induction, Grammar inference, Learning from positive
and negative examples, Exhaustive search",
ISSN = "1571-0661",
doi = "doi:10.1016/j.entcs.2005.02.055",
abstract = "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.",
}
@InCollection{Cretin:al:EA95,
author = "Guillaume Cretin and Evelyne Lutton and Jacques
Levy-Vehel and Philippe Glevarec and Cedric Roll",
title = "Mixed {IFS}: Resolution of the Inverse Problem Using
Genetic Programming",
booktitle = "Artificial Evolution",
publisher = "Springer Verlag",
year = "1996",
editor = "Jean-Marc Alliot and Evelyne Lutton and Edmund Ronald
and Marc Schoenauer and Dominique Snyers",
volume = "1063",
series = "LNCS",
pages = "247--258",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-540-61108-0",
doi = "doi:10.1007/3-540-61108-8_42",
size = "11 pages",
abstract = "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.",
notes = "Selected papers from two conferences: Evolution
Artificielle 94 and Evolution Artificielle 95
http://www.cmap.polytechnique.fr/www.eark/ea95.html see
also \cite{lutton:1995:IFScs} and
\cite{lutton:1995:IFScs}",
affiliation = "INRIA-Rocquencourt B.P. 105 78153 Le Chesnay Cedex
France B.P. 105 78153 Le Chesnay Cedex France",
}
@InProceedings{cribbs:1999:AMGAA,
author = "H. Brown {Cribbs III}",
title = "Aircraft Maneuvering via Genetics-Based Adaptive
Agent",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1249--1256",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "artificial life, adaptive behavior and agents",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-035.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-035.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@TechReport{oai:CiteSeerPSU:265557,
title = "Defending a Computer System using Autonomous Agents",
author = "Mark Crosbie and Gene Spafford",
institution = "Department of Computer Science, Perdue University",
year = "1994",
type = "Technical Report",
number = "95-022",
address = "West Lafayette, IN, USA",
month = "11 " # mar,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cerias.purdue.edu/homes/spaf/tech-reps/9522.ps",
URL = "http://citeseer.ist.psu.edu/265557.html",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:265557",
rights = "unrestricted",
abstract = "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.",
notes = "citeseer 1999 oct 21",
size = "11 pages",
}
@InProceedings{crosbie:1995:aGPid,
title = "Applying Genetic Programming to Intrusion Detection",
author = "Mark Crosbie and Eugene H. Spafford",
booktitle = "Working Notes for the AAAI Symposium on Genetic
Programming",
year = "1995",
editor = "E. V. Siegel and J. R. Koza",
pages = "1--8",
address = "MIT, Cambridge, MA, USA",
publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA",
month = "10--12 " # nov,
publisher = "AAAI",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.aaai.org/Papers/Symposia/Fall/1995/FS-95-01/FS95-01-001.pdf",
URL = "http://www.aaai.org/Library/Symposia/Fall/fs95-01.php",
URL = "http://citeseer.ist.psu.edu/197480.html",
size = "8 pages",
abstract = "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.",
notes = "AAAI-95f GP. Part of \cite{siegel:1995:aaai-fgp} {\em
Telephone:} 415-328-3123 {\em Fax:} 415-321-4457 {\em
email} info@aaai.org {\em URL:} http://www.aaai.org/",
}
@InProceedings{crosbie:1996:eedp,
author = "Mark Crosbie and Eugene H. Spafford",
title = "Evolving Event Driven Programs",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "273--278",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
URL = "http://citeseer.ist.psu.edu/rd/13718071%2C200806%2C1%2C0.25%2CDownload/http://citeseer.ist.psu.edu/cache/papers/cs/8415/http:zSzzSzwww.best.comzSz%7EmcrosbiezSzResearchzSzgp96.pdf/crosbie96evolving.pdf",
URL = "http://citeseer.ist.psu.edu/200806.html",
size = "6 pages",
abstract = "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.",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96",
}
@Article{Csukas:1996:CCE,
author = "B. Csukas and R. Lakner and K. Varga and S. Balogh",
title = "Combining generated structural models with genetic
programming in evolutionary synthesis",
journal = "Computers \& Chemical Engineering",
year = "1996",
volume = "20",
pages = "S61--S66",
number = "Supplement 1",
note = "European Symposium on Computer Aided Process
Engineering-6",
keywords = "genetic algorithms, genetic programming",
ISSN = "0098-1354",
doi = "doi:10.1016/0098-1354(96)00021-X",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6TFT-48JC24K-F/2/d8223cad7192932d658ef2274794f502",
abstract = "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.",
}
@Article{Csukas:1998:CI,
author = "Bela Csukas and Sandor Balogh",
title = "Combining genetic programming with generic simulation
models in evolutionary synthesis",
journal = "Computers in Industry",
volume = "36",
pages = "181--197",
year = "1998",
number = "3",
month = "1 " # jun,
keywords = "genetic algorithms, genetic programming, Generic
simulation, Genetic evolution, Process design,
Structural modeling, Multicriteria evaluation",
URL = "http://www.sciencedirect.com/science/article/B6V2D-3VW737S-3/1/87e285c0690af97d9d081c4f2582fdcd",
doi = "doi:10.1016/S0166-3615(98)00071-2",
abstract = "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.",
}
@InProceedings{DBLP:conf/gecco/CuiBO09,
author = "Wei Cui and Anthony Brabazon and Michael O'Neill",
title = "Efficient trade execution using a genetic algorithm in
an order book based artificial stock market",
booktitle = "GECCO-2009 Late-Breaking Papers",
year = "2009",
editor = "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",
pages = "2023--2028",
address = "Montreal",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "8-12 " # jul,
organisation = "SigEvo",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-60558-325-9",
bibsource = "DBLP, http://dblp.uni-trier.de",
doi = "doi:10.1145/1570256.1570270",
abstract = "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.",
notes = "Distributed on CD-ROM at GECCO-2009.
ACM Order Number 910092.",
}
@InProceedings{cui:2010:evofin,
author = "Wei Cui and Anthony Brabazon and Michael O'Neill",
title = "Evolving Dynamic Trade Execution Strategies Using
Grammatical Evolution",
booktitle = "EvoFIN",
year = "2010",
editor = "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",
volume = "6025",
series = "LNCS",
pages = "192--201",
address = "Istanbul",
month = "7-9 " # apr,
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
isbn13 = "978-3-642-12241-5",
doi = "doi:10.1007/978-3-642-12242-2_20",
abstract = "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.",
notes = "EvoFIN'2010 held in conjunction with EuroGP'2010
EvoCOP2010 EvoBIO2010",
}
@InProceedings{cui_etal:cec2010,
author = "Wei Cui and Anthony Brabazon and Michael O'Neill",
title = "Evolving Efficient Limit Order Strategy using
Grammatical Evolution",
booktitle = "2010 IEEE World Congress on Computational
Intelligence",
pages = "2408--2413",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
isbn13 = "978-1-4244-6910-9",
abstract = "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.",
}
@InProceedings{DBLP:conf/ices/Cullen08,
author = "Jamie Cullen",
title = "Evolutionary Meta Compilation: Evolving Programs Using
Real World Engineering Tools",
booktitle = "Proceedings of the 8th International Conference
Evolvable Systems: From Biology to Hardware, ICES
2008",
year = "2008",
editor = "Gregory Hornby and Luk{\'a}s Sekanina and Pauline C.
Haddow",
series = "Lecture Notes in Computer Science",
volume = "5216",
pages = "414--419",
address = "Prague, Czech Republic",
month = sep # " 21-24",
publisher = "Springer",
bibsource = "DBLP, http://dblp.uni-trier.de",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
isbn13 = "978-3-540-85856-0",
doi = "doi:10.1007/978-3-540-85857-7_38",
size = "6 pages",
abstract = "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.",
notes = "Artificial Intelligence Laboratory, University of New
South Wales, Sydney, NSW.
Santa Fe ant. Taxi problem (loops). gcc. tiny c (tcc),
full adder circuit",
}
@InProceedings{DBLP:conf/seal/Cullen08,
author = "Jamie Cullen",
title = "Evolving Digital Circuits in an Industry Standard
Hardware Description Language",
booktitle = "Proceedings of the 7th International Conference on
Simulated Evolution And Learning (SEAL '08)",
year = "2008",
editor = "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{\"u}rgen Branke
and Yuhui Shi",
volume = "5361",
series = "Lecture Notes in Computer Science",
pages = "514--523",
address = "Melbourne, Australia",
month = dec # " 7-10",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, Grammatical
Evolution",
isbn13 = "978-3-540-89693-7",
doi = "doi:10.1007/978-3-540-89694-4_52",
abstract = "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.",
bibsource = "DBLP, http://dblp.uni-trier.de",
}
@InProceedings{Cullen:2009:GEC,
author = "Jamie Cullen",
title = "Evolving common {LISP} programs in a linear-genotype
evolutionary computation system",
booktitle = "GEC '09: Proceedings of the first ACM/SIGEVO Summit on
Genetic and Evolutionary Computation",
year = "2009",
editor = "Lihong Xu and Erik D. Goodman and Guoliang Chen and
Darrell Whitley and Yongsheng Ding",
bibsource = "DBLP, http://dblp.uni-trier.de",
pages = "75--80",
address = "Shanghai, China",
organisation = "SigEvo",
doi = "doi:10.1145/1543834.1543846",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = jun # " 12-14",
isbn13 = "978-1-60558-326-6",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "Also known as \cite{DBLP:conf/gecco/Cullen09} part of
\cite{DBLP:conf/gec/2009}",
}
@InProceedings{Cullen:2009:GECa,
author = "Jamie Cullen",
title = "Evolutionary meta programming",
booktitle = "GEC '09: Proceedings of the first ACM/SIGEVO Summit on
Genetic and Evolutionary Computation",
year = "2009",
editor = "Lihong Xu and Erik D. Goodman and Guoliang Chen and
Darrell Whitley and Yongsheng Ding",
bibsource = "DBLP, http://dblp.uni-trier.de",
pages = "81--88",
address = "Shanghai, China",
organisation = "SigEvo",
doi = "doi:10.1145/1543834.1543847",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = jun # " 12-14",
isbn13 = "978-1-60558-326-6",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "Also known as \cite{DBLP:conf/gecco/Cullen09a} part of
\cite{DBLP:conf/gec/2009}",
}
@InProceedings{cummins:2004:lbp,
author = "Ronan Cummins and Colm O'Riordan",
title = "Using Genetic Programming to Evolve Weighting Schemes
for the Vector Space Model of Information Retrieval",
booktitle = "Late Breaking Papers at the 2004 Genetic and
Evolutionary Computation Conference",
year = "2004",
editor = "Maarten Keijzer",
address = "Seattle, Washington, USA",
month = "26 " # jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/LBP038.pdf",
abstract = "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.",
notes = "Part of \cite{keijzer:2004:GECCO:lbp}",
}
@InProceedings{cummins:2004:AICS,
author = "Ronan Cummins and Colm O'Riordan",
title = "Determining General Term Weighting Schemes for the
Vector Space Model of Information Retrieval Using
Genetic Programming",
booktitle = "15th Artificial Intelligence and Cognitive Science
Conference (AICS 2004)",
year = "2004",
editor = "Lorraine McGinty",
address = "Galway-Mayo Institute of Technology, Castlebar Campus,
Ireland",
month = "8-10 " # sep,
keywords = "genetic algorithms, genetic programming",
notes = "http://www.gmit.ie/aics_2004/",
}
@TechReport{Cummins:2004:071204,
author = "Ronan Cummins and Colm O'Riordan",
title = "Evolving, Analysing and Improving Global
Term-Weighting Schemes in Information Retrieval",
institution = "National University of Ireland, Galway",
year = "2004",
type = "Technical Report",
number = "NUIG-IT-071204",
address = "Ireland",
keywords = "genetic algorithms, genetic programming, information
retrieval, term-weighting",
URL = "http://www.it.nuigalway.ie/Publications/TR/abstracts/NUIG-IT-071204.pdf",
abstract = "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.",
notes = "25 January 2005",
size = "11 pages",
}
@TechReport{Cummins:2005:201205,
author = "Ronan Cummins and Colm O'Riordan",
title = "Evolving Term-Selection Schemes for Pseudo-Relevance
Feedback in Information Retrieval",
institution = "National University of Ireland, Galway",
year = "2005",
number = "NUIG-IT-201205",
address = "Ireland",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.it.nuigalway.ie/publications/TR/abstracts/NUIG-IT-201205.ps",
notes = "Problem displaying page 1",
size = "9 pages",
}
@InProceedings{cummins:2005:CIKM,
author = "Ronan Cummins and Colm O'Riordan",
title = "An evaluation of evolved term-weighting schemes in
information retrieval",
year = "2005",
booktitle = "CIKM '05: Proceedings of the 14th ACM international
conference on Information and knowledge management",
editor = "Otthein Herzog and Hans-Jorg Schek and Norbert Fuhr
and Abdur Chowdhury and Wilfried Teiken",
pages = "305--306",
address = "Bremen, Germany",
publisher_address = "New York, NY, USA",
month = "31 " # oct # " - 5 " # nov,
organisation = "ACM",
publisher = "ACM press",
keywords = "genetic algorithms, genetic programming, information
retrieval, term-weighting, Poster Session",
URL = "http://portal.acm.org/citation.cfm?doid=1099639",
ISBN = "1-59593-140-6",
size = "2 pages",
abstract = "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.",
order_no = "605050",
notes = "Proceedings of the 14th ACM international conference
on Information and knowledge management",
}
@InProceedings{cummins:2005:AICS,
author = "Ronan Cummins and Colm O'Riordan",
title = "Evolving Co-occurrence Based Query Expansion Schemes
in Information Retrieval Using Genetic Programming",
booktitle = "The 16th Irish conference on Artificial Intelligence
and Cognitive Science (AICS05)",
year = "2005",
editor = "Norman Creaney",
pages = "137--146",
address = "School of Computing and Information Engineering,
University of Ulster",
publisher_address = "Cromore Road, Coleraine, BT52 1SA, UK",
month = "7-9 " # sep,
publisher = "University of Ulster",
keywords = "genetic algorithms, genetic programming, information
retrieval, query expansion",
ISBN = "1-85923-197-7",
URL = "http://www.infc.ulst.ac.uk/~norman/aics05/AICS05_Proceedings_V3.pdf",
abstract = "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.",
notes = "http://www.infc.ulst.ac.uk/~norman/aics05/",
}
@Article{Cummins:2005:AIR,
author = "Ronan Cummins and Colm O'Riordan",
title = "Evolving General Term-Weighting Schemes for
Information Retrieval: Tests on Larger Collections",
journal = "Artificial Intelligence Review",
year = "2005",
volume = "24",
number = "3-4",
pages = "277--299",
month = nov,
email = "ronan.cummins@nuigalway.ie",
keywords = "genetic algorithms, genetic programming,
term-weighting schemes, Information Retrieval",
ISSN = "0269-2821",
doi = "doi:10.1007/s10462-005-9001-y",
abstract = "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.",
notes = "www.kluweronline.com/issn/0269-2821",
}
@Article{Cummins:2006:IR,
author = "Ronan Cummins and Colm O'Riordan",
title = "Evolving local and global weighting schemes in
information retrieval",
journal = "Information Retrieval",
year = "2006",
volume = "9",
number = "3",
pages = "311--330",
month = jun,
keywords = "genetic algorithms, genetic programming, Information
Retrieval, Term-Weighting Schemes",
ISSN = "1386-4564",
doi = "doi:10.1007/s10791-006-1682-6",
abstract = "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.",
}
@InProceedings{Cummins:2006:ECAI,
author = "Ronan Cummins and Colm O'Riordan",
title = "Term-Weighting in Information Retrieval using Genetic
Programming: {A} Three Stage Process",
booktitle = "The 17th European Conference on Artificial
Intelligence, ECAI-2006",
year = "2006",
editor = "Gerhard Brewka and Silvia Coradeschi and Anna Perini
and Paolo Traverso",
pages = "793--794",
address = "Riva del Garda, Italy",
month = aug # " 28th - " # sep # " 1st",
publisher = "IOS Press",
bibdate = "2006-10-25",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/ecai/ecai2006.html#CumminsO06",
keywords = "genetic algorithms, genetic programming, poster,
information retrieval, term-weighting",
ISBN = "1-58603-642-4",
URL = "http://ww2.it.nuigalway.ie/cirg/localpubs/CumminsECAI2006.pdf",
size = "2 pages",
notes = "ECAI-2001
http://ecai2006.itc.it/cda/aree/index.php?section=76&area=13",
}
@InProceedings{rc-tir06,
author = "Ronan Cummins and Colm O'Riordan",
title = "A Framework for the study of Evolved Term-Weighting
Schemes in Information Retrieval",
booktitle = "TIR-06 Text based Information Retrieval, Workshop.
ECAI 2006",
year = "2006",
editor = "Benno Stein and Odej Kao",
address = "Riva del Garda, Italy",
month = "29 " # aug,
keywords = "genetic algorithms, genetic programming, information
retrieval, phenotype distance",
URL = "http://ww2.it.nuigalway.ie/cirg/localpubs/CumminsECAI2006-Workshop.pdf",
URL = "http://www-ai.upb.de/aisearch/tir-06/proceedings/cummins06-framework-for-the-study-evolved-term-weighting-schemes-IR.pdf",
abstract = "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.",
notes = "TIR-06 http://www.aisearch.de/tir-06/",
}
@InProceedings{Cummins:2006:AICS,
author = "Ronan Cummins and Colm O'Riordan",
title = "An analysis of the Solution Space for Genetically
Programmed Term-Weighting Schemes in Information
Retrieval",
booktitle = "17th Irish Artificial Intelligence and Cognitive
Science Conference (AICS 2006)",
year = "2006",
editor = "D. A. Bell",
address = "Queen's University, Belfast",
month = "11th-13th " # sep,
organisation = "Artificial Intelligence Association of Ireland",
keywords = "genetic algorithms, genetic programming",
notes = "http://www.cs.qub.ac.uk/aics06/aics.html",
}
@InProceedings{1277390,
author = "Ronan Cummins and Colm O'Riordan",
title = "Using genetic programming for information retrieval:
local and global query expansion",
booktitle = "GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation",
year = "2007",
editor = "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",
volume = "2",
isbn13 = "978-1-59593-697-4",
pages = "2255--2255",
address = "London",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2255.pdf",
doi = "doi:10.1145/1276958.1277390",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
month = "7-11 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, Real-World
Applications: Poster, information retrieval,
query-expansion",
abstract = "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.",
notes = "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",
}
@InProceedings{Cummins:2007:AICS,
author = "Ronan Cummins and Colm O'Riordan",
title = "An Axiomatic Comparison of Learned Term-weighting
Schemes in Information Retrieval",
booktitle = "18th Irish Conference on Artificial Intelligence and
Cognitive Science",
year = "2007",
editor = "Sarah Jane Delany and Michael Madden",
address = "Dublin Institute of Technology",
month = "29-31 " # aug,
keywords = "genetic algorithms, genetic programming",
notes = "http://www.comp.dit.ie/aics07/program.html",
}
@Article{cummins:2007:AIR,
author = "Ronan Cummins and Colm O'Riordan",
title = "Evolved term-weighting schemes in Information
Retrieval: an analysis of the solution space",
journal = "Artificial Intelligence Review",
year = "2006",
volume = "26",
number = "1-2",
pages = "35--47",
month = oct,
keywords = "genetic algorithms, genetic programming, Information
Retrieval, Term-weighting schemes",
doi = "doi:10.1007/s10462-007-9034-5",
abstract = "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.",
notes = "Published online: 12 September 2007",
}
@Article{cummins:2007a:AIR,
author = "Ronan Cummins and Colm O'Riordan",
title = "An axiomatic comparison of learned term-weighting
schemes in information retrieval: clarifications and
extensions",
journal = "Artificial Intelligence Review",
year = "2007",
volume = "28",
number = "1",
pages = "51--68",
month = jun,
keywords = "genetic algorithms, genetic programming, Information
retrieval, Axiomatic constraints",
doi = "doi:10.1007/s10462-008-9074-5",
abstract = "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.",
notes = "Published online: 13 September 2008",
}
@InProceedings{Cummins:2007:SIGIR,
author = "Ronan Cummins and Colm O'Riordan",
title = "An Axiomatic Study of Learned Term-Weighting Schemes",
booktitle = "SIGIR 2007 workshop: Learning to Rank for Information
Retrieval",
year = "2007",
editor = "Thorsten Joachims and Hang Li and Tie-Yan Liu and
ChengXiang Zhai",
month = "27 " # jul,
organisation = "Microsoft",
keywords = "genetic algorithms, genetic programming",
URL = "http://ww2.it.nuigalway.ie/cirg/localpubs/axioms.pdf",
abstract = "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.",
notes = "https://research.microsoft.com/en-us/um/beijing/events/LR4IR-2007/",
}
@PhdThesis{Cummins:thesis,
author = "Ronan Cummins",
title = "The Evolution and Analysis of Term-Weighting Schemes
in Information Retrieval",
school = "National University of Ireland, Galway",
year = "2008",
month = may,
keywords = "genetic algorithms, genetic programming",
URL = "http://www3.it.nuigalway.ie/cirg/rcummins_thesis.pdf",
size = "201 pages",
abstract = "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.",
notes = "Supervisor: Colm O'Riordan",
}
@InProceedings{Cummins:2009:SIGIR,
author = "Ronan Cummins and Colm O'Riordan",
title = "Learning in a pairwise term-term proximity framework
for information retrieval",
booktitle = "SIGIR '09: Proceedings of the 32nd international ACM
SIGIR conference on Research and development in
information retrieval",
year = "2009",
editor = "James Allan and Javed Aslam",
pages = "251--258",
address = "Boston, MA, USA",
publisher_address = "New York, NY, USA",
publisher = "ACM",
keywords = "genetic algorithms, genetic programming, information
retrieval, learning to rank, proximity",
isbn13 = "978-1-60558-483-6",
doi = "doi:10.1145/1571941.1571986",
abstract = "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.",
notes = "Also known as \cite{1571986}",
}
@Article{Cunge:2003:JH,
author = "Jean A Cunge",
title = "Of data and models",
journal = "Journal of Hydroinformatics",
year = "2003",
volume = "5",
number = "2",
pages = "75--98",
month = apr,
keywords = "genetic algorithms, genetic programming",
ISSN = "1464-7141",
URL = "http://www.iwaponline.com/jh/005/0075/0050075.pdf",
size = "24 pages",
abstract = "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.",
notes = "GP amongst others",
}
@InCollection{cunningham:2003:UGAEWSO,
author = "Tucker Cunningham",
title = "Using the Genetic Algorithm to Evolve a Winning
Strategy for Othello",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2003",
year = "2003",
editor = "John R. Koza",
pages = "31--37",
address = "Stanford, California, 94305-3079 USA",
month = "4 " # dec,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms",
URL = "http://www.genetic-programming.org/sp2003/Cunningham.pdf",
notes = "part of \cite{koza:2003:gagp}",
}
@InProceedings{Cupertino:2011:CIGPU,
author = "Leandro F. Cupertino and Cleomar P. Silva and Douglas
M. Dias and Marco Aurelio C. Pacheco and Cristiana
Bentes",
title = "Evolving {CUDA} {PTX} programs by quantum inspired
linear genetic programming",
booktitle = "GECCO 2011 Computational intelligence on consumer
games and graphics hardware (CIGPU)",
year = "2011",
editor = "Simon Harding and W. B. Langdon and Man Leung Wong and
Garnett Wilson and Tony Lewis",
isbn13 = "978-1-4503-0690-4",
keywords = "genetic algorithms, genetic programming, EDA,
Artificial Intelligence, automatic programming, program
synthesis, Performance, GPU, CUDA, PTX,
quantum-inspired algorithms",
pages = "399--406",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001858.2002026",
publisher = "ACM",
publisher_address = "New York, NY, USA",
size = "8 pages",
abstract = "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.",
notes = "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 \cite{2002026} Distributed on CD-ROM at
GECCO-2011.
ACM Order Number 910112.",
}
@InProceedings{Curran:2010:gecco,
author = "Dara Curran and Eugene Freuder and Thomas Jansen",
title = "Incremental evolution of local search heuristics",
booktitle = "GECCO '10: Proceedings of the 12th annual conference
on Genetic and evolutionary computation",
year = "2010",
editor = "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",
isbn13 = "978-1-4503-0072-8",
pages = "981--982",
keywords = "genetic algorithms, genetic programming, incremental
evolution, genetic programming, local search
heuristics, graph colouring, hyperheuristics, Poster",
month = "7-11 " # jul,
organisation = "SIGEVO",
address = "Portland, Oregon, USA",
doi = "doi:10.1145/1830483.1830660",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Culberon's random graph generator.
Also known as \cite{1830660} GECCO-2010 A joint meeting
of the nineteenth international conference on genetic
algorithms (ICGA-2010) and the fifteenth annual genetic
programming conference (GP-2010)",
}
@InProceedings{currey:2004:CSCSI,
author = "Robert Curry and Malcolm I. Heywood",
title = "Towards Efficient Training on Large Datasets for
Genetic Programming",
booktitle = "17th Conference of the Canadian Society for
Computational Studies of Intelligence",
year = "2004",
editor = "Ahmed Y. Tawfik and Scott D. Goodwin",
volume = "3060",
series = "LNAI",
pages = "161--174",
address = "London, Ontario, Canada",
month = "17-19 " # may,
publisher = "Springer-Verlag",
email = "mheywood@cs.dal.ca",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-22004-6",
URL = "http://users.cs.dal.ca/~mheywood/X-files/Publications/robert-CaAI04.pdf",
URL = "http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3060&spage=161",
doi = "doi:10.1007/b97823",
abstract = "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
\cite{ga94aGathercole} 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.",
}
@Article{curry:2007:SMC,
author = "Robert Curry and Peter Lichodzijewski and Malcolm I.
Heywood",
title = "Scaling Genetic Programming to Large Datasets Using
Hierarchical Dynamic Subset Selection",
journal = "IEEE Transactions on Systems, Man, and Cybernetics:
Part B - Cybernetics",
year = "2007",
volume = "37",
number = "4",
pages = "1065--1073",
month = aug,
email = "mheywood@cs.dal.ca",
keywords = "genetic algorithms, genetic programming, active
learning, classification, unbalanced data, hierarchical
DSS, RSS, linear genetic programming, casGP",
ISSN = "1083-4419",
URL = "http://www.cs.dal.ca/~mheywood/X-files/GradPubs.html#curry",
doi = "doi:10.1109/TSMCB.2007.896406",
size = "9 pages",
abstract = "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.",
notes = "max prog length=8, comparsion with lilGP, binary
classification, unbalanced training sets, selecting
balanced training subsets, page based crossover",
}
@InProceedings{Curry:2007:SMCb,
author = "R. Curry and M. I. Heywood",
title = "One-Class Learning with Multi-Objective Genetic
Programming",
booktitle = "Proceedings of the IEEE International Conference on
Systems, Man, and Cybernetics",
year = "2007",
pages = "1938--1945",
address = "Montreal",
month = "7-10 " # oct,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, evolutionary
multi-criteria optimisation, one-class learning",
ISBN = "1-4244-0991-8",
URL = "http://users.cs.dal.ca/~mheywood/X-files/GradPubs.html#curry",
abstract = "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.",
notes = "http://www.smc2007.org/program.html
rcurry_SMC07.pdf is twenty pages",
URL = "http://users.cs.dal.ca/~mheywood/X-files/Publications/rcurry_SMC07.pdf",
}
@InProceedings{Curry:2009:eurogp,
author = "Robert Curry and Malcolm Heywood",
title = "One-Class Genetic Programming",
booktitle = "Proceedings of the 12th European Conference on Genetic
Programming, EuroGP 2009",
year = "2009",
editor = "Leonardo Vanneschi and Steven Gustafson and Alberto
Moraglio and Ivanoe {De Falco} and Marc Ebner",
volume = "5481",
series = "LNCS",
pages = "1--12",
address = "Tuebingen",
month = apr # " 15-17",
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-01180-1",
doi = "doi:10.1007/978-3-642-01181-8_1",
notes = "Part of \cite{conf/eurogp/2009} EuroGP'2009 held in
conjunction with EvoCOP2009, EvoBIO2009 and
EvoWorkshops2009",
}
@Article{cus:2003:ME,
author = "Franci Cus and Joze Balic and Uros Zuperl",
title = "Genetic algorithm based optimisation of end milling
parameters",
journal = "Machine Engineering",
year = "2003",
volume = "3",
number = "1/2",
pages = "116--126",
keywords = "genetic algorithms",
ISSN = "1642-6568",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/cus_2003_ME.pdf",
size = "10 pages",
abstract = "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.",
notes = "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
\cite{kusiak:2001:ME}",
}
@Article{cus:2003:RCIM,
author = "Franci Cus and Joze Balic",
title = "Optimization of cutting process by {GA} approach",
journal = "Robotics and Computer-Integrated Manufacturing",
year = "2003",
volume = "19",
number = "1-2",
pages = "113--121",
month = feb # "-" # apr,
keywords = "genetic algorithms, genetic programming, Cutting
parameters, Manufacturing, simulation",
ISSN = "0736-5845",
doi = "doi:10.1016/S0736-5845(02)00068-6",
abstract = "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.",
notes = "http://www.elsevier.com/wps/find/journaldescription.cws_home/704/description#description",
}
@Article{cus:2004:ME,
author = "Franc Cus and Matjaz Milfelner and Joze Balic",
title = "Optimization of cutting forces in ball-end milling by
{GA}",
journal = "Machine Engineering",
year = "2004",
volume = "4",
number = "1/2",
pages = "281--288",
keywords = "genetic algorithms, genetic programming",
ISSN = "1642-6568",
abstract = "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.",
notes = "Also appears as: {"}Machine tools and factories of the
knowledge{"}, Jerzy Jedrzejewsk (editor).
For Machine Engineering journal see also
\cite{kusiak:2001:ME}",
}
@Article{Cus200690,
author = "F. Cus and M. Milfelner and J. Balic",
title = "An intelligent system for monitoring and optimization
of ball-end milling process",
journal = "Journal of Materials Processing Technology",
volume = "175",
number = "1-3",
pages = "90--97",
year = "2006",
note = "Achievements in Mechanical and Materials Engineering",
ISSN = "0924-0136",
doi = "DOI:10.1016/j.jmatprotec.2005.04.041",
URL = "http://www.sciencedirect.com/science/article/B6TGJ-4GJKTR6-4/2/5b1e17c8ac5f2a7435ab419b4db98260",
keywords = "Genetic algorithm, Ball-end milling, Cutting forces,
Monitoring, Optimization",
abstract = "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.",
}
@InProceedings{alifexi_cussatblanc_134,
author = "Sylvain Cussat-Blanc and Herve Luga and Yves Duthen",
title = "From single cell to simple creature morphology and
metabolism",
editor = "S. Bullock and J. Noble and R. Watson and M. A.
Bedau",
booktitle = "Artificial Life XI: Proceedings of the Eleventh
International Conference on the Simulation and
Synthesis of Living Systems",
publisher = "MIT Press",
month = "5-8 " # aug,
address = "Winchester, Hants",
publisher_address = "Cambridge, MA, USA",
year = "2008",
pages = "134--141",
isbn13 = "978-0-262-75017-2",
URL = "http://www.alifexi.org/papers/ALIFExi_pp134-141.pdf",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{Cussat-Blanc:2009:cec,
author = "Sylvain Cussat-Blanc and Herve Luga and Yves Duthen",
title = "Cell2Organ: Self-Repairing Artificial Creatures Thanks
to a Healthy Metabolism",
booktitle = "2009 IEEE Congress on Evolutionary Computation",
year = "2009",
editor = "Andy Tyrrell",
pages = "-",
address = "Trondheim, Norway",
month = "18-21 " # may,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-2959-2",
file = "P391.pdf",
abstract = "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 \cite{alifexi_cussatblanc_134}.",
keywords = "genetic algorithms, genetic programming",
notes = "Gene Regulartory Network GRN, promoter, enhance,
inhibitor. Java. Grid5000.fr powered parallel GA
\cite{Cussat-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",
}
@InProceedings{cvetkovic:1999:UPGMO,
author = "Dragan Cvetkovic and Ian C. Parmee",
title = "Use of Preferences for {GA}-based Multi-objective
Optimisation",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1504--1509",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "real world applications",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-764.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-764.pdf",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{1144158,
author = "Luis E. {Da Costa} and Jacques-Andre Landry",
title = "Relaxed genetic programming",
booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference
on Genetic and evolutionary computation",
year = "2006",
editor = "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",
volume = "1",
ISBN = "1-59593-186-4",
pages = "937--938",
address = "Seattle, Washington, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p937.pdf",
doi = "doi:10.1145/1143997.1144158",
publisher = "ACM Press",
publisher_address = "New York, NY, 10286-1405, USA",
month = "8-12 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming: Poster,
bloat, generalisation error, measurement",
notes = "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",
}
@InProceedings{conf/ae/CostaLL07,
title = "Treating Noisy Data Sets with Relaxed Genetic
Programming",
author = "Luis E. {Da Costa} and Jacques-Andre Landry and Yan
Levasseur",
year = "2007",
volume = "4926",
bibdate = "2008-05-16",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/ae/ae2007.html#CostaLL07",
booktitle = "Artificial Evolution",
editor = "Nicolas Monmarch{\'e} and El-Ghazali Talbi and Pierre
Collet and Marc Schoenauer and Evelyne Lutton",
isbn13 = "978-3-540-79304-5",
pages = "1--12",
series = "Lecture Notes in Computer Science",
address = "Tours, France",
month = "31-29 " # oct,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1007/978-3-540-79305-2_1",
abstract = "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.",
notes = "EA'07",
}
@Article{Daga:2009:NH,
author = "Mansi Daga and M. C. Deo",
title = "Alternative data-driven methods to estimate wind from
waves by inverse modeling",
journal = "Natural Hazards",
year = "2009",
volume = "49",
number = "2",
pages = "293--310",
month = may,
keywords = "genetic algorithms, genetic programming, Locally
weighted learning, Model trees, Inverse modeling, Wind
estimation, LWOR, MT, GP",
ISSN = "0921-030X",
doi = "doi:10.1007/s11069-008-9299-2",
size = "18 pages",
abstract = "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.",
notes = "Discipulus. Goa, Minicoy Island, Marmagoa. Storm
modelling
Department of Civil Engineering, Indian Institute of
Technology, Bombay, Mumbai, 400076, India",
}
@InProceedings{daida:1995:bsmch,
author = "J. M. Daida and S. J. Ross and B. C. Hannan",
title = "Biological Symbiosis as a Metaphor for Computational
Hybridization",
booktitle = "Genetic Algorithms: Proceedings of the Sixth
International Conference (ICGA95)",
year = "1995",
editor = "Larry J. Eshelman",
pages = "248--255",
address = "Pittsburgh, PA, USA",
publisher_address = "San Francisco, CA, USA",
month = "15-19 " # jul,
publisher = "Morgan Kaufmann",
keywords = "Genetic Algorithms",
ISBN = "1-55860-370-0",
URL = "ftp://ftp.eecs.umich.edu/people/daida/papers/icga95.pdf",
size = "8 pages",
}
@InProceedings{Daida:1995:SARice,
author = "J. M. Daida and J. D. Hommes and S. J. Ross and J. F.
Vesecky",
title = "Extracting curvilinear features from {SAR} images of
arctic ice: Algorithm discovery using the genetic
programming paradigm",
booktitle = "Proceedings of IEEE International Geoscience and
Remote Sensing",
year = "1995",
editor = "T. Stein",
pages = "673--675",
address = "Florence, Italy",
publisher_address = "Washington",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
URL = "ftp://ftp.eecs.umich.edu/people/daida/papers/igarss95_GP.pdf",
URL = "http://citeseer.ist.psu.edu/406479.html",
notes = "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",
}
@InProceedings{Daida:1995:ehspsSAR,
author = "J. M. Daida and A. Freeman and R. Onstott",
title = "Evaluation of hybrid symbiotic systems on segmenting
{SAR} imagery",
booktitle = "Proceedings of IEEE International Geoscience and
Remote Sensing",
year = "1995",
editor = "T. Stein",
pages = "1415--1417",
address = "Florence, Italy",
publisher_address = "Washington",
publisher = "IEEE Press",
keywords = "genetic algorithms",
URL = "ftp://ftp.eecs.umich.edu/people/daida/papers/igarss95_symbiosis.pdf",
notes = "
Invited Paper Firenze, Italy",
}
@InProceedings{Daida:1995:mtssw,
author = "J. M. Daida and D. E. Lund and C. Wolf and G. A.
Meadows and K. Schroeder and J. F. Vesecky and D. R.
Lyzenga and R. Bertram",
title = "Measuring topography of small-scale waves",
booktitle = "Proceedings of IEEE International Geoscience and
Remote Sensing",
year = "1995",
editor = "T. Stein",
pages = "1881--1883",
address = "Florence, Italy",
publisher_address = "Washington",
publisher = "IEEE Press",
keywords = "genetic algorithms",
URL = "ftp://ftp.eecs.umich.edu/people/daida/papers/igarss95_GA.pdf",
notes = "
Firenze, Italy",
}
@InCollection{daida:1996:aigp2,
author = "Jason M. Daida and Jonathan D. Hommes and Tommaso F.
Bersano-Begey and Steven J. Ross and John F. Vesecky",
title = "Algorithm Discovery Using the Genetic Programming
Paradigm: Extracting Low-Contrast Curvilinear Features
from {SAR} Images of Arctic Ice",
booktitle = "Advances in Genetic Programming 2",
publisher = "MIT Press",
year = "1996",
editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}",
pages = "417--442",
chapter = "21",
address = "Cambridge, MA, USA",
keywords = "genetic algorithms, genetic programming, GAIA",
ISBN = "0-262-01158-1",
URL = "http://sitemaker.umich.edu/daida/files/GP2_cha21.pdf",
URL = "http://cisnet.mit.edu/Advances-in-Genetic-Programming/434",
abstract = "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.",
notes = "see also
http://www.sprl.umich.edu/acers/gaia/aigpGaia.html",
}
@InProceedings{daida:1996:scas,
author = "J. M. Daida and C. S. Grasso and S. A. Stanhope and S.
J. Ross",
title = "Symbionticism and Complex Adaptive Systems {I}:
Implications of Having Symbiosis Occur in Nature",
booktitle = "Evolutionary Programming V: Proceedings of the Fifth
Annual Conference on Evolutionary Programming",
year = "1996",
editor = "Lawrence J. Fogel and Peter J. Angeline and Thomas
Baeck",
pages = "177--186",
address = "San Diego",
publisher_address = "Cambridge, MA, USA",
month = feb # " 29-" # mar # " 3",
publisher = "MIT Press",
ISBN = "0-262-06190-2",
URL = "ftp://ftp.eecs.umich.edu/people/daida/papers/EP96_symbiosis.pdf",
notes = "EP-96, Invited Paper
http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=8383",
}
@InProceedings{daida:1996:cadic,
author = "Jason M. Daida and Tommaso F. Bersano-Begey and Steven
J. Ross and John F. Vesecky",
title = "Computer-Assisted Design of Image Classification
Algorithms: Dynamic and Static Fitness Evaluations in a
Scaffolded Genetic Programming Environment",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "279--284",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
URL = "ftp://ftp.eecs.umich.edu/people/daida/papers/GP96_image.pdf",
size = "6 pages",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96",
}
@InProceedings{daida:1996:ircERSSARias,
author = "J. M. Daida and R. G. Onstott and T. F. Bersano-Begey
and S. J. Ross and J. F. Vesecky",
title = "Ice Roughness Classification and {ERS} {SAR} Imagery
of Arctic Sea Ice: Evaluation of Feature-Extraction
Algorithms by Genetic Programming",
booktitle = "Proceedings of the 1996 International Geoscience and
Remote Sensing Symposium",
year = "1996",
pages = "1520--1522",
publisher_address = "Washington",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
URL = "ftp://ftp.eecs.umich.edu/people/daida/papers/igarss96_GP_Valid.pdf",
}
@InProceedings{daida:1996:efxa,
author = "J. M. Daida and T. F. Bersano-Begey and S. J. Ross and
J. F. Vesecky",
title = "Evolving Feature-Extraction Algorithms: Adapting
Genetic Programming for Image Analysis in Geoscience
and Remote Sensing",
booktitle = "Proceedings of the 1996 International Geoscience and
Remote Sensing Symposium",
year = "1996",
pages = "2077--2079",
publisher_address = "Washington",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
URL = "ftp://ftp.eecs.umich.edu/people/daida/papers/igarss96_GP.pdf",
}
@InProceedings{daida:1996:,
author = "J. M. Daida and R. R. Bertram and D. R. Lyzenga and C.
Wolf and D. T. Walker and S. A. Stanhope and G. A.
Meadows and J. F. Vesecky and D. E. Lund",
title = "Measuring Small-Scale Water Surface Waves: Nonlinear
Interpolation and Integration Techniques for Slope
Image Data",
booktitle = "Proceedings of the 1996 International Geoscience and
Remote Sensing Symposium",
year = "1996",
pages = "2219--2221",
publisher_address = "Washington",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
URL = "ftp://ftp.eecs.umich.edu/people/daida/papers/igarss96_GA/igarss96_GAfig.pdf",
notes = "note: these pages are reverse ordered",
}
@InProceedings{daida:1997:vrmGP,
author = "Jason Daida and Steven Ross and Jeffrey McClain and
Derrick Ampy and Michael Holczer",
title = "Challenges with Verification, Repeatability, and
Meaningful Comparisons in Genetic Programming",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "64--69",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
URL = "ftp://ftp.eecs.umich.edu/people/daida/papers/GP97challenges.pdf",
notes = "GP-97",
}
@InProceedings{Daida:1997:taging,
author = "Jason M. Daida and Robert R. Bertram and Catherine S.
Grasso and Stephen A. Stanhope",
title = "Tagging as a Means for Self-Adaptive Hybridization",
booktitle = "Late Breaking Papers at the 1997 Genetic Programming
Conference",
year = "1997",
editor = "John R. Koza",
pages = "42--50",
address = "Stanford University, CA, USA",
publisher_address = "Stanford University, Stanford, California,
94305-3079, USA",
month = "13--16 " # jul,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-206995-8",
notes = "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",
}
@InCollection{daida:1999:aigp3,
author = "Jason M. Daida and Robert R. Bertram and John A.
{Polito~2} and Stephen A. Stanhope",
title = "Analysis of Single-Node (Building) Blocks in Genetic
Programming",
booktitle = "Advances in Genetic Programming 3",
publisher = "MIT Press",
year = "1999",
editor = "Lee Spector and William B. Langdon and Una-May
O'Reilly and Peter J. Angeline",
chapter = "10",
pages = "217--241",
address = "Cambridge, MA, USA",
month = jun,
keywords = "genetic algorithms, genetic programming",
ISBN = "0-262-19423-6",
URL = "http://www.cs.bham.ac.uk/~wbl/aigp3/ch10.pdf",
language = "en",
oai = "oai:CiteSeerXPSU:10.1.1.141.1123",
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.1123",
abstract = "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.",
notes = "AiGP3",
}
@InProceedings{daida:1999:fogp,
author = "Jason M. Daida",
title = "Reconnoiter by Candle: Identifying Assumptions in
Genetic Programming",
booktitle = "Foundations of Genetic Programming",
year = "1999",
editor = "Thomas Haynes and William B. Langdon and Una-May
O'Reilly and Riccardo Poli and Justinian Rosca",
pages = "53--54",
address = "Orlando, Florida, USA",
month = "13 " # jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/fogp/daida.ps.gz",
size = "2 pages",
notes = "GECCO'99 WKSHOP, part of \cite{haynes:1999:fogp}
GECCO-99WKS Part of wu:1999:GECCOWKS",
}
@InProceedings{daida:1999:CVRMCGPGM,
author = "Jason M. Daida and Derrick S. Ampy and Michael
Ratanasavetavadhana and Hsiaolei Li and Omar A.
Chaudhri",
title = "Challenges with Verification, Repeatability, and
Meaningful Comparison in Genetic Programming: Gibson's
Magic",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1851--1858",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, methodology,
pedagogy and philosophy",
ISBN = "1-55860-611-4",
URL = "ftp://ftp.eecs.umich.edu/people/daida/papers/GECCO99challenges.pdf",
URL = "http://citeseer.ist.psu.edu/257412.html",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/MP-604.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/MP-604.ps",
abstract = "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.",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{daida:1999:MSWMPGATDPGP,
author = "Jason M. Daida and John A. Polito and Steven A.
Stanhope and Robert R. Bertram and Jonathan C. Khoo and
Shahbaz A. Chaudhary",
title = "What Makes a Problem {GP}-Hard? Analysis of a Tunably
Difficult Problem in Genetic Programming",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "982--989",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-611-4",
URL = "ftp://ftp.eecs.umich.edu/people/daida/papers/GECCO99landscape.pdf",
URL = "http://citeseer.ist.psu.edu/240700.html",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-444.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-444.ps",
abstract = "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.",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{daida:1999:OMDDGPC,
author = "Jason M. Daida and Seth P. Yalcin and Paul M. Litvak
and Gabriel A. Eickhoff and John A. Polito",
title = "Of Metaphors and Darwinism: Deconstructing Genetic
Programming's Chimera",
booktitle = "Proceedings of the Congress on Evolutionary
Computation",
year = "1999",
editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc
Schoenauer and Xin Yao and Ali Zalzala",
volume = "1",
pages = "453--462",
address = "Mayflower Hotel, Washington D.C., USA",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "6-9 " # jul,
organisation = "Congress on Evolutionary Computation, IEEE / Neural
Networks Council, Evolutionary Programming Society,
Galesia, IEE",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, biomodeling",
ISBN = "0-7803-5536-9 (softbound)",
ISBN = "0-7803-5537-7 (Microfiche)",
URL = "ftp://ftp.eecs.umich.edu/people/daida/papers/CEC99metaphors.pdf",
URL = "http://citeseer.ist.psu.edu/242099.html",
size = "10 pages",
abstract = "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.",
notes = "CEC-99 - A joint meeting of the IEEE, Evolutionary
Programming Society, Galesia, and the IEE.
Library of Congress Number = 99-61143",
}
@Article{daida:2001:GPEM,
author = "Jason M. Daida and Robert R. Bertram and Stephen A.
Stanhope and Jonathan C. Khoo and Shahbaz A. Chaudhary
and Omer A. Chaudhri and John A. {Polito II}",
title = "What Makes a Problem {GP}-Hard? Analysis of a Tunably
Difficult Problem in Genetic Programming",
journal = "Genetic Programming and Evolvable Machines",
year = "2001",
volume = "2",
number = "2",
pages = "165--191",
month = jun,
keywords = "genetic algorithms, genetic programming, problem
difficulty, test problems, fitness landscapes, GP
theory",
ISSN = "1389-2576",
broken = "http://ipsapp009.lwwonline.com/content/getfile/4723/5/5/fulltext.pdf",
doi = "doi:10.1023/A:1011504414730",
abstract = "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.",
notes = "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",
}
@InProceedings{daida:2002:lteigplm,
author = "Jason M. Daida",
title = "Limits to Expression in Genetic Programming:
Lattice-Aggregate Modeling",
booktitle = "Proceedings of the 2002 Congress on Evolutionary
Computation CEC2002",
editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
and Garry Greenwood and Hitoshi Iba and Paul Marrow and
Mark Shackleton",
pages = "273--278",
year = "2002",
publisher = "IEEE Press",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
organisation = "IEEE Neural Network Council (NNC), Institution of
Electrical Engineers (IEE), Evolutionary Programming
Society (EPS)",
ISBN = "0-7803-7278-6",
month = "12-17 " # may,
notes = "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)",
keywords = "genetic algorithms, genetic programming",
URL = "http://sitemaker.umich.edu/daida/files/CEC7272.pdf",
abstract = "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.",
}
@InCollection{daida:2003:GPTP,
author = "Jason M. Daida",
title = "What Makes a Problem {GP}-Hard? {A} Look at How
Structure Affects Content",
booktitle = "Genetic Programming Theory and Practice",
publisher = "Kluwer",
year = "2003",
editor = "Rick L. Riolo and Bill Worzel",
chapter = "7",
pages = "99--118",
keywords = "genetic algorithms, genetic programming, GP theory,
tree structures, problem difficulty, GP-hard, test
problems",
abstract = "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.",
notes = "great pictures Part of \cite{RioloWorzel:2003}",
size = "19 pages",
}
@InProceedings{daida0:2003:gecco,
author = "Jason M. Daida and Adam M. Hilss",
title = "Identifying Structural Mechanisms in Standard Genetic
Programming",
booktitle = "Genetic and Evolutionary Computation -- GECCO-2003",
editor = "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",
year = "2003",
pages = "1639--1651",
address = "Chicago",
publisher_address = "Berlin",
month = "12-16 " # jul,
volume = "2724",
series = "LNCS",
ISBN = "3-540-40603-4",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
URL = "http://sitemaker.umich.edu/daida/files/LNCS2724lattice.pdf",
abstract = "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.",
notes = "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 \cite{daida:2003:gecco}",
}
@InProceedings{daida:2003:gecco,
author = "Jason M. Daida and Adam M. Hilss and David J. Ward and
Stephen L. Long",
title = "Visualizing Tree Structures in Genetic Programming",
booktitle = "Genetic and Evolutionary Computation -- GECCO-2003",
editor = "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",
year = "2003",
pages = "1652--1664",
address = "Chicago",
publisher_address = "Berlin",
month = "12-16 " # jul,
volume = "2724",
series = "LNCS",
ISBN = "3-540-40603-4",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
URL = "http://sitemaker.umich.edu/daida/files/LNCS2724viz.pdf",
size = "13 pages",
abstract = "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.",
notes = "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",
}
@InProceedings{daida3:2003:gecco,
author = "Jason M. Daida and Hsiaolei Li and Ricky Tang and Adam
M. Hilss",
title = "What Makes a Problem {GP}-Hard? Validating a
Hypothesis of Structural Causes",
booktitle = "Genetic and Evolutionary Computation -- GECCO-2003",
editor = "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",
year = "2003",
pages = "1665--1677",
address = "Chicago",
publisher_address = "Berlin",
month = "12-16 " # jul,
volume = "2724",
series = "LNCS",
ISBN = "3-540-40603-4",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "GECCO-2003. A joint meeting of the twelfth
International Conference on Genetic Algorithms
(ICGA-2003) and the eighth Annual Genetic Programming
Conference (GP-2003)",
}
@InCollection{daida:2004:GPTP,
author = "Jason Daida",
title = "Considering the Roles of Structure in Problem Solving
by a Computer",
booktitle = "Genetic Programming Theory and Practice {II}",
year = "2004",
editor = "Una-May O'Reilly and Tina Yu and Rick L. Riolo and
Bill Worzel",
chapter = "5",
pages = "67--86",
address = "Ann Arbor",
month = "13-15 " # may,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, GP theory,
tree structures, problem difficulty, GP-hard, test
problems, Lid, Highlander, Binomial-3",
ISBN = "0-387-23253-2",
doi = "doi:10.1007/0-387-23254-0_5",
abstract = "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.",
notes = "part of \cite{oreilly:2004:GPTP2}",
}
@InProceedings{daida:2004:dctdwatdpfgp,
title = "Demonstrating Constraints to Diversity with a Tunably
Difficulty Problem for Genetic Programming",
author = "Jason M. Daida and Michael E. Samples and Bryan T.
Hart and Jeffry Halim and Aditya Kumar",
pages = "1217--1224",
booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary
Computation",
year = "2004",
publisher = "IEEE Press",
month = "20-23 " # jun,
address = "Portland, Oregon",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming, Theoretical
Foundations of Evolutionary Computation",
URL = "http://sitemaker.umich.edu/daida/files/CEC04highlander.pdf",
abstract = "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.",
size = "8 pages",
notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and
the IEE.",
}
@InProceedings{daida:2004:vtlodigp,
title = "Visualizing the Loss of Diversity in Genetic
Programming",
author = "Jason M. Daida and David J. Ward and Adam M. Hilss and
Stephen L. Long and Mark R. Hodges and Jason T.
Kriesel",
pages = "1225--1232",
booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary
Computation",
year = "2004",
publisher = "IEEE Press",
month = "20-23 " # jun,
address = "Portland, Oregon",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming, Theoretical
Foundations of Evolutionary Computation",
URL = "http://sitemaker.umich.edu/daida/files/CEC04viz.pdf",
abstract = "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.",
notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and
the IEE.",
}
@Article{daida:2005:GPEM,
author = "Jason M. Daida and Adam M. Hilss and David J. Ward and
Stephen L. Long",
title = "Visualizing Tree Structures in Genetic Programming",
journal = "Genetic Programming and Evolvable Machines",
year = "2005",
volume = "6",
number = "1",
pages = "79--110",
month = mar,
keywords = "genetic algorithms, genetic programming",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-005-7621-2",
size = "32 pages",
abstract = "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.",
notes = "
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",
}
@InCollection{daida:2005:GPTP,
author = "Jason Daida",
title = "Challenges in Open-Ended Problem Solving with Genetic
Programming",
booktitle = "Genetic Programming Theory and Practice {III}",
year = "2005",
editor = "Tina Yu and Rick L. Riolo and Bill Worzel",
volume = "9",
series = "Genetic Programming",
chapter = "17",
pages = "259--274",
address = "Ann Arbor",
month = "12-14 " # may,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, open-ended
problem solving, McMaster Problem Solving",
ISBN = "0-387-28110-X",
size = "16 pages",
abstract = "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.",
notes = "part of \cite{yu:2005:GPTP} Published Jan 2006 after
the workshop",
}
@InProceedings{1068284,
author = "Jason M. Daida",
title = "Towards identifying populations that increase the
likelihood of success in genetic programming",
booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on
Genetic and evolutionary computation",
year = "2005",
editor = "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",
volume = "2",
ISBN = "1-59593-010-8",
pages = "1627--1634",
address = "Washington DC, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1627.pdf",
doi = "doi:10.1145/1068009.1068284",
publisher = "ACM Press",
publisher_address = "New York, NY, 10286-1405, USA",
month = "25-29 " # jun,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, binomial-3,
building blocks, experimentation, genetic programming
problem difficulty, initial populations, performance,
population dynamics, selection methods, theory",
notes = "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",
}
@InProceedings{1068295,
author = "Jason M. Daida and Michael E. Samples and Matthew J.
Byom",
title = "Probing for limits to building block mixing with a
tunably-difficult problem for genetic programming",
booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on
Genetic and evolutionary computation",
year = "2005",
editor = "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",
volume = "2",
ISBN = "1-59593-010-8",
pages = "1713--1720",
address = "Washington DC, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1713.pdf",
doi = "doi:10.1145/1068009.1068295",
publisher = "ACM Press",
publisher_address = "New York, NY, 10286-1405, USA",
month = "25-29 " # jun,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, building
blocks, experimentation, highlander problem, initial
populations, performance, tunably-difficult problems,
theory",
notes = "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",
}
@InCollection{Daida:2006:GPTP,
author = "Jason M. Daida and Ricky Tang and Michael E. Samples
and Matthew J. Byom",
title = "Phase Transitions in Genetic Programming Search",
booktitle = "Genetic Programming Theory and Practice {IV}",
year = "2006",
editor = "Rick L. Riolo and Terence Soule and Bill Worzel",
volume = "5",
series = "Genetic and Evolutionary Computation",
chapter = "1",
pages = "-",
address = "Ann Arbor",
month = "11-13 " # may,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-387-33375-4",
abstract = "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.",
notes = "part of \cite{Riolo:2006:GPTP} Published Jan 2007
after the workshop",
}
@InProceedings{1144140,
author = "Jason M. Daida",
title = "Characterizing the dynamics of symmetry breaking in
genetic programming",
booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference
on Genetic and evolutionary computation",
year = "2006",
editor = "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",
volume = "1",
ISBN = "1-59593-186-4",
pages = "799--806",
address = "Seattle, Washington, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p799.pdf",
doi = "doi:10.1145/1143997.1144140",
publisher = "ACM Press",
publisher_address = "New York, NY, 10286-1405, USA",
month = "8-12 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, analysis
methods, computational geometry, data structures,
design patterns, graphics techniques, languages,
measurement, patterns, program synthesis, symmetry
breaking, synthesis, theory, tree",
notes = "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",
}
@InProceedings{dain:1997:GPmrwfa,
author = "Robert A. Dain",
title = "Genetic Programming For Mobile Robot Wall-Following
Algorithms",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "70",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/dain_1997_GPmrwfa.pdf",
size = "1 page",
notes = "GP-97",
}
@Article{dain:1998:GPmrwfa,
author = "Robert A. Dain",
title = "Developing Mobile Robot Wall-Folowing Algorithms Using
Genetic Programming",
journal = "Applied Intelligence",
year = "1998",
volume = "8",
number = "5",
pages = "33--41",
month = jan,
keywords = "genetic algorithms, genetic programming, computational
genetics, machine learning, adaptive systems",
ISSN = "0924-669X",
notes = "Special Issues on Evolutionary Learning, Xin Yao and
Don Potter, Guest Editors",
}
@InCollection{dain:1999:dmrwaugp,
author = "Robert A. Dain",
title = "Develoopment of Mobile Robot Wall-Following Algorithms
using Genetic Programming",
year = "1999",
pages = "269--283",
booktitle = "Industrial Applications of Genetic Algorithms",
editor = "Charles L. Karr and L. Michael Freeman",
address = "Boca Raton, FL, USA",
publisher = "CRC Press",
series = "Computational Intelligence",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-8493-9801-0",
size = "pages",
}
@TechReport{dallaway:1993:GPcm,
author = "Richard Dallaway",
title = "Genetic programming and cognitive models",
institution = "School of Cognitive \& Computing Sciences, University
of Sussex,",
year = "1993",
number = "CSRP 300",
address = "Brighton, UK",
note = "In: Brook \& Arvanitis, eds., 1993 The Sixth White
House Papers: Graduate Research in the Cognitive \&
Computing Sciences at Sussex",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.dallaway.com/acad/evolution/evocog.html",
abstract = "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.",
notes = "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",
}
@Article{Damasevicius2010633,
author = "Robertas Damasevicius",
title = "Structural analysis of regulatory {DNA} sequences
using grammar inference and Support Vector Machine",
journal = "Neurocomputing",
volume = "73",
number = "4-6",
pages = "633--638",
year = "2010",
note = "Bayesian Networks / Design and Application of Neural
Networks and Intelligent Learning Systems (KES 2008 /
Bio-inspired Computing: Theories and Applications
(BIC-TA 2007)",
ISSN = "0925-2312",
doi = "doi:10.1016/j.neucom.2009.09.018",
URL = "http://www.sciencedirect.com/science/article/B6V10-4XRYT4P-1/2/2e5b008bc8df4d5a39553b40fe6728c3",
keywords = "genetic algorithms, genetic programming, DNA sequence
analysis, Grammar inference, L-grammar, Support Vector
Machine, SVM",
abstract = "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.",
notes = "TATA box",
}
@InProceedings{Danielson:2006:alife,
author = "Peter Danielson",
title = "From Artificial Morality to {NERD}: Models,
Experiments, \& Robust Reflective Equilibrium",
booktitle = "Artificial Life X. Workshop Proceedings",
year = "2006",
pages = "45--48",
address = "Bloomington, IN, USA",
month = "3-7 " # jun,
URL = "http://www.alifex.org/program/wkshp_proceed.pdf",
abstract = "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.",
}
@InProceedings{darabos:evobio12,
author = "Christian Darabos and Mario Giacobini and Ting Hu and
Jason H. Moore",
title = "{L}\'evy-flight {GP}: towards a new mutation
paradigm",
booktitle = "10th European Conference on Evolutionary Computation,
Machine Learning and Data Mining in Bioinformatics,
{EvoBIO 2012}",
year = "2012",
month = "11-13 " # apr,
editor = "Mario Giacobini and Leonardo Vanneschi and William S.
Bush",
series = "LNCS",
volume = "7246",
publisher = "Springer Verlag",
address = "Malaga, Spain",
pages = "38--49",
organisation = "EvoStar",
isbn13 = "978-3-642-29065-7",
doi = "doi:10.1007/978-3-642-29066-4_4",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "Part of \cite{Giacobini:2012:EvoBio} EvoBio'2012 held
in conjunction with EuroGP2012, EvoCOP2012,
EvoMusArt2012 and EvoApplications2012",
}
@InProceedings{icec96darwen,
author = "Paul Darwen and Xin Yao",
title = "Automatic Modularization by Speciation",
booktitle = "Third IEEE International Conference on Evolutionary
Computation",
year = "1996",
publisher = "IEEE press",
keywords = "genetic algorithms",
URL = "http://www.demo.cs.brandeis.edu/papers/icec96darwen.ps.gz",
abstract = "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.",
}
@PhdThesis{Das:thesis,
author = "Abhishek Das",
title = "Analyses of Crash Occurrence and Injury Severities on
Multi Lane Highways using Machine Learning Algorithms",
school = "Department of Civil, Environmental, and Construction
Engineering (CECE) of the University of Central
Florida",
year = "2009",
address = "Orlando, USA",
month = "13 " # oct,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cecs.ucf.edu/graddefense/pdf/10",
abstract = "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.",
}
@Article{Das2010548,
author = "Abhishek Das and Mohamed Abdel-Aty",
title = "A genetic programming approach to explore the crash
severity on multi-lane roads",
journal = "Accident Analysis \& Prevention",
volume = "42",
number = "2",
pages = "548--557",
year = "2010",
ISSN = "0001-4575",
doi = "doi:10.1016/j.aap.2009.09.021",
URL = "http://www.sciencedirect.com/science/article/B6V5S-4XFXSWB-3/2/d3dd6df818f461070f758ebe4fb9f1f3",
keywords = "genetic algorithms, genetic programming, Crash
severity, Multi-lane roads, Genetic algorithm,
Discipulus",
abstract = "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.",
}
@Article{Das2011,
author = "Abhishek Das and Mohamed A. Abdel-Aty",
title = "A combined frequency-severity approach for the
analysis of rear-end crashes on urban arterials",
journal = "Safety Science",
note = "In Press, Corrected Proof",
year = "2011",
ISSN = "0925-7535",
doi = "doi:10.1016/j.ssci.2011.03.007",
URL = "http://www.sciencedirect.com/science/article/B6VF9-52T1BCG-2/2/dbc605442a050a3d5a59a825025f0f40",
keywords = "genetic algorithms, genetic programming, Arterial
safety, Injury severity, Crash frequency, Sensitivity
analysis",
abstract = "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.",
}
@InProceedings{Das:2009:DATE,
author = "Angan Das and Ranga Vemuri",
title = "A graph grammar based approach to automated
multi-objective analog circuit design",
booktitle = "Design, Automation Test in Europe Conference
Exhibition, DATE '09.",
year = "2009",
month = "20-24 " # apr,
pages = "700--705",
URL = "http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=&arnumber=5090755",
abstract = "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.",
keywords = "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",
ISSN = "1530-1591",
notes = "Also known as \cite{5090755}",
}
@InProceedings{das:GPVR,
author = "Sumit Das and Terry Franguidakis and Michael Papka and
Thomas A. DeFanti and Daniel J. Sandin",
title = "A genetic programming application in virtual reality",
booktitle = "Proceedings of the first IEEE Conference on
Evolutionary Computation",
year = "1994",
publisher = "IEEE Press",
volume = "1",
note = "Part of 1994 IEEE World Congress on Computational
Intelligence, Orlando, Florida",
pages = "480--484",
address = "Orlando, Florida, USA",
month = "27-29 " # jun,
organisation = "IEEE",
size = "5 pages",
keywords = "genetic algorithms, genetic programming",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/797/http:zSzzSzwww.evl.uic.eduzSzEVLzSzRESEARCHzSzPAPERSzSzPAPKAzSzgp94.pdf/a-genetic-programming-application.pdf",
URL = "http://citeseer.ist.psu.edu/8701.html",
abstract = "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.",
notes = "Displays 4 simple geometric 3dee items in virtual
reality CAVE. User breeds from those he likes.",
}
@InProceedings{dasgupta:1999:AIASR,
author = "Dipankar Dasgupta and Yuehua Cao and Congjun Yang",
title = "An Immunogenetic Approach to Spectra Recognition",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "149--155",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{Dasgupta:2006:Homeland,
author = "D. Dasgupta",
title = "Computational Intelligence in Cyber Security",
booktitle = "Proceedings of the 2006 IEEE International Conference
on Computational Intelligence for Homeland Security and
Personal Safety",
year = "2006",
pages = "2--3?",
address = "Alexandria, VA, USA",
month = oct,
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-4244-0744-3",
doi = "doi:10.1109/CIHSPS.2006.313289",
abstract = "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",
notes = "Center for Inf. Assurance & Intelligent Security Syst.
Res. Lab., Memphis Univ., TN",
}
@InProceedings{daSilva:2000:ewbss,
author = "Adelino R. Ferreira {da Silva}",
title = "Evolutionary Wavelet Bases in Signal Spaces",
booktitle = "Real-World Applications of Evolutionary Computing",
year = "2000",
editor = "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",
volume = "1803",
series = "LNCS",
pages = "44--53",
address = "Edinburgh",
publisher_address = "Berlin",
month = "17 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-67353-9",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1803&spage=45",
notes = "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",
}
@InProceedings{daSilva:2000:GECCO,
author = "Adelino R. Ferreira {da Silva}",
title = "Genetic Algorithms for Component Analysis",
pages = "243--250",
year = "2000",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2000)",
editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz
and Lee Spector and Ian Parmee and Hans-Georg Beyer",
address = "Las Vegas, Nevada, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "10-12 " # jul,
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-708-0",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/GA050.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/GA050.ps",
notes = "A joint meeting of the ninth International Conference
on Genetic Algorithms (ICGA-2000) and the fifth Annual
Genetic Programming Conference (GP-2000) Part of
\cite{whitley:2000:GECCO}",
}
@InProceedings{Silva:2002:AoECiEPS,
author = "Alexandre P. {Alves da Silva} and Pedro Jose Abrao",
title = "Applications of Evolutionary Computation in Electric
Power Systems",
booktitle = "Proceedings of the 2002 Congress on Evolutionary
Computation CEC2002",
editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
and Garry Greenwood and Hitoshi Iba and Paul Marrow and
Mark Shackleton",
pages = "1057--1062",
year = "2002",
publisher = "IEEE Press",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
organisation = "IEEE Neural Network Council (NNC), Institution of
Electrical Engineers (IEE), Evolutionary Programming
Society (EPS)",
month = "12-17 " # may,
ISBN = "0-7803-7278-6",
keywords = "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",
doi = "doi:10.1109/CEC.2002.1004389",
abstract = "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.",
notes = "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)",
}
@Article{Dassau:Mat:06,
author = "Eyal Dassau and Benyamin Grosman and Daniel R. Lewin",
title = "Modeling and temperature control of rapid thermal
processing",
journal = "Computers and Chemical Engineering",
year = "2006",
volume = "30",
number = "4",
pages = "686--697",
month = "15 " # feb,
keywords = "genetic algorithms, genetic programming, Rapid thermal
processing (RTP), Non-linear model predictive control
(NMPC), GA, GP",
URL = "http://tx.technion.ac.il/~dlewin/publications/rtp_paper_v9.pdf",
doi = "doi:10.1016/j.compchemeng.2005.11.007",
size = "28 pages",
abstract = "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.",
}
@InProceedings{dastani:2001:gecco,
title = "Finding Perceived Pattern Structures using Genetic
Programming",
author = "Mehdi Dastani and Elena Marchiori and Robert Voorn",
pages = "3--10",
year = "2001",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2001)",
editor = "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",
address = "San Francisco, California, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "7-11 " # jul,
keywords = "genetic algorithms, genetic programming, visual
perception, gestalt, simplicity principle, structural
information theory (SIT), perceptual regularity",
ISBN = "1-55860-774-9",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d01.pdf",
URL = "http://people.cs.uu.nl/mehdi/publication/Gecco01.ps",
size = "8 pages",
notes = "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 \cite{spector:2001:GECCO}",
}
@Article{dautenhahn:2002:GPEM,
author = "Kerstin Dautenhahn",
title = "Book Review: {Swarm} Intelligence",
journal = "Genetic Programming and Evolvable Machines",
year = "2002",
volume = "3",
number = "1",
pages = "93--97",
month = mar,
keywords = "genetic algorithms, genetic programming, evolvable
hardware",
ISSN = "1389-2576",
doi = "doi:10.1023/A:1014827205360",
abstract = "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",
notes = "Article ID: 395992",
}
@InProceedings{davenport:1999:RIURPR,
author = "G. F. Davenport and M. D. Ryan and V. J.
Rayward-Smith",
title = "Rule Induction Using a Reverse Polish Representation",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "990--995",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-433.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-433.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{Davidge:1993:rr,
author = "Robert Davidge",
title = "Looping as a Means of Survival: Playing Russian
Roulette in a Harsh Environment",
booktitle = "ECAL-93 Self organisation and life: from simple rules
to global complexity",
year = "1993",
pages = "259--273",
address = "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",
month = "24--26 " # may,
organisation = "Centre for Non-Linear Phenomena and Complex Systems",
email = "robertd@cogs.susx.ac.uk",
keywords = "genetic algorithms",
size = "15 pages",
abstract = "Cline 4bit processor runs across 2dee memory array.
Controlled by 16 chromosome of micro-instruction
sequences of fixed length.",
notes = "There seems to be some doubt as to wether ECAL-93 was
published. This copy from attendee.",
}
@InProceedings{davidson:1999:snr:htpa,
author = "J. W. Davidson and D. A. Savic and G. A. Walters",
title = "Symbolic and numerical regression: a hybrid technique
for polynomial approximators",
booktitle = "Proceedings of Recent Advances in Soft Computing'99",
year = "1999",
editor = "Robert John and Ralph Birkenhead",
pages = "111--116",
address = "De Montfort University, Leicester, UK",
month = "1-2 " # jul,
publisher = "Physica Verlag",
keywords = "genetic algorithms, genetic programming, least
squares, polynomial expressions, symbolic algebra,
symbolic regression",
}
@Article{davidson:1999:miepfftpf,
author = "J. W. Davidson and D. A. Savic and G. A. Walters",
title = "Method for the identification of explicit polynomial
formulae for the friction in turbulent pipe flow",
journal = "Journal of Hydroinformatics",
year = "1999",
volume = "1",
number = "2",
pages = "115--126",
keywords = "genetic algorithms, genetic programming, least
squares, polynomial expressions, symbolic algebra,
symbolic regression",
ISSN = "1464-7141",
URL = "http://www.iwaponline.com/jh/001/0115/0010115.pdf",
size = "12 pages",
abstract = "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.",
notes = "Improving on Ephemeral random constants",
}
@InProceedings{davidson:1999:ac-wfohrm,
author = "J. W. Davidson and D. A. Savic and G. A. Walters",
title = "Approximators for the Colebrook-White Formula Obtained
through a Hybrid Regression Method",
booktitle = "Proceedings of XIII International Conference on
Computational Methods in Water Resources",
year = "2000",
address = "Calgary, Canada",
month = "25-29 " # jun,
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{davidson:2000:rrmunprm,
author = "J. W. Davidson and D. A. Savic and G. A. Walters",
title = "Rainfall Runoff Modeling Using a New Polynomial
Regression Method",
booktitle = "Proceedings of the 4th International Conference on
Hydroinformatics",
year = "2000",
address = "Iowa City, Iowa, USA",
month = "23-27 " # jul,
organisation = "Iowa Institute of Hydraulic Research",
note = "CD-ROM only",
keywords = "genetic algorithms, genetic programming",
ISBN = "none",
notes = "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) ?",
}
@InProceedings{davidson:2000:snrea,
author = "J. W. Davidson and D. A. Savic and G. A. Walters",
title = "Symbolic and numerical regression: experiments and
applications",
booktitle = "Developments in Soft Computing",
year = "2001",
editor = "Robert John and Ralph Birkenhead",
pages = "175--182",
address = "De Montfort University, Leicester, UK",
month = "29-30 " # jun # " 2000.",
publisher = "Physica Verlag",
keywords = "genetic algorithms, genetic programming,
least-squares, rule-based programming, stepwise
regression, symbolic regression",
ISBN = "3-7908-1361-3",
abstract = "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",
}
@Article{davidson:2003:IS,
author = "J. W. Davidson and D. A. Savic and G. A. Walters",
title = "Symbolic and numerical regression: Experiments and
applications",
journal = "Information Sciences",
year = "2003",
volume = "150",
pages = "95--117",
number = "1-2",
doi = "doi:10.1016/S0020-0255(02)00371-7",
URL = "http://www.sciencedirect.com/science/article/B6V0C-474DD2V-1/2/3368220198ea15f93a793594af73d8d1",
keywords = "genetic algorithms, genetic programming, Least
squares, Rule-based programming, Stepwise regression,
Symbolic regression",
abstract = "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.",
}
@Article{David-Tabibi:2010:GPEM,
author = "Omid David-Tabibi and Moshe Koppel and Nathan S.
Netanyahu",
title = "Expert-driven genetic algorithms for simulating
evaluation functions",
journal = "Genetic Programming and Evolvable Machines",
year = "2011",
volume = "12",
number = "1",
pages = "5--22",
month = mar,
keywords = "genetic algorithms, Computer chess, Fitness
evaluation, Games, Parameter tuning",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-010-9103-4",
abstract = "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.",
notes = "Not GP.
A preliminary version of this paper appeared in
Proceedings of the 2008 Genetic and Evolutionary
Computation Conference \cite{David-Tabibi:2008:gecco}
and received the Best Paper Award in the conference's
Real-World Applications track.",
affiliation = "Department of Computer Science, Bar-Ilan University,
52900 Ramat-Gan, Israel",
}
@InCollection{davis:1994:spec,
author = "James Davis",
title = "Single Populations v. Co-Evolution",
booktitle = "Artificial Life at Stanford 1994",
year = "1994",
editor = "John R. Koza",
pages = "20--27",
address = "Stanford, California, 94305-3079 USA",
month = jun,
organisation = "Stanford University",
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-182105-2",
notes = "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",
}
@Article{Davis:Nfs:06,
author = "Richard A. Davis and Adrian J. Charlton and Sarah
Oehlschlager and Julie C. Wilson",
title = "Novel feature selection method for genetic programming
using metabolomic {1H NMR} data",
journal = "Chemometrics and Intelligent Laboratory Systems",
year = "2006",
volume = "81",
number = "1",
pages = "50--59",
month = mar,
keywords = "genetic algorithms, genetic programming, Metabolomics,
Multivariate data analysis, Feature selection, NMR",
doi = "doi:10.1016/j.chemolab.2005.09.006",
abstract = "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.",
}
@Article{day:2002:AEM,
author = "Jennifer P. Day and Douglas B. Kell and Gareth W.
Griffith",
title = "Differentiation of Phytophthora infestans Sporangia
from Other Airborne Biological Particles by Flow
Cytometry",
journal = "Applied and Environmental Microbiology",
year = "2002",
volume = "68",
number = "1",
pages = "37--45",
month = jan,
keywords = "genetic algorithms, genetic programming",
URL = "http://intl-aem.asm.org/cgi/reprint/68/1/37.pdf",
doi = "doi:10.1128/AEM.68.1.37-45.2002",
abstract = "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.",
notes = "GMax-Bio",
}
@Article{Day:2007:ASLP,
title = "Robust Text-Independent Speaker Verification Using
Genetic Programming",
author = "Peter Day and Asoke K. Nandi",
journal = "IEEE Transactions on Audio, Speech and Language
Processing",
year = "2007",
volume = "15",
number = "1",
pages = "285--295",
month = jan,
keywords = "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",
doi = "doi:10.1109/TASL.2006.876765",
ISSN = "1558-7916",
abstract = "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",
notes = "see also IEEE Transactions on Speech and Audio
Processing",
}
@InProceedings{Day:2008:MLSP,
author = "Peter Day and Asoke K. Nandi",
title = "Sunspot prediction using genetic programming augmented
by Binary String Fitness Characterisation and
Comparative Partner Selection",
booktitle = "IEEE Workshop on Machine Learning for Signal
Processing, MLSP 2008",
year = "2008",
month = oct,
pages = "175--180",
keywords = "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",
doi = "doi:10.1109/MLSP.2008.4685475",
ISSN = "1551-2541",
abstract = "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.",
notes = "Also known as \cite{4685475}",
}
@Article{Day:2008:TEC,
title = "Binary String Fitness Characterization and Comparative
Partner Selection in Genetic Programming",
author = "Peter Day and Asoke K. Nandi",
journal = "IEEE Transactions on Evolutionary Computation",
year = "2008",
month = dec,
volume = "12",
number = "6",
pages = "724--735",
keywords = "genetic algorithms, genetic programming, binary string
fitness characterization, comparative partner
selection, evolutionary methods, genetic programming
benchmarking problems, adaptive crossover and mutation,
mate selection, CPS",
ISSN = "1089-778X",
doi = "doi:10.1109/TEVC.2008.917201",
size = "12 pages",
abstract = "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.",
notes = "Also known as \cite{4472181} 3 bit parity, 5-even
parity, 11 mux, quartic, Rastrigin, Sunspot, parsimony
pressure, bloat,",
}
@InCollection{day:2010:Chiong,
author = "Peter Day and Asoke Nandi",
title = "Genetic Programming for Robust Text Independent
Speaker Verification",
booktitle = "Nature-Inspired Informatics for Intelligent
Applications and Knowledge Discovery: Implications in
Business, Science, and Engineering",
publisher = "IGI Global",
year = "2010",
editor = "Raymond Chiong",
pages = "259--280",
keywords = "genetic algorithms, genetic programming",
isbn13 = "1605667056",
URL = "http://www.igi-global.com/Bookstore/Chapter.aspx?TitleId=36319",
doi = "doi:10.4018/978-1-60566-705-8",
abstract = "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.",
notes = "http://www.igi-global.com/Bookstore/TitleDetails.aspx?TitleId=794&DetailsType=Description",
}
@Article{Dayik:2007:JTI,
author = "M. Dayik and M. C. Kayacan and H. Calis and E.
Cakmak",
title = "Control of warp tension during weaving procedure using
evaluation programming",
journal = "The Journal of the Textile Institute",
year = "2006",
volume = "97",
number = "4",
pages = "313--324",
keywords = "genetic algorithms, genetic programming, Gene
Expression Programming, Weaving, warp tension, let-off
control, warp break",
ISSN = "0040-5000",
doi = "doi:10.1533/joti.2005.0132",
abstract = "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.",
notes = "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",
}
@InProceedings{deakin:1996:GPtaw1,
author = "Anthony G. Deakin and Derek F. Yates",
title = "Genetic Programming Tools Available on the Web: {A}
First Encounter",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "420",
address = "Stanford University, CA, USA",
publisher_address = "Cambridge, MA, USA",
publisher = "MIT Press",
URL = "http://www.liv.ac.uk/~anthonyd/gp9632.ps",
size = "1 page",
notes = "GP-96 10 page version at
http://www.csc.liv.ac.uk/~anthony/gp961.ps (broken
2006)",
}
@InProceedings{Deakin:1997:esGP,
author = "Anthony G. Deakin and Derek F. Yates",
title = "Economical Solutions with Genetic Programming: the
Non-Hamstrung Squadcar Problem, Fv{M} and {EHP}",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "71--76",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/Deakin_1997_esGP.pdf",
size = "6 pages",
notes = "GP-97",
}
@InProceedings{deakin:1997:PTN,
author = "Anthony G. Deakin and Derek F. Yates",
title = "Phase Transition Networks: {A} Modelling technique
supporting the Evolution of Autonomous Agents' Tactical
and Operational Activities",
booktitle = "Evolutionary Computing",
year = "1997",
editor = "David Corne and Jonathan L. Shapiro",
volume = "1305",
series = "Lecture Notes in Computer Science",
pages = "263--273",
address = "Manchester, UK",
month = "11-13 " # apr,
organisation = "AISB",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, agents,
MPHaSys",
ISBN = "3-540-63476-2",
doi = "doi:10.1007/BFb0027180",
notes = "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,",
}
@InProceedings{deakin:1998:eoaasGP,
author = "Anthony G. Deakin and Derek F. Yates",
title = "Evolving and Optimizing Autonomous Agents' Strategies
with Genetic Programming",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "42--47",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-548-7",
notes = "GP-98",
}
@InProceedings{conf/icnc/AlmeidaSNY05,
title = "Application of Genetic Programming for Fine Tuning
{PID} Controller Parameters Designed Through
{Ziegler-Nichols} Technique",
author = "Gustavo Maia {de Almeida} and Valceres Vieira Rocha {e
Silva} and Erivelton Geraldo Nepomuceno and Ryuichi
Yokoyama",
year = "2005",
pages = "313--322",
editor = "Lipo Wang and Ke Chen and Yew-Soon Ong",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3612",
booktitle = "Advances in Natural Computation, First International
Conference, ICNC 2005, Proceedings, Part III",
address = "Changsha, China",
month = aug # " 27-29",
bibdate = "2005-08-01",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/icnc/icnc2005-3.html#AlmeidaSNY05",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-28320-X",
doi = "doi:10.1007/11539902_37",
size = "10 pages",
abstract = "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.",
}
@InProceedings{conf/sigir/AlmeidaGCC07,
author = "Humberto Mossri {de Almeida} and Marcos Andre
Goncalves and Marco Cristo and Pavel Calado",
title = "A combined component approach for finding
collection-adapted ranking functions based on genetic
programming",
booktitle = "Proceedings of the 30th Annual International ACM
Conference on Research and Development in Information
Retrieval, SIGIR 2007",
year = "2007",
editor = "Wessel Kraaij and Arjen P. {de Vries} and Charles L.
A. Clarke and Norbert Fuhr and Noriko Kando",
pages = "399--406",
address = "Amsterdam, The Netherlands",
month = jul # " 23-27",
publisher = "ACM",
keywords = "genetic algorithms, genetic programming, Information
Retrieval, Ranking Functions, Term-weighting, Machine
Learning",
isbn13 = "978-1-59593-597-7",
doi = "doi:10.1145/1277741.1277810",
size = "8 pages",
abstract = "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.",
bibdate = "2007-08-24",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/sigir/sigir2007.html#AlmeidaGCC07",
}
@InProceedings{conf/seal/PereiraJV10,
title = "A Niched Genetic Programming Algorithm for
Classification Rules Discovery in Geographic
Databases",
author = "Marconi {de Arruda Pereira} and Clodoveu Augusto
{Davis Junior} and Joao Antonio {de Vasconcelos}",
booktitle = "Simulated Evolution and Learning - 8th International
Conference, {SEAL} 2010, Kanpur, India, December 1-4,
2010. Proceedings",
publisher = "Springer",
year = "2010",
volume = "6457",
editor = "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{\"u}rgen Branke and Sushil J.
Louis and Kay Chen Tan",
isbn13 = "978-3-642-17297-7",
pages = "260--269",
series = "Lecture Notes in Computer Science",
URL = "http://dx.doi.org/10.1007/978-3-642-17298-4",
keywords = "genetic algorithms, genetic programming",
bibdate = "2010-12-01",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/seal/seal2010.html#PereiraJV10",
}
@InProceedings{deaton:1996:gsreDNA,
author = "R. Deaton and M. Garzon and R. C. Murphy and J. A.
Rose and D. R. Franceschetti and S. E. {Stevens, Jr.}",
title = "Genetic Search of Reliable Encodings for {DNA}-Based
Computation",
booktitle = "Late Breaking Papers at the Genetic Programming 1996
Conference Stanford University July 28-31, 1996",
year = "1996",
editor = "John R. Koza",
pages = "9--15",
address = "Stanford University, CA, USA",
publisher_address = "Stanford University, Stanford, California
94305-3079, USA",
month = "28--31 " # jul,
publisher = "Stanford Bookstore",
ISBN = "0-18-201031-7",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.csce.uark.edu/~rdeaton/dna/papers/gp-96.pdf",
size = "7 pages",
abstract = "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.",
notes = "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",
}
@InProceedings{deaton:1997:ithr,
author = "R. Deaton and M. Garzon and R. C. Murphy and D. R.
Franceschetti and J. A. Rose and S. E. {Stevens, Jr.}",
title = "Information Transfer through Hybridization Reactions
in {DNA} based Computing",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "DNA Computing",
pages = "463--471",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
notes = "GP-97",
}
@InProceedings{deaton:1999:RTCDC,
author = "Russell Deaton",
title = "Reaction Temperature Constraints in {DNA} Computing",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1803--1804",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "dna and molecular computing",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/dn-101.pdf",
URL = "http://csce.uark.edu/~rdeaton/dna/papers/dn-101.pdf",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{deb:1998:otsRGA,
author = "Kalyanmoy Deb and Surendra Gulati and Sekhar
Chakrabarti",
title = "Optimal Truss-Structure Design using Real-Coded
Genetic Algorithms",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "479--486",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms",
notes = "SGA-98",
}
@InProceedings{deb:1999:CTPMO,
author = "Kalyanmoy Deb",
title = "Construction of Test Problems for Multi-Objective
Optimization",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "164--171",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{deb:1999:SRGASBC,
author = "Kalyanmoy Deb and Hans-Georg Beyer",
title = "Self-Adaptation in Real-Parameter Genetic Algorithms
with Simulated Binary Crossover",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "172--179",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/deb_gecco1.ps.gz",
URL = "http://ls11-www.informatik.uni-dortmund.de/people/deb/papers/gecco1.ps.gz",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@Proceedings{deb:2004:GECCO1,
title = "Genetic and Evolutionary Computation -- {GECCO}-2004,
Part {I}",
year = "2004",
editor = "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",
series = "Lecture Notes in Computer Science",
address = "Seattle, WA, USA",
publisher_address = "Heidelberg",
month = "26-30 " # jun,
organisation = "ISGEC",
publisher = "Springer-Verlag",
volume = "3102",
ISBN = "3-540-22344-4",
ISSN = "0302-9743",
URL = "http://www.springerlink.com/content/978-3-540-22344-3",
keywords = "genetic algorithms, genetic programming",
organisation = "ISGEC",
size = "1445 pages",
notes = "GECCO-2004 A joint meeting of the thirteenth
international conference on genetic algorithms
(ICGA-2004) and the ninth annual genetic programming
conference (GP-2004)",
}
@Proceedings{deb:2004:GECCO2,
title = "Genetic and Evolutionary Computation -- {GECCO}-2004,
Part {II}",
year = "2004",
editor = "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",
series = "Lecture Notes in Computer Science",
address = "Seattle, WA, USA",
publisher_address = "Heidelberg",
month = "26-30 " # jun,
organisation = "ISGEC",
publisher = "Springer-Verlag",
volume = "3103",
ISBN = "3-540-22343-6",
ISSN = "0302-9743",
URL = "http://www.springerlink.com/content/978-3-540-22343-6",
keywords = "genetic algorithms, genetic programming",
organisation = "ISGEC",
size = "1439 pages",
notes = "GECCO-2004 A joint meeting of the thirteenth
international conference on genetic algorithms
(ICGA-2004) and the ninth annual genetic programming
conference (GP-2004)",
}
@InProceedings{deCarvalho:2006:JCDL,
author = "Moises G. {de Carvalho} and Marcos Andre Goncalves and
Alberto H. F. Laender and Altigran S. {da Silva}",
title = "Learning to deduplicate",
booktitle = "Proceedings of the 6th ACM/IEEE-CS Joint Conference on
Digital Libraries, JCDL '06",
year = "2006",
pages = "41--50",
address = "Chapel Hill, NC, USA",
month = jun,
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming,
Deduplication, Digital Libraries",
ISBN = "1-59593-354-9",
URL = "http://delivery.acm.org/10.1145/1150000/1141760/p41-decarvalho.pdf?key1=1141760&key2=6906456911&coll=GUIDE&dl=GUIDE&CFID=45325455&CFTOKEN=75817203",
doi = "doi:10.1145/1141753.1141760",
size = "10 pages",
abstract = "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",
notes = "Comput. Sci. Dept., Fed. Univ. of Minas Gerais, Belo
Horizonte",
}
@InProceedings{conf/sbbd/CarvalhoLGP08,
title = "The Impact of Parameters Setup on a Genetic
Programming Approach to Record Deduplication",
author = "Moises G. {de Carvalho} and Alberto H. F. Laender and
Marcos Andre Goncalves and Thiago C. Porto",
bibdate = "2009-03-02",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/sbbd/sbbd2008.html#CarvalhoLGP08",
booktitle = "{XXIII} Simp{\'o}sio Brasileiro de Banco de Dados",
publisher = "SBC",
year = "2008",
editor = "Sandra de Amo",
isbn13 = "978-85-7669-205-8",
pages = "91--105",
URL = "http://www.lbd.dcc.ufmg.br:8080/colecoes/sbbd/2008/007.pdf",
address = "Campinas, {S}{\~a}o Paulo, Brasil",
month = "13-15 " # oct,
keywords = "genetic algorithms, genetic programming",
size = "15 pages",
abstract = "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.",
notes = "SDG SBBD 2008.",
}
@Article{deCarvalho:2011:ieeeTKDE,
author = "Moises G. {de Carvalho} and Alberto H. F. Laender and
Marcos Andre Goncalves and Altigran S. {da Silva}",
title = "A Genetic Programming Approach to Record
Deduplication",
journal = "IEEE Transactions on Knowledge and Data Engineering",
year = "2012",
month = mar,
volume = "24",
number = "3",
pages = "399--412",
abstract = "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.",
keywords = "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",
size = "14 pages",
doi = "doi:10.1109/TKDE.2010.234",
ISSN = "1041-4347",
notes = "Also known as \cite{5645623}",
}
@MastersThesis{decaux:2001:masters,
author = "Robert {De Caux}",
title = "Using Genetic Programming to Evolve Strategies for the
Iterated Prisoner's Dilemma",
school = "University College, London",
year = "2001",
month = sep,
keywords = "genetic algorithms, genetic programming, java, gpsys,
ipd, Coevolution, Pareto scoring, strongly typed",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/decaux.masters.zip",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/decaux.masters.pdf",
size = "97 pages",
abstract = "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.",
notes = "Awarded a distinction. Supervised by Robin Hirsch. Zip
archive contains msword document",
}
@InCollection{deconde:2003:EPDMCTD,
author = "Rob P. DeConde",
title = "Evolving Programs for Distributed Multi-Agent
Configuration in Two Dimensions",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2003",
year = "2003",
editor = "John R. Koza",
pages = "38--44",
address = "Stanford, California, 94305-3079 USA",
month = "4 " # dec,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.genetic-programming.org/sp2003/DeConde.pdf",
notes = "part of \cite{koza:2003:gagp}",
}
@InProceedings{eurogp07:Decraene,
author = "James Decraene and George G. Mitchell and Barry
McMullin and Ciaran Kelly",
title = "The Holland Broadcast Language and the Modeling of
Biochemical Networks",
editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and
Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar",
booktitle = "Proceedings of the 10th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "4445",
year = "2007",
address = "Valencia, Spain",
month = "11-13 " # apr,
pages = "361--370",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-540-71602-0",
ISBN = "3-540-71602-5",
doi = "doi:10.1007/978-3-540-71605-1_34",
abstract = "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.",
notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in
conjunction with EvoCOP2007, EvoBIO2007 and
EvoWorkshops2007",
}
@InProceedings{DeFalco:1997:GPekc,
author = "M. Conte and G. Tautteur and I. {De Falco} and A.
Della Cioppa and E. Tarantino",
title = "Genetic Programming Estimates of Kolmogorov
Complexity",
booktitle = "Genetic Algorithms: Proceedings of the Seventh
International Conference",
year = "1997",
editor = "Thomas Back",
pages = "743--750",
address = "Michigan State University, East Lansing, MI, USA",
publisher_address = "San Francisco, CA, USA",
month = "19-23 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-487-1",
broken = "http://www.irsip.na.cnr.it/~hotg/papers/kc.ps",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/1152/http:zSzzSzamalfi.dis.unina.itzSz~deanzSzpaperszSzicga97.pdf/conte97genetic.pdf",
URL = "http://citeseer.ist.psu.edu/355332.html",
size = "7 pages",
abstract = "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.",
notes = "ICGA-97",
}
@InProceedings{falco:1999:TSNM,
author = "I. {De Falco} and A. Iazzetta and E. Tarantino and A.
Della Cioppa and A. Iacuelli",
title = "Towards a Simulation of Natural Mutation",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "156--163",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{DeFalco:2000:GECCO,
author = "I. {De Falco} and A. Iazzetta and E. Tarantino and A.
Della Cioppa and G. Trautteur",
title = "A Kolmogorov Complexity-based Genetic Programming tool
for string compression",
pages = "427--434",
year = "2000",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2000)",
editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz
and Lee Spector and Ian Parmee and Hans-Georg Beyer",
address = "Las Vegas, Nevada, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "10-12 " # jul,
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-708-0",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/GP124.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/GP124.ps",
notes = "A joint meeting of the ninth International Conference
on Genetic Algorithms (ICGA-2000) and the fifth Annual
Genetic Programming Conference (GP-2000) Part of
\cite{whitley:2000:GECCO}",
}
@Article{DeFalco:ASC,
author = "I. {De Falco} and A. {Della Cioppa} and E. Tarantino",
title = "Discovering interesting classification rules with
genetic programming",
journal = "Applied Soft Computing",
year = "2001",
volume = "1",
number = "4",
pages = "257--269",
month = may,
keywords = "genetic algorithms, genetic programming, Data mining,
Classification",
URL = "http://www.sciencedirect.com/science/article/B6W86-44KWJTS-1/1/8f98e1cb13b739a68dad80864389ca51",
URL = "http://www.elsevier.com/gej-ng/10/10/65/45/43/28/article.pdf",
doi = "doi:10.1016/S1568-4946(01)00024-2",
abstract = "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.",
notes = "comparsison in \cite{yu:2004:ECDM}",
}
@InProceedings{falco:2002:usprfmibmoagpa,
author = "Ivanoe {De Falco} and Antonio Della Cioppa and Ernesto
Tarantino",
title = "Unsupervised Spectral Pattern Recognition for
Multispectral Images by means of a Genetic Programming
approach",
booktitle = "Proceedings of the 2002 Congress on Evolutionary
Computation CEC2002",
editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
and Garry Greenwood and Hitoshi Iba and Paul Marrow and
Mark Shackleton",
pages = "231--236",
year = "2002",
month = "12-17 " # may,
publisher = "IEEE Press",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
organisation = "IEEE Neural Network Council (NNC), Institution of
Electrical Engineers (IEE), Evolutionary Programming
Society (EPS)",
ISBN = "0-7803-7278-6",
notes = "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)",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
}
@InProceedings{defalco:2004:wsc9,
author = "I. {De Falco} and A. {Della Cioppa} and F. Fontanella
and E. Tarantino",
title = "An Innovative Approach to Genetic Programming-based
Clustering",
booktitle = "9th Online World Conference on Soft Computing in
Industrial Applications",
year = "2004",
editor = "Ajith Abraham and Mario K{\"o}ppen",
pages = "Paper No. 073",
address = "On the World Wide Web",
month = "20 " # sep # " - 8 " # oct,
organisation = "World Federation on Soft Computing (WFSC)",
keywords = "genetic algorithms, genetic programming, clustering",
abstract = "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",
notes = "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.",
}
@InProceedings{conf/sac/FalcoTCG05a,
title = "A novel grammar-based genetic programming approach to
clustering",
author = "Ivan {De Falco} and Ernesto Tarantino and Antonio
{Della Cioppa} and F. Gagliardi",
year = "2005",
bibdate = "2006-02-10",
pages = "928--932",
editor = "Hisham Haddad and Lorie M. Liebrock and Andrea Omicini
and Roger L. Wainwright",
booktitle = "Proceedings of the 2005 ACM Symposium on Applied
Computing (SAC)",
publisher = "ACM",
address = "Santa Fe, New Mexico, USA",
month = mar # " 13-17",
organisation = "ACM",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/sac/sac2005.html#FalcoTCG05a",
keywords = "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",
ISBN = "1-58113-964-0",
doi = "doi:10.1145/1066677.1066891",
abstract = "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.",
}
@InProceedings{conf/sac/FalcoTCP05,
title = "Inductive inference of chaotic series by Genetic
Programming: a Solomonoff-based approach",
author = "Ivan {De Falco} and Ernesto Tarantino and Antonio
{Della Cioppa} and A. Passaro",
year = "2005",
pages = "957--958",
editor = "Hisham Haddad and Lorie M. Liebrock and Andrea Omicini
and Roger L. Wainwright",
booktitle = "Proceedings of the 2005 ACM Symposium on Applied
Computing (SAC)",
publisher = "ACM",
address = "Santa Fe, New Mexico, USA",
month = mar # " 13-17",
organisation = "ACM",
bibdate = "2006-02-10",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/sac/sac2005.html#FalcoTCP05",
keywords = "genetic algorithms, genetic programming, Automatic
Programming, Algorithms, Experimentation, Inductive
inference, Chaotic series",
ISBN = "1-58113-964-0",
doi = "doi:10.1145/1066677.1066897",
size = "2 pages",
abstract = "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.",
}
@InProceedings{conf/wilf/FalcoCPT05,
title = "Genetic Programming for Inductive Inference of Chaotic
Series",
author = "Ivan {De Falco} and Antonio {Della Cioppa} and A.
Passaro and Ernesto Tarantino",
year = "2005",
pages = "156--163",
editor = "Isabelle Bloch and Alfredo Petrosino and Andrea
Tettamanzi",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3849",
booktitle = "Fuzzy Logic and Applications, 6th International
Workshop, WILF 2005, Revised Selected Papers",
address = "Crema, Italy",
month = sep # " 15-17",
bibdate = "2006-02-22",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/wilf/wilf2005.html#FalcoCPT05",
keywords = "genetic algorithms, genetic programming, Solomonoff
complexity, chaotic series",
ISBN = "3-540-32529-8",
doi = "doi:10.1007/11676935_19",
size = "8 pages",
abstract = "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.",
}
@InProceedings{eurogp06:DeFalcoDellaCioppaMaistoTarantino,
author = "Ivanoe {De Falco} and Antonio {Della Cioppa} and
Domenico Maisto and Ernesto Tarantino",
title = "A Genetic Programming Approach to {Solomonoff's}
Probabilistic Induction",
editor = "Pierre Collet and Marco Tomassini and Marc Ebner and
Steven Gustafson and Anik\'o Ek\'art",
booktitle = "Proceedings of the 9th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3905",
year = "2006",
address = "Budapest, Hungary",
month = "10 - 12 " # apr,
organisation = "EvoNet",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-33143-3",
pages = "24--35",
URL = "http://link.springer.de/link/service/series/0558/papers/3905/39050024.pdf",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in
conjunction with EvoCOP2006 and EvoWorkshops2006",
}
@InProceedings{eurogp07:DeFalco,
author = "Ivanoe {De Falco} and Antonio {Della Cioppa} and
Domenico Maisto and Umberto Scafuri and Ernesto
Tarantino",
title = "Parsimony doesn't mean Simplicity: Genetic Programming
for Inductive Inference on Noisy Data",
editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and
Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar",
booktitle = "Proceedings of the 10th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "4445",
year = "2007",
address = "Valencia, Spain",
month = "11-13 " # apr,
pages = "351--360",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-540-71602-0",
ISBN = "3-540-71602-5",
doi = "doi:10.1007/978-3-540-71605-1_33",
abstract = "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.",
notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in
conjunction with EvoCOP2007, EvoBIO2007 and
EvoWorkshops2007",
}
@InProceedings{platel83,
author = "Michael {Defoin Platel} and Manuel Clergue and
Philippe Collard",
title = "Maximum Homologous Crossover for Linear Genetic
Programming",
booktitle = "Genetic Programming, Proceedings of EuroGP'2003",
year = "2003",
editor = "Conor Ryan and Terence Soule and Maarten Keijzer and
Edward Tsang and Riccardo Poli and Ernesto Costa",
volume = "2610",
series = "LNCS",
pages = "194--203",
address = "Essex",
publisher_address = "Berlin",
month = "14-16 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-00971-X",
URL = "http://www.i3s.unice.fr/~defoin/publications/eurogp_03.pdf",
size = "11 pages",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=194",
abstract = "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.",
notes = "EuroGP'2003 held in conjunction with EvoWorkshops
2003",
}
@InProceedings{defoin-platel:2003:EA,
author = "Michael {Defoin Platel} and Sebastien Verel and Manuel
Clergue and Philippe Collard",
title = "From Royal Road to Epistatic Road for Variable Length
Evolution Algorithm",
booktitle = "Evolution Artificielle, 6th International Conference",
year = "2003",
editor = "Pierre Liardet and Pierre Collet and Cyril Fonlupt and
Evelyne Lutton and Marc Schoenauer",
volume = "2936",
series = "Lecture Notes in Computer Science",
pages = "3--14",
address = "Marseilles, France",
month = "27-30 " # oct,
publisher = "Springer",
note = "Revised Selected Papers",
keywords = "genetic algorithms, genetic programming, Artificial
Evolution, String Edit Distance, Levenshtein distance",
ISBN = "3-540-21523-9",
URL = "http://www.i3s.unice.fr/~defoin/publications/ea_03.pdf",
URL = "http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=2936&spage=3",
doi = "doi:10.1007/b96080",
size = "12 pages",
abstract = "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.",
bibsource = "DBLP, http://dblp.uni-trier.de",
notes = "EA'03",
}
@InProceedings{defoin-platel:2003:hgscigp,
author = "Michael {Defoin Platel} and Manuel Clergue and
Philippe Collard",
title = "Homology gives size control in genetic programming",
booktitle = "Proceedings of the 2003 Congress on Evolutionary
Computation CEC2003",
editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass
and Kay Chen Tan and Bob McKay and Daryl Essam and Tom
Gedeon",
pages = "281--288",
year = "2003",
publisher = "IEEE Press",
address = "Canberra",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "8-12 " # dec,
organisation = "IEEE Neural Network Council (NNC), Engineers Australia
(IEAust), Evolutionary Programming Society (EPS),
Institution of Electrical Engineers (IEE)",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-7804-0",
URL = "http://www.i3s.unice.fr/~defoin/publications/cec_03.pdf",
size = "8 pages",
abstract = "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.",
notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust,
the EPS, and the IEE.",
}
@InProceedings{eurogp:Defoin-PlatelCCC05,
author = "Michael Defoin-Platel and Malik Chami and Manuel
Clergue and Philippe Collard",
editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre
Collet and Jano I. {van Hemert} and Marco Tomassini",
title = "Teams of Genetic Predictors for Inverse Problem
Solving",
booktitle = "Proceedings of the 8th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3447",
year = "2005",
address = "Lausanne, Switzerland",
month = "30 " # mar # " - 1 " # apr,
organisation = "EvoNet",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-25436-6",
pages = "341--350",
size = "10",
URL = "http://www.obs-vlfr.fr/LOV/OMT/fichiers_PDF/Defoin_and_Chami_LNCS_05.pdf",
URL = "http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=341",
doi = "doi:10.1007/b107383",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in
conjunction with EvoCOP2005 and EvoWorkshops2005",
}
@InProceedings{DBLP:conf/ae/Defoin-PlatelCC05,
author = "Michael Defoin-Platel and Manuel Clergue and Philippe
Collard",
title = "Size Control with Maximum Homologous Crossover",
year = "2005",
pages = "13--24",
editor = "El-Ghazali Talbi and Pierre Liardet and Pierre Collet
and Evelyne Lutton and Marc Schoenauer",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3871",
ISBN = "3-540-33589-7",
bibsource = "DBLP, http://dblp.uni-trier.de",
booktitle = "7th International Conference on Artificial Evolution
EA 2005",
address = "Lille, France",
month = oct # " 26-28",
note = "Revised Selected Papers",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1007/11740698_2",
notes = "published 2006",
}
@InProceedings{Defoin-Platel:2006:HIS,
author = "M. D. Platel and M. Clergue",
title = "Monitoring Genetic Variations in Variable Length
Evolutionary Algorithms",
booktitle = "Sixth International Conference on Hybrid Intelligent
Systems, HIS '06",
year = "2006",
pages = "4--4?",
address = "Rio de Janeiro, Brazil",
month = dec,
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming, bloat",
ISBN = "0-7695-2662-4",
doi = "doi:10.1109/HIS.2006.264887",
abstract = "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.",
notes = "Laboratoire d'Oceanographie de Villefranche (LOV),
France;",
}
@InProceedings{eurogp07:defoin,
author = "Michael {Defoin Platel} and S\'ebastien Verel and
Manuel Clergue and Malik Chami",
title = "Density estimation with Genetic Programming for
Inverse Problem solving",
editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and
Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar",
booktitle = "Proceedings of the 10th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "4445",
year = "2007",
address = "Valencia, Spain",
month = "11-13 " # apr,
pages = "45--54",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-71602-5",
isbn13 = "978-3-540-71602-0",
doi = "doi:10.1007/978-3-540-71605-1_5",
abstract = "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.",
notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in
conjunction with EvoCOP2007, EvoBIO2007 and
EvoWorkshops2007",
}
@InProceedings{deFreitas:2010:cec,
author = "Junio {de Freitas} and Gisele L. Pappa and Altigran S.
{da Silva} and Marcos A. Goncalves and Edleno Moura and
Adriano Veloso and Alberto H. F. Laender and Moises G.
{de Carvalho}",
title = "Active Learning Genetic programming for record
deduplication",
booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-6910-9",
URL = "http://www.dcc.ufmg.br/~adrianov/papers/CEC10/cec10.pdf",
abstract = "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.",
doi = "doi:10.1109/CEC.2010.5586104",
notes = "WCCI 2010. Also known as \cite{5586104}",
}
@InProceedings{oai:CiteSeerPSU:512552,
author = "Hugo {de Garis}",
title = "Artificial Embryology",
booktitle = "Artificial Life {III}",
year = "1992",
address = "Santa Fe",
month = jun,
keywords = "genetic algorithms, cellular automata",
citeseer-isreferencedby = "oai:CiteSeerPSU:81581",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:512552",
rights = "unrestricted",
URL = "http://www.iss.whu.edu.cn/degaris/papers/ALife92.pdf",
URL = "http://citeseer.ist.psu.edu/512552.html",
size = "20 pages",
abstract = "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{"}.",
notes = "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.",
}
@InProceedings{deGaris:1992:dcGPssg,
author = "Hugo {de Garis} and Hitoshi Iba and Tatsumi Furuya",
title = "Differentiable Chromosomes: The Genetic Programming of
switchable Shape-Genes",
booktitle = "Parallel Problem Solving from Nature 2",
month = "28-30 " # sep,
year = "1992",
editor = "R Manner and B Manderick",
pages = "489--498",
address = "Brussels, Belgium",
publisher = "Elsevier Science",
keywords = "genetic algorithms, genetic programming",
size = "10 pages",
URL = "http://www.iss.whu.edu.cn/degaris/papers/PPSN92.pdf",
notes = "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",
}
@InProceedings{degaris:1993:erGPsrca,
author = "Hugo {de Garis}",
title = "Evolving a Replicator The Genetic Programming of Self
Reproduction in Cellular Automata",
booktitle = "ECAL-93 Self organisation and life: from simple rules
to global complexity",
year = "1993",
pages = "274--284",
address = "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",
month = "24--26 " # may,
organisation = "Centre for Non-Linear Phenomena and Complex Systems",
email = "degaris@hip.att.co.jp",
keywords = "genetic algorithms, genetic programming,
nonotechnology, nanots, artificial life,
Qantum-electronic computers, Darwin machines",
URL = "http://www.iss.whu.edu.cn/degaris/papers/ECAL93.pdf",
URL = "http://citeseer.ist.psu.edu/521663.html",
size = "11 pages",
abstract = "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)",
notes = "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.",
}
@InProceedings{deGaris:1994:CAM-BRAIN,
author = "Hugo {de Garis}",
title = "{CAM}-{BRAIN} The Genetic Programming of an Artificial
Brain Which Grows/Evolves at Electronic Speeds in a
Cellular Automata Machine",
booktitle = "Proceedings of the 1994 IEEE World Congress on
Computational Intelligence",
year = "1994",
volume = "1",
pages = "337--339b",
address = "Orlando, Florida, USA",
month = "27-29 " # jun,
publisher = "IEEE Press",
keywords = "genetic algorithms, cellular automata, neural
networks",
size = "6 pages",
doi = "doi:10.1109/ICEC.1994.349929",
abstract = "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",
notes = "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.",
}
@Misc{degaris:1996:alifeV,
author = "Hugo {de Garis}",
title = "Alife-{V} 1996 Conference Report",
year = "1996",
month = jul,
keywords = "genetic algorithms, genetic programming, artificial
life",
URL = "http://www.hip.atr.co.jp/~degaris/AlifeV.txt broken",
size = "7 pages",
abstract = "Personal account of the 5th World Artificial Life
Conference, 16-18 May 1996, Nara, Japan",
}
@InProceedings{garis:1999:AABPASWIMENNMCDDAI,
author = "Hugo {de Garis} and Andrzej Buller and Michael Korkin
and Felix Gers and Norberto Eija Nawa and Michael
Hough",
title = "{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}",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1233",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, poster
papers",
ISBN = "1-55860-611-4",
notes = "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",
}
@InProceedings{degaris:2002:gecco:lbp,
title = "A Reversible Evolvable Network Architecture and
Methodology to Overcome the Heat Generation Problem in
Molecular Scale Brain Building",
author = "Hugo {de Garis} and Jonathan Dinerstein and
Ravichandra Sriram",
booktitle = "Late Breaking Papers at the Genetic and Evolutionary
Computation Conference ({GECCO-2002})",
editor = "Erick Cant{\'u}-Paz",
year = "2002",
month = jul,
pages = "83--90",
address = "New York, NY",
publisher = "AAAI",
publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.iss.whu.edu.cn/degaris/papers/RENN.pdf",
abstract = "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.",
notes = "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",
}
@Article{degaris:2005:EC,
author = "Hugo {de Garis}",
title = "Evolvable Hardware 2005",
journal = "Evolutionary Computation",
year = "2005",
volume = "13",
number = "4",
month = "Winter",
pages = "545--550",
keywords = "genetic algorithms, genetic programming, EHW",
doi = "doi:10.1162/106365605774666840",
notes = "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 \cite{koza:2005:EH}, Simon Harding
\cite{harding:2005:EH}, and Tim Gordon
\cite{gordon:2005:EH}.",
}
@InProceedings{deHaas:2009:ismir,
author = "W. Bas {de Haas} and and Martin Rohrmeier and Remco C.
Veltkamp and Frans Wiering",
title = "Modeling Harmonic Similarity Using a Generative
Grammar of Tonal Harmony",
booktitle = "10th International Society for Music Information
Retrieval Conference",
year = "2009",
editor = "Keiji Hirata and George Tzanetakis",
pages = "549--554",
address = "Kobe, Japan",
month = "26-30 " # oct,
URL = "http://ismir2009.ismir.net/proceedings/OS7-2.pdf",
size = "6 pages",
abstract = "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.",
}
@InProceedings{Deias:2009:LAPC,
author = "L. Deias and G. Mazzarella and N. Sirena",
title = "Bandwidth optimization of {EBG} surfaces using genetic
programming",
booktitle = "Loughborough Antennas Propagation Conference, LAPC
2009",
year = "2009",
month = "16-17 " # nov,
address = "Loughborough, UK",
pages = "593--596",
keywords = "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",
doi = "doi:10.1109/LAPC.2009.5352381",
abstract = "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.",
notes = "Also known as \cite{5352381}",
}
@InProceedings{Deias:2010:APSURSI,
author = "L. Deias and G. Mazzarella and N. Sirena",
title = "{EBG} substrate synthesis for {2.45 GHz} applications
using Genetic Programming",
booktitle = "Antennas and Propagation Society International
Symposium (APSURSI), 2010 IEEE",
year = "2010",
month = "11-17 " # jul,
abstract = "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).",
keywords = "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",
doi = "doi:10.1109/APS.2010.5562232",
ISSN = "1522-3965",
notes = "ECJ Dept. of Electr. & Electron. Eng., Univ. of
Cagliari, Cagliari, Italy. Also known as
\cite{5562232}",
}
@InProceedings{eddejong:1999:gssc,
author = "Edwin D. {de Jong} and Luc Steels",
title = "Generation and Selection of Sensory Channels",
booktitle = "Evolutionary Image Analysis, Signal Processing and
Telecommunications: First European Workshop, EvoIASP'99
and EuroEcTel'99",
year = "1999",
editor = "Riccardo Poli and Hans-Michael Voigt and Stefano
Cagnoni and Dave Corne and George D. Smith and Terence
C. Fogarty",
volume = "1596",
series = "LNCS",
pages = "90--100",
address = "Goteborg, Sweden",
publisher_address = "Berlin",
month = "28-29 " # may,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-65837-8",
URL = "http://arti.vub.ac.be/~edwin/publications/channels.ps.gz",
doi = "doi:10.1007/10704703_7",
size = "11 pages",
abstract = "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.",
notes = "EvoIASP99'99",
}
@InProceedings{jong:2001:gecco,
title = "Reducing Bloat and Promoting Diversity using
Multi-Objective Methods",
author = "Edwin D. {de Jong} and Richard A. Watson and Jordan B.
Pollack",
pages = "11--18",
year = "2001",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2001)",
editor = "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",
address = "San Francisco, California, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "7-11 " # jul,
keywords = "genetic algorithms, genetic programming, code growth,
bloat, introns, diversity maintenance, evolutionary
multi-objective optimization, Pareto, optimality",
ISBN = "1-55860-774-9",
URL = "http://www.demo.cs.brandeis.edu/papers/rbpd_gecco01.pdf",
URL = "http://www.demo.cs.brandeis.edu/papers/rbpd_gecco01.ps.gz",
URL = "http://www.demo.cs.brandeis.edu/papers/long.html#rbpd_gecco01",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d01.pdf",
URL = "http://citeseer.ist.psu.edu/440305.html",
abstract = "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.",
notes = "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 \cite{spector:2001:GECCO}",
}
@Article{dejong:2003:GPEM,
author = "Edwin D. {de Jong} and Jordan B. Pollack",
title = "Multi-Objective Methods for Tree Size Control",
journal = "Genetic Programming and Evolvable Machines",
year = "2003",
volume = "4",
number = "3",
pages = "211--233",
month = sep,
keywords = "genetic algorithms, genetic programming, variable size
representations, bloat, code growth, multi-objective
optimization, Pareto optimality, interpretability",
ISSN = "1389-2576",
URL = "http://www.cs.uu.nl/~dejong/publications/bloat.ps",
URL = "http://www.cs.uu.nl/~dejong/index.html#bloatgpem",
doi = "doi:10.1023/A:1025122906870",
abstract = "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.",
notes = "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!",
}
@InProceedings{icga87:deJong,
author = "Kenneth {De Jong}",
title = "On Using Genetic Algorithms to Search Program Spaces",
booktitle = "Genetic Algorithms and their Applications: Proceedings
of the second international conference on Genetic
Algorithms",
year = "1987",
editor = "John J. Grefenstette",
pages = "210--216",
month = "28-31 " # jul,
organisation = "AAAI",
address = "MIT, Cambridge, MA, USA",
publisher_address = "Hillsdale, NJ, USA",
publisher = "Lawrence Erlbaum Associates",
keywords = "genetic algorithms, genetic programming",
size = "7 pages",
ISBN = "0-8058-0158-8",
notes = "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",
}
@InProceedings{DBLP:conf/iwinac/CruzPA05,
author = "Marina {de la Cruz Echeandia} and Alfonso {Ortega de
la Puente} and Manuel Alfonseca",
title = "Attribute Grammar Evolution",
booktitle = "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",
year = "2005",
editor = "Jos{\'e} Mira and Jos{\'e} R. {\'A}lvarez",
series = "Lecture Notes in Computer Science",
volume = "3562",
pages = "182--191",
address = "Las Palmas, Canary Islands, Spain",
month = jun # " 15-18",
publisher = "Springer",
bibsource = "DBLP, http://dblp.uni-trier.de",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-26319-5",
doi = "doi:10.1007/11499305_19",
size = "10 pages",
abstract = "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.",
notes = "cited by \cite{Ortega:2007:ieeeTEC}",
}
@InProceedings{delaCruzEcheandia:2010:ICEC,
author = "Marina {de la Cruz Echeandia} and Alba Martin Lazaro
and Alfonso Ortega {de la Puente}",
title = "The role of Keeping Semantic Blocks Invariant -
Effects in Linear Genetic Programming Performance",
booktitle = "Proceedings of the International Conference on
Evolutionary Computation (ICEC 2010)",
year = "2010",
editor = "Agostinho Rosa",
pages = "Paper Nr: 78",
address = "Valencia, Spain",
month = "24-26 " # oct,
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "http://www.icec.ijcci.org/ICEC2010/home.asp
http://www.ecta.ijcci.org/Abstracts/2010/ICEC_2010_Abstracts.htm",
}
@InProceedings{DelCarpio:2006:CEC,
author = "Carlos A. {Del Carpio M.} and Mohamed Ismael and
Eichiro Ichiishi and Michihisa Koyama and Momoji Kubo
and Akira Miyamoto",
title = "An Evolving Automaton for {RNA} Secondary Structure
Prediction",
booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary
Computation",
year = "2006",
editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and
Simon M. Lucas",
pages = "4533--4540",
address = "Vancouver",
month = "6-21 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-9487-9",
size = "8 pages",
abstract = "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.",
notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
the IEE.
IEEE Catalog Number: 06TH8846D",
}
@Article{DelDuce:2009:ieeeJSTQE,
author = "Andrea {Del Duce} and Polina Bayvel",
title = "Quantum Logic Circuits and Optical Signal Generation
for a Three-Qubit, Optically Controlled, Solid-State
Quantum Computer",
journal = "IEEE Journal of Selected Topics in Quantum
Electronics",
year = "2009",
month = nov # "-" # dec,
volume = "15",
number = "6",
pages = "1694--1703",
keywords = "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",
doi = "doi:10.1109/JSTQE.2009.2024326",
ISSN = "1077-260X",
abstract = "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.",
notes = "Also known as \cite{5290118} See also
\cite{oai:arXiv.org:0910.1673}
http://arxiv.org/abs/0910.1673",
}
@PhdThesis{DelDuce:thesis,
author = "Andrea {Del Duce}",
title = "Quantum Logic circuits for solid-state quantum
information processing",
school = "University College London",
year = "2009",
address = "UK",
month = oct,
keywords = "genetic algorithms, genetic programming",
URL = "http://discovery.ucl.ac.uk/20166/1/20166.pdf",
bibsource = "OAI-PMH server at eprints.ucl.ac.uk",
language = "eng",
oai = "oai:eprints.ucl.ac.uk.OAI2:20166",
URL = "http://eprints.ucl.ac.uk/20166/",
size = "175 pages",
abstract = "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.",
}
@InProceedings{delgado:1999:MHEDFS,
author = "Myriam Delgado and Fernando Von Zuben and Fernando
Gomide",
title = "Modular and Hierarchial Evolutionary Design of Fuzzy
Systems",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "180--187",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
URL = "http://www.dca.fee.unicamp.br/~myriam/papers/gecco99.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-850.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-850.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{delgado:2002:FUZZIEEE,
author = "Myriam Regattieri Delgado and Fernando {Von Zuben} and
Fernando Gomide",
title = "Multi-Objective Decision Making: Towards Improvement
of Accuracy, Interpretability and Design Autonomy in
Hierarchical Genetic Fuzzy Systems",
booktitle = "Proceedings of the 2002 IEEE International Conference
on Fuzzy Systems, FUZZ-IEEE-02",
pages = "1222--1227",
year = "2002",
month = "12-17 " # may,
address = "Hilton Hawaiian Village Hotel, Honolulu, Hawaii",
publisher = "IEEE Press",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
organisation = "IEEE",
ISBN = "0-7803-7280-8",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "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.",
}
@PhdThesis{delgado:2002:thesis,
author = "Myriam Regattieri De Biase da Silva Delgado",
title = "Projeto Automatico de Sistemas Nebulosos: Uma
Abordagem Co-Evolutiva",
school = "FACULDADE DE ENGENHARIA ELETRICA E DE COMPUTACAO,
UNIVERSIDADE ESTADUAL DE CAMPINAS",
year = "2002",
month = "26 " # feb,
keywords = "genetic algorithms, fuzzy systems",
URL = "http://www.dca.fee.unicamp.br/~myriam/phdthesis.pdf",
URL = "http://www.dca.fee.unicamp.br/~vonzuben/research/myriam_dout.html",
size = "204 pages",
abstract = "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.",
notes = "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",
}
@InProceedings{deLima:2010:cec,
author = "Elisa Boari {de Lima} and Gisele L. Pappa and Jussara
Marques {de Almeida} and Marcos A. Goncalves and Wagner
Meira",
title = "Tuning Genetic Programming parameters with factorial
designs",
booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-6910-9",
abstract = "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.",
doi = "doi:10.1109/CEC.2010.5586084",
notes = "WCCI 2010. Also known as \cite{5586084}",
}
@Article{delisle:2004:CIM,
author = "Robert Kirk DeLisle and Steven L. Dixon",
title = "Induction of Decision Trees via Evolutionary
Programming",
journal = "Journal of Chemical Information and Modeling",
year = "2004",
volume = "44",
number = "3",
pages = "862--870",
keywords = "genetic algorithms, genetic programming, EP, EPTree",
doi = "doi:10.1021/ci034188s",
abstract = "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.",
notes = "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",
}
@InProceedings{del-Rosal:2011:IWINAC,
author = "Emilio {del Rosal} and Marina {de la Cruz} and Alfonso
{Ortega de la Puente}",
title = "Towards the Automatic Programming of {NEP}s",
booktitle = "Proceedings of the 4th International Work-Conference
on the Interplay Between Natural and Artificial
Computation, IWINAC 2011, Part I",
year = "2011",
editor = "Jose Manuel Ferrandez and Jose Ramon {Alvarez Sanchez}
and Felix {de la Paz} and F. Javier Toledo",
series = "Lecture Notes in Computer Science",
pages = "303--312",
volume = "6686",
address = "La Palma, Canary Islands, Spain",
month = may # " 30-" # jun # " 3",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
isbn13 = "978-3-642-21343-4",
doi = "doi:10.1007/978-3-642-21344-1_32",
size = "10 pages",
abstract = "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.",
notes = "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 \cite{DBLP:conf/iwinac/CruzPA05}",
affiliation = "Departamento de Ingenieria Informatica, Escuela
Politecnica Superior, Universidad Autonoma de Madrid,
Spain",
}
@InProceedings{DeMaagd:2010:HICSS,
author = "Kurt DeMaagd and Johannes Bauer",
title = "A Genetic Programming Approach to Network Management
Regulation",
booktitle = "43rd Hawaii International Conference on System
Sciences (HICSS 2010)",
year = "2010",
month = "5-8 " # jan,
abstract = "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.",
keywords = "genetic algorithms, genetic programming, United
States, business incentives, discriminatory prices,
economic growth, network management regulation,
commerce, telecommunication industry, telecommunication
network management, telecommunication services",
doi = "doi:10.1109/HICSS.2010.14",
ISSN = "1530-1605",
notes = "Also known as \cite{5428681}",
}
@InCollection{dembo:2002:EMSGA,
author = "Adar Dembo",
title = "Evolving Musical Scores using the Genetic Algorithm",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2002",
year = "2002",
editor = "John R. Koza",
pages = "65--72",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms",
URL = "http://www.genetic-programming.org/sp2002/Dembo.pdf",
notes = "part of \cite{koza:2002:gagp}",
}
@Article{deMenezes:Fwg:06,
author = "Lilian M. {de Menezes} and Nikolay Y. Nikolaev",
title = "Forecasting with genetically programmed polynomial
neural networks",
journal = "International Journal of Forecasting",
year = "2006",
volume = "22",
number = "2",
pages = "249--265",
month = apr # "-" # jun,
keywords = "genetic algorithms, genetic programming, Nonlinear
models, Tree-structured polynomial neural network
models, Statistical learning algorithms",
doi = "doi:10.1016/j.ijforecast.2005.05.002",
abstract = "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.",
}
@InProceedings{dempsey:2004:gew:idem,
author = "Ian Dempsey and Michael O'Neill and Anthony Brabazon",
title = "Live Trading with Grammatical Evolution",
editor = "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",
booktitle = "GECCO 2004 Workshop Proceedings",
year = "2004",
month = "26-30 " # jun,
address = "Seattle, Washington, USA",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/WGEW001.pdf",
notes = "GECCO-2004WKS Distributed on CD-ROM at GECCO-2004",
}
@InProceedings{1068289,
author = "Ian Dempsey and Michael O'Neill and Anthony Brabazon",
title = "Meta-grammar constant creation with grammatical
evolution by grammatical evolution",
booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on
Genetic and evolutionary computation",
year = "2005",
editor = "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",
volume = "2",
ISBN = "1-59593-010-8",
pages = "1665--1671",
address = "Washington DC, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1665.pdf",
doi = "doi:10.1145/1068009.1068289",
publisher = "ACM Press",
publisher_address = "New York, NY, 10286-1405, USA",
month = "25-29 " # jun,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, constant
creation, digit concatenation, ephemeral random
constants, grammatical evolution, metagrammars,
theory",
notes = "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",
}
@InProceedings{dempsey:gecco05ws,
author = "Ian Dempsey",
title = "Constant Generation for the Financial Domain using
Grammatical Evolution",
booktitle = "Genetic and Evolutionary Computation Conference
{(GECCO2005)} workshop program",
year = "2005",
month = "25-29 " # jun,
editor = "Franz Rothlauf and Misty Blowers and J{\"u}rgen Branke
and Stefano Cagnoni and Ivan I. Garibay and Ozlem
Garibay and J{\"o}rn 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",
publisher = "ACM Press",
address = "Washington, D.C., USA",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
pages = "350--353",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0350.pdf",
abstract = "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.",
notes = "Distributed on CD-ROM at GECCO-2005. ACM
1-59593-097-3/05/0006",
}
@InProceedings{dempsey:2006:CEC,
author = "Ian Dempsey and Michael O'Neill and Anthony Brabazon",
title = "Adaptive Trading with Grammatical Evolution",
booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary
Computation",
year = "2006",
pages = "9137--9142",
address = "Vancouver",
month = "6-21 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
ISBN = "0-7803-9487-9",
size = "6 pages",
abstract = "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.",
notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
the IEE.",
}
@Article{Dempsey:2007:IJICA,
author = "Ian Dempsey and Michael O'Neill and Anthony Brabazon",
title = "Constant Creation in Grammatical Evolution",
journal = "International Journal of Innovative Computing and
Applications",
year = "2007",
volume = "1",
number = "1",
pages = "23--38",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, constant creation, digit concatenation,
ephemeral random constants, grammar based genetic
programming, persistent random constants",
URL = "http://www.inderscience.com/search/index.php?action=record&rec_id=13399&prevQuery=&ps=10&m=or",
doi = "doi:10.1504/IJICA.2007.013399",
abstract = "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.",
}
@InCollection{Dempsey:2007:geRBF,
author = "Ian Dempsey and Anthony Brabazon and Michael O'Neill",
title = "A Grammatical Genetic Programming Representation for
Radial Basis Function Networks",
booktitle = "Engineering Evolutionary Intelligent Systems",
publisher = "Springer",
year = "2007",
editor = "Ajith Abraham and Crina Grosan and Witold Pedrycz",
volume = "82",
series = "Studies in Computational Intelligence",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
isbn13 = "978-3-540-75395-7",
notes = "http://www.springer.com/east/home/engineering?SGWID=5-175-22-173762620-0",
}
@PhdThesis{Dempsey:thesis,
author = "Ian Dempsey",
title = "Grammatical Evolution in Dynamic Environments",
school = "University College Dublin",
year = "2007",
address = "Ireland",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, dynamic environments",
abstract = "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.",
}
@Book{Dempsey:book,
author = "Ian Dempsey and Michael O'Neill and Anthony Brabazon",
title = "Foundations in Grammatical Evolution for Dynamic
Environments",
publisher = "Springer",
year = "2009",
volume = "194",
series = "Studies in Computational Intelligence",
month = apr,
isbn13 = "978-3-642-00313-4",
URL = "http://www.springer.com/engineering/book/978-3-642-00313-4",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
abstract = "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.",
size = "approx 190 pages",
}
@TechReport{Dempster:2000:wp35,
author = "M. A. H. Dempster and C. M. Jones",
title = "The Profitability of Intra-Day {FX} Trading Using
Technical Indicators",
institution = "Judge Institute of Management Studies, University of
Cambridge",
year = "2000",
type = "Working Paper",
number = "35/00",
address = "Trumpington Street, Cambridge, CB2 1AG",
keywords = "genetic algorithms, genetic programming",
URL = "http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/1999/profitability.pdf",
abstract = "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.",
size = "70 pages",
}
@Article{Dempster:2000:QF,
author = "M. A. H. Dempster and C. M. Jones",
title = "A real-time adaptive trading system using genetic
programming",
journal = "Quantitative Finance",
year = "2000",
volume = "1",
pages = "397--413",
keywords = "genetic algorithms, genetic programming",
URL = "http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/2000/geneticprogramming.pdf",
URL = "http://citeseer.ist.psu.edu/dempster01realtime.html",
size = "17 pages",
abstract = "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.",
notes = "INSTITUTE OF PHYSICS PUBLISHING quant.iop.org",
}
@Article{Dempster:2001:trading,
author = "M. A. H. Dempster and Tom W. Payne and Yazann Romahi
and G. W. P. Thompson",
title = "Computational learning techniques for intraday {FX}
trading using popular technical indicators",
journal = "IEEE Transactions on Neural Networks",
year = "2001",
volume = "12",
number = "4",
pages = "744--754",
month = jul,
keywords = "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",
ISSN = "1045-9227",
URL = "http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/2000/ieeetrading.pdf",
doi = "doi:10.1109/72.935088",
abstract = "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",
notes = "CODEN: ITNNEP. INSPEC Accession Number:6997298
Location: technical report WP30/2000
",
}
@InProceedings{DBLP:conf/ideal/DempsterR02,
author = "M. A. H. Dempster and Y. S. Romahi",
title = "Intraday {FX} Trading: An Evolutionary Reinforcement
Learning Approach",
booktitle = "Proceedings of Third International Conference on
Intelligent Data Engineering and Automated Learning -
IDEAL 2002",
year = "2002",
editor = "Hujun Yin and Nigel M. Allinson and Richard T. Freeman
and John A. Keane and Simon J. Hubbard",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "2412",
pages = "347--358",
address = "Manchester",
month = "12-14 " # aug,
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-44025-9",
bibsource = "DBLP, http://dblp.uni-trier.de",
URL = "http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/2002/WP3-2002.pdf",
URL = "http://link.springer.de/link/service/series/0558/bibs/2412/24120347.htm",
abstract = "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.",
notes = "Location: technical report WP03/2002
",
}
@Article{Dempster:2006:ESA,
author = "M. A. H. Dempster and V. Leemans",
title = "An automated {FX} trading system using adaptive
reinforcement learning",
journal = "Expert Systems with Applications",
year = "2006",
volume = "30",
number = "3",
pages = "543--552",
month = apr,
note = "Special Issue on Financial Engineering",
keywords = "genetic algorithms, genetic programming",
URL = "http://mahd-pc.jbs.cam.ac.uk/archive/PAPERS/2004/WP18.pdf",
doi = "doi:10.1016/j.eswa.2005.10.012",
abstract = "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.",
notes = "Centre for Financial Research, Judge Business School,
University of Cambridge & Cambridge Systems Associates
Limited, Cambridge, UK Also technical report
WP18/2004
",
}
@Article{Deneubourg1986176,
author = "J. L. Deneubourg and S. Aron and S. Goss and J. M.
Pasteels and G. Duerinck",
title = "Random behaviour, amplification processes and number
of participants: How they contribute to the foraging
properties of ants",
journal = "Physica D: Nonlinear Phenomena",
volume = "22",
number = "1-3",
pages = "176--186",
year = "1986",
note = "Proceedings of the Fifth Annual International
Conference",
ISSN = "0167-2789",
doi = "doi:10.1016/0167-2789(86)90239-3",
URL = "http://www.sciencedirect.com/science/article/B6TVK-4CVPV04-F/2/80230b3fab67ba01fc8a22aa94873a7e",
notes = "Not on GP",
}
@PhdThesis{Denham:thesis,
title = "Predicci{\'o}n de la Evoluci{\'o}n de los Incendios
Forestales Guiada Din{\'a}micamente por los Datos",
author = "Monica Malen Denham",
year = "2009",
school = "Universitat Autonoma de Barcelona. Departament
d'Arquitectura de Computadors i Sistemes Operatius",
address = "Spain",
keywords = "genetic algorithms, forest fire prediction",
bibsource = "OAI-PMH server at www.tdx.cesca.es",
contributor = "Ana Cort{\'e}s Fit{\'e}",
institution = "Universitat Aut{\`o}noma de Barcelona",
language = "spa",
oai = "oai:UAB.es:TDX-0322111-153520",
rights = "Copyright information available at source archive",
subject = "469 - DEPARTAMENT D'ARQUITECTURA DE COMPUTADORS I
SISTEMES OPERATIUS",
URL = "http://www.tdx.cat/bitstream/handle/10803/5776/mmd1de1.pdf",
URL = "http://www.tesisenxarxa.net/TDX-0322111-153520/",
URL = "http://www.tesisenxarxa.net",
size = "163 pages",
abstract = "Desde hace a{\~n}os los incendios forestales son una
amenaza para la calidad de vida en nuestro planeta,
dado que la cantidad y magnitud de los mismos se ha
incrementado de forma alarmante. Actualmente, existe un
intenso trabajo para la lucha contra estos incendios y
la disminuci{\'o}n r{\'a}pida y efectiva de su avance,
de sus consecuencias y de sus peligros. La
predicci{\'o}n del comportamiento del fuego en
incendios forestales es un tema que se est{\'a}
desarrollando hace tiempo en este marco. Desde la
inform{\'a}tica, se han desarrollado diversos
simuladores del comportamiento del fuego en incendios
forestales [3] [4] [5] [14] [17]. Estos simuladores
calculan el avance del fuego, dependiendo de su estado
inicial y de las caracter{\'i}sticas del lugar donde
dicho incendio se desarrolla. Esto es,
caracter{\'i}sticas de la topograf{\'i}a,
vegetaci{\'o}n [2], humedad del combustible, humedad
relativa del ambiente, estado del viento, etc. Estos
simuladores son utilizados para predecir el avance del
fuego en un lugar y momento espec{\'i}ficos. En este
marco, una predicci {\'o}n es realmente {\'u}til si es
de buena calidad (se corresponde con la real propagaci
on del fuego) y si la respuesta est a dentro de un
ll{\'i}mite de tiempo acotado. Por lo tanto,
necesitamos simulaciones con alta calidad de respuesta,
que realmente realmente reflejen el real avance del
fuego, y respuestas que se obtengan velozmente,
minimizando el tiempo de la misma. Estos dos factores
son necesarios y determinan caracter {\'i}sticas
importantes de nuestro trabajo Un problema
frecuentemente encontrado en la utilizaci{\'o}n de
estas herramientas inform{\'a}ticas para predecir el
comportamiento del fuego es la cantidad y complejidad
de los datos de entrada. Normalmente, este tipo de
simuladores necesita numerosos datos de entrada, que
describan de forma correcta el entorno donde se
desarrolla el fuego. Topograf{\'i}a, meteorolog{\'i}a y
vegetaci{\'o}n del entorno del fuego deben estar
descriptos de forma adecuada en el nivel de
abstracci{\'o}n y detalle que el simulador utilizado
requiera. En la realidad, es muy dif{\'i}cil disponer
de una correcta descripci{\'o}n de todas estas
variables (y sus interacciones). Esta dificultad radica
principalmente en: naturaleza din{\'a}mica de algunos
factores (que var{\'i}an y siguen su propio patr{\'o}n
de comportamiento), par{\'a}metros que no pueden ser
medidos directamente (por lo que se utilizan
estimaciones de los mismos), par{\'a}metros que no
pueden ser medidos en todos los puntos
(utiliz{\'a}ndose interpolaciones), mapas
(topogr{\'a}ficos, vegetaci{\'o}n, etc.), los cuales
pueden estar desactualizados, o utilizar
discretizaciones que representan de forma incorrecta
las caracter{\'i}sticas que est{\'a}n representando,
etc. Es necesario disponer una correcta descripci{\'o}n
del entorno del fuego, dado que predicciones con datos
de entrada que no sean correctos, no ser{\'a}n
{\'u}tiles, pues predecir{\'a}n un fuego en un entorno
que no es el real. En este trabajo de investigaci{\'o}n
se ha propuesto un framework donde se tiene como
objetivo mejorar la calidad de los datos de entrada del
simulador utilizado. Adem{\'a}s, se pone gran esfuerzo
en minimizar los tiempos de respuesta. En este trabajo
se utiliza el simulador fireLib [5], un simulador que
implementa el modelo de propagaci{\'o}n de fuego
desarrollado por Rothermel [20] [21]. Para mejorar la
calidad de los datos de entrada, se realiza un
procesamiento sobre el espacio de b{\'u}squeda que es
el resultado de considerar todas las posibles
combinaciones de los par{\'a}metros de entrada en sus
rangos de variaci{\'o}n. Esto da como resultado un
espacio de b{\'u}squeda muy grande. Con el objetivo de
evitar que esta b{\'u}squeda penalice el tiempo de
respuesta, se utiliza un algoritmo gen{\'e}tico [16]
[19] guiado din{\'a}micamente por los datos: Dynamic
Data Driven Genetic Algorithm [7] [8] [9].",
abstract = "Forest fires are part of natural balance in our
planet. Natural fires are provoked by natural factors
combination: dry seasons, favorable fuel moistures,
electrical storms, volcanoes, earth{$\neg$}quakes, etc.
Natural forest fires can devastate overall forests as
well as productive forest areas, farms, etc. Nowadays,
human is arduously working on this problem in order to
avoid and to reduce forest fires damages. As results of
this effort there exist different kind of studies,
strategies and tools used to prevent fires, to define
risk areas and to reduce the fire effects when a
disaster occurs. Forest fire control and manage are
complex tasks, due to forest fires are related with
weather, topography, human population, fire aspects,
etc., having all of them its own behavior pattern.
Furthermore, there exist several behavior interactions
between them, that determine additional behavior
characteristics, resulting in very complex fire
behavior pattern. This problem is nowadays studied by
di_erent areas. Technology is not an exception, and
informatics tools are continuously developed. One of
the most important computer tool for forest fires are
forest fire behavior simulators. These kind of
simulators determine the advance of the fire line,
taking into account beginning fire state, topography
conditions, weather aspects, fuel characteristics, etc
[3], [4], [5] [14] [17] [20] [21]. Furthermore, this
kind of problem has the additional requirement that
time constraints add. A forest fire prediction is
really useful when it is available before the real fire
propagation occurs and when prediction really describes
real fire progress. In order to be reliable, it is
necessary high quality predictions, it means, predicted
fire progress must be as similar to the real fire as
possible. During this work, we are going to use the
forest fire simulator called fireLib [5] holding it in
a framework that attempts to improve input parameter
accuracy in order to increase prediction quality.
Usually, a prediction is made using a forest fire
simulator which receives several inputs (fire
environment description) and it returns the state of
the fire for a later instant of time. Input parameters
usually include: initial fire front state, topography,
vegetation [2], wind, fuel hu{$\neg$}midities, and
additionally, relative moisture, cover crown, cover
clouds, etc. All these input parameters depend on the
forest fire simulator used. Thus, having initial fire
line and environmental characteristics, simulator uses
some fire prop{$\neg$}agation model in order to
simulate fire behavior. Taking into account this
classical prediction method, we can see that it is a
straight, simply method and it has the advantage of
performing just one simulation (what means low
processor time requirements). But these advantages are
in a sense the main weak point of the method: final
prediction quality depends on the suitability of the
unique simulation (that means, using a unique input
parameters set). As we had mentioned during previous
paragraphs, the accuracy of the input parameters are
really open to debate due to having its actual values
is not easy, some times it is impossible. Consequently,
we are going to present a method where a search of
better parameter values is performed in order to reduce
input data uncertainty [1]. This method consists of two
stages: a new stage was added before the prediction
step. This new stage is called Calibration stage, and
it allows us to find a set of input parameter values
that achieve a good simulation from instant ti to
instant ti+1. Then, we can use this good set of input
parameters to predict fire behavior during the next
instant of time (ti+2). Once a combination of values
for the input parameters is founded, we consider that
the environmental characteristics that are good
described by these values at instant ti+1 will
re{$\neg$}main useful for the subsequent instant of
time (from ti+1 to ti+2).",
abstrct = "Then we use these values for obtaining the predicted
map for instant ti+2. This scheme is based on the
premise that environmental features will be maintained
during involved time steps. In addition, each of these
input parameters have its own valid range where they
can vary, and in fact, these ranges may be di_erent:
they vary depending on the characteristic that it
represents. Thus, the amount of di_erent combinations
of these parameter values leaves us a very big search
space. In order to avoid that this Calibration Stage
becomes a bottle neck, we had developed a Dynamic Data
Driven Genetic Algorithm [16] [19]. Strategies adopted
through this application result in an e_cient search
solution. Our Dynamic Data Driven Genetic Algorithm
dynamically incorporates new data (from storage device
or on line captured) promising more accuracy data
analysis, more accurate pre{$\neg$}dictions, more
precise controls and more reliable outcomes [8] [9]
[7]. Taking into account that two stages method needs
the information of the real fire spread from instant ti
to ti + 1, useful information will be obtained from the
analysis of this real fire progress. This information
will be used for steering searching process through
genetic algorithm, in order to improve the values of
the parameters. Our genetic algorithm intents to
minimize our error function (individual fitness): error
value determines the di_erences between real fire line
and simulated fire line. Due to simulator
imple{$\neg$}ments a cellular automata model, all
involved maps are a grid of cells, then, the error
function is based on a cell by cell comparison (of real
and simulated maps). When either slope nor wind are
strong enough, fire grows forming a circular shape. But
when wind or slope are presented (both or one of them),
they influence fire growth in a de{$\neg$}terminant
way. Shape, velocity, direction, intensity, all of
these fire features are influenced by wind and slope
factors. Wind velocity and direction, slope inclination
and aspect combination are crucial in fire spread
behavior. Thus, knowing wind and slope decisive
influences and knowing the real fire shape (by the
analysis of real fire at instant ti+1 disposed in
calibration stage), we can combine this
informa{$\neg$}tion in order to incorporate additional
data that will be useful in order to improve fire
spread simulations. This information will be used as
feedback information in order to improve
simula{$\neg$}tion accuracy. Actually, slope and real
propagation are known. This information is used to
calculate wind speed and wind direction needed to
generate the observed fire propagation in presence of
the current slope features. Once wind main
characteristics are calculated they will be used
through two methods for dynamically steering our
genetic algorithm: Computational and Analytical
Methods. In particular, Analytical Method was developed
in order to validate Computational Method operation.
Computational Method uses di_erent forest fires
information (including fire environment) in order to
discover wind main features. Forest fires data can come
from historical real fires, prescribed burnings, or
synthetic simulated fires (using a forest fire
simulator). Forest fire main characteristics are stored
through a data base. Data base information must be as
complete as possible, in order to reflect the most
amount of fire cases that can happen in the real world.
In this data base several fire spreads are stored and
we look for a fire progress similar to the real fire
line observed for instant ti+1 in presence of similar
or equal slope characteristics. Historical real fires
information could be used in order to construct our
application data base. Unfortunately, detailed real
fires information was not available for us since we
were deal{$\neg$}ing with prescribe and synthetic
fires.",
abstrct = "In order to generate a suitable data base the forest
fire simulator fireLib was used for obtaining a high
number of detailed burning cases. Computational Method
is based on following process: real forest fire
progress is analyzed at time ti+1, thus, fire progress
direction, velocity and distance are obtained. Then,
all real forest fire characteristics are used in order
to find the most similar fire into the data base. When
most similar fire is founded, wind direction and
velocity are injected during genetic algorithm
operation. Specifically, these wind values will be used
to define a subrange through the whole parameter valid
range and, when mutation operator takes place, wind
values will be assigned using a ran{$\neg$}dom value
limited by the new subrange (taking into account data
base cases incompleteness). Analytical Method was
created in order to evaluate the proper operation of
Computational Method. This method is based on an
exhaustive study of Rothermel model and fireLib
sim{$\neg$}ulator ([21] and [5]). This method is based
on some calculus performed by the simulator in order to
obtain fire direction and velocity, by the combination
of wind, slope and environmental factors. Once the
model and simulator was studied and understood, we use
the steps performed by the simulator but in a suitable
order, for obtaining wind characteristics from slope
and real fire characteristics combination. When
Analytical Method is applied, each simulation is
performed using an individual (sce{$\neg$}nario) and
ideal wind values are calculated and stored together
with such individual. Then, these values are assigned
as individual wind velocity and direction when elitism
or mutation operations take place during Dynamic Data
Driven genetic algorithm execution. In practice, we
expect that this method will be more precise than
Computational Method but, by contrast, it is severely
coupled to the underlying simulator (being this fact an
important method drawback). Taking into account
application response time limits, non simulation
dependences and simu{$\neg$}lations time requirements,
proposed application was developed using the parallel
paradigm [15] [18], dividing simulation processes and
error calculus between di_erent parallel tasks. Master
process performs genetic algorithm operations and
distributes population individu{$\neg$}als between
worker processes. Every time a worker process receives
a group of individuals, it performs the simulation and
calculates the error function with each individual. In
order to avoid that application communication pattern
became a bottle neck, individuals are distributed by
groups (chunks) instead of individual transmissions.
When a worker final{$\neg$}izes the evaluation of a
specific chunk, this worker process returns the
evaluated chunk to the master process. Then, master
process sends another non evaluated chunk to it until
all chunks are evaluated. During this work, two main
objectives were considered: prediction quality
improvement (what means prediction error reduction) and
reduction of prediction process time requirements.
Several times, these two aspects have mutual
dependencies: suitable simulation accuracy can be
achieved if enough time is available for prediction
process. On the other hand, if prediction results are
required in a short term of time, this feature can
attempt on prediction quality. During this work,
experimental results were analyzed and best application
characteristics were studied. We could see that 2
stages prediction method achieve best results when they
are compared with classical prediction. Performing a
pre-search of input parameters values achieve an
important error reduction due to the use of suitable
input parameters values. Steering methods,
Computational Method as well as Analytical Method, in
most of the cases, reduce simulation errors, achieving
more precise simulations during calibration stage, and
consequently, more precise predictions [10] [11]
[13].",
abstract = "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.",
}
@InProceedings{Heijer:2010:EvoMUSART,
author = "E. {den Heijer} and A. E. Eiben",
title = "Comparing Aesthetic Measures for Evolutionary Art",
booktitle = "EvoMUSART",
year = "2010",
editor = "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",
volume = "6025",
series = "LNCS",
pages = "311--320",
address = "Istanbul",
month = "7-9 " # apr,
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-12241-5",
doi = "doi:10.1007/978-3-642-12242-2_32",
abstract = "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.",
notes = "EvoMUSART'2010 held in conjunction with EuroGP'2010
EvoCOP2010 EvoBIO2010",
}
@InProceedings{denHeijer:2010:cec,
author = "Eelco {den Heijer} and A. E. Eiben",
title = "Using aesthetic measures to evolve art",
booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-6910-9",
size = "8 pages",
abstract = "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.",
doi = "doi:10.1109/CEC.2010.5586245",
notes = "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 \cite{5586245}",
}
@InProceedings{denHeijer:2011:GECCO,
author = "Eelco {den Heijer} and Agoston Endre Eiben",
title = "Evolving art with scalable vector graphics",
booktitle = "GECCO '11: Proceedings of the 13th annual conference
on Genetic and evolutionary computation",
year = "2011",
editor = "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",
isbn13 = "978-1-4503-0557-0",
pages = "427--434",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, Digital entertainment technologies and
arts",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001576.2001635",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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",
notes = "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, \cite{Ross: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 \cite{2001635} GECCO-2011 A joint meeting
of the twentieth international conference on genetic
algorithms (ICGA-2011) and the sixteenth annual genetic
programming conference (GP-2011)",
}
@InProceedings{DBLP:conf/ices/DeniziakG08,
author = "Stanislaw Deniziak and Adam Gorski",
title = "Hardware/Software Co-synthesis of Distributed Embedded
Systems Using Genetic Programming",
booktitle = "Proceedings of the 8th International Conference
Evolvable Systems: From Biology to Hardware, ICES
2008",
year = "2008",
editor = "Gregory Hornby and Luk{\'a}s Sekanina and Pauline C.
Haddow",
series = "Lecture Notes in Computer Science",
volume = "5216",
pages = "83--93",
address = "Prague, Czech Republic",
month = sep # " 21-24",
publisher = "Springer",
bibsource = "DBLP, http://dblp.uni-trier.de",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-540-85856-0",
doi = "doi:10.1007/978-3-540-85857-7_8",
abstract = "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.",
notes = "Cracow University of Technology, Dept. of Computer
Engineering, Warszawska 24, 31-155 Cracow, Poland",
}
@Article{Deo2008340,
author = "M. C. Deo",
title = "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",
journal = "Applied Ocean Research",
volume = "30",
number = "4",
pages = "340",
year = "2008",
ISSN = "0141-1187",
doi = "doi:10.1016/j.apor.2009.02.002",
URL = "http://www.sciencedirect.com/science/article/B6V1V-4VY6FSK-1/2/70a6592b22ba65b93887b8122e985f75",
size = "1 page",
notes = "Reply to \cite{Gandomi2008338}. Original article
\cite{Kalra200799}",
}
@Article{Deo:2008:IJTS,
author = "Omkar Deo and V. Jothiprakash and M. C. Deo",
title = "Genetic Programming to Predict Spillway Scour",
journal = "International Journal of Tomography \& Statistics",
year = "2008",
volume = "8",
number = "W08",
pages = "32--45",
month = "Winter",
keywords = "genetic algorithms, genetic programming, neural
networks, scour predictions spillway scour, skijump
bucket",
ISSN = "0972-9976",
URL = "http://www.ceser.res.in/ceserp/index.php/ijts/article/view/532",
size = "14 pages",
abstract = "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.",
notes = "Discipulus.
Datta Meghe College of Engineering, Airoli, Navi
Mumbai, 400708, India",
}
@InProceedings{conf/evoW/DeodharM10,
title = "Grammatical Evolution Decision Trees for Detecting
Gene-Gene Interactions",
author = "Sushamna Deodhar and Alison A. Motsinger-Reif",
booktitle = "8th European Conference on Evolutionary Computation,
Machine Learning and Data Mining in Bioinformatics
(EvoBIO 2010)",
publisher = "Springer",
year = "2010",
editor = "Clara Pizzuti and Marylyn D. Ritchie and Mario
Giacobini",
volume = "6023",
pages = "98--109",
series = "Lecture Notes in Computer Science",
address = "Istanbul, Turkey",
month = apr # " 7-9",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
isbn13 = "978-3-642-12210-1",
doi = "doi:10.1007/978-3-642-12211-8",
}
@InProceedings{derrig:1998:hecagcs,
author = "Daniel Derrig and James D. Johannes",
title = "Hierarchical Exemplar Based Credit Allocation for
Genetic Classifier Systems",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "622--628",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, classifiers",
ISBN = "1-55860-548-7",
notes = "GP-98",
}
@InProceedings{derrig:1998:deosc,
author = "Daniel Derrig and James Johannes",
title = "Deleting End-of-Sequence Classifiers",
booktitle = "Late Breaking Papers at the Genetic Programming 1998
Conference",
year = "1998",
editor = "John R. Koza",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "Stanford, CA, USA",
month = "22-25 " # jul,
publisher = "Stanford University Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "GP-98LB",
}
@InProceedings{deschain:2000:ASTC,
author = "Larry M. Deschaine and Fred A. Zafran and Janardan J.
Patel and David Amick and Robert Pettit and Frank D.
Francone and Peter Nordin and Edward Dilkes and Laurene
V. Fausett",
title = "Solving the Unsolved Using Machine Learning, Data
Mining and Knowledge Discovery to Model a Complex
Production Process",
booktitle = "Advanced Technology Simulation Conference",
year = "2000",
editor = "M. Ades",
address = "Wasington, DC, USA",
organisation = "Society for Computer Simulations",
month = "22-26 " # apr,
keywords = "genetic algorithms, genetic programming, discipulus",
broken = "http://pw2.netcom.com/%7elmdmit84/SoilStabilization2000.pdf",
URL = "http://citeseer.ist.psu.edu/deschaine00solving.html",
size = "6 pages",
notes = "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:-(",
}
@InProceedings{Deschain:2001:ASTC,
author = "Larry M. Deschaine and Janardan J. Patel and Ronald D.
Guthrie and Joseph T. Grimski and M. J. Ades",
title = "Using Linear Genetic Programming to Develop a {C/C++}
Simulation Model of a Waste Incinerator",
booktitle = "Advanced Technology Simulation Conference",
year = "2001",
editor = "M. Ades",
address = "Seattle",
month = "22-26 " # apr,
organisation = "Society for Computer Simulations",
keywords = "genetic algorithms, genetic programming, discipulus,
DSS, 10 demes",
broken = "http://pw2.netcom.com/~lmdmit84/ASTC2001-LGP-INCINERATOR.pdf",
URL = "http://www.aimlearning.com/Environmental.Engineering.pdf",
URL = "http://citeseer.ist.psu.edu/451766.html",
URL = "http://citeseer.ist.psu.edu/396498.html",
abstract = "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.",
notes = "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:-(",
}
@Article{deschain:2000:PCAI,
author = "Larry M. Deschaine",
title = "Tackling Real-World Environmental Challenges with
Linear Genetic Programming",
journal = "PCAI",
year = "2000",
volume = "15",
number = "5",
pages = "35--37",
month = sep # "/" # oct,
keywords = "genetic algorithms, genetic programming",
size = "pages",
notes = "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:-(",
}
@Article{Deschaine:2001:PCAI,
author = "L. M. Deschaine and Jennifer McCormack and D. Pyle and
F. Francone",
title = "Genetic Algorithms and Intelligent Agents Team Up:
Techniques for Data Assembly, Preprocessing, Modeling,
and Decision Optimization",
journal = "PCAI magazine",
year = "2001",
volume = "15",
number = "3",
pages = "38--44",
month = may # "/" # jun,
keywords = "genetic algorithms, genetic programming",
abstract = "Discussing a set of techniques for optimal real-time
decision making from distributed, heterogeneous
information found in financial, industrial, and
scientific data",
notes = "http://www.pcai.com/web/indexes/index_vol_15.html
",
}
@InProceedings{deschaine:2002:FEA,
author = "Larry M. Deschaine and Frank D. Francone",
title = "Design Optimization Integrating the Outer
Approximation Method with Process Simulators and Linear
Genetic Programming",
booktitle = "Proceedings of the 6th Joint Conference on Information
Science",
year = "2002",
editor = "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",
pages = "618--621",
address = "Research Triangle Park, North Carolina, USA",
month = mar # " 8-13",
publisher = "JCIS / Association for Intelligent Machinery, Inc.",
bibsource = "DBLP, http://dblp.uni-trier.de",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-9707890-1-7",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/deschaine/FEA_2002_Design_Optimization.pdf",
abstract = "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.",
notes = "
FEA2002 In conjunction with Sixth Joint Conference on
Information Sciences
My printer refuses to deal with this as PDF",
}
@InProceedings{ASTC_2002_UXOFinder_Invention_Paper,
author = "Larry M. Deschaine and Richard A. Hoover and Joseph N.
Skibinski and Janardan J. Patel and Frank Francone and
Peter Nordin and M. J. Ades",
title = "Using Machine Learning to Compliment and Extend the
Accuracy of {UXO} Discrimination Beyond the Best
Reported Results of the {Jefferson} Proving Ground
Technology Demonstration",
booktitle = "2002 Advanced Technology Simulation Conference",
year = "2002",
address = "San Diego, CA, USA",
month = "14-18 " # apr,
organisation = "The Society for Modeling and Simulation
International",
keywords = "genetic algorithms, genetic programming, Unexploded
ordnance, anomaly detection, geophysics, UXO",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/deschaine/ASTC_2002_UXOFinder_Invention_Paper.pdf",
URL = "http://www.scs.org/docInfo.cfm?get=1488",
size = "7 pages",
abstract = "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.",
notes = "http://www.scs.org/confernc/astc/astc02/ASTC02finalprogram.pdf
",
}
@InProceedings{Deschaine:2003:informs,
author = "Larry Deschaine and Janos D. Pinter and Sudip Regmi",
title = "Developing High Fidelity Approximations to Expensive
Simulation Models for Expedited Optimization",
booktitle = "INFORMS Annual Meeting Conference",
year = "2003",
address = "Atlanta, Georgia, USA",
month = oct # " 19-22",
note = "Presented at",
keywords = "genetic algorithms, genetic programming",
URL = "http://informs.emeetingsonline.com/emeetings/formbuilder/clustersessiondtl.asp?csnno=1278",
abstract = "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.",
notes = "
",
}
@Article{GDI0605scr,
author = "Larry Deschaine",
title = "Using Information fusion, machine learning, and global
optimisation to increase the accuracy of finding and
understanding items interest in the subsurface",
journal = "GeoDrilling International",
year = "2006",
number = "122",
pages = "30--32",
month = may,
address = "London",
keywords = "genetic algorithms, genetic programming, Groundwater
plumes, Source zones, Landmines and unexploded ordnance
UXO",
URL = "http://www.mining-journal.com/gdi_magazine/pdf/GDI0605scr.pdf",
size = "3 pages",
notes = "
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.",
}
@InProceedings{Deschaine:2006:euro,
author = "Larry M. Deschaine and Frank D. Francone and Janos D.
Pinter and Melissa McKay and Jeff Warren and Seth
Blanchard",
title = "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}",
booktitle = "EURO XXI",
year = "2006",
editor = "Tuula Kinnunen",
address = "Reykjavik, Iceland",
month = "2-6 " # jul,
organisation = "Icelandic Operations Research Society and The
Association of European OR Societies",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Euro2006_Deschaine_Finding_Subsurface_Objects_of_Interest_6-29-06_Final.pdf",
URL = "https://www.euro-online.org/euro21/display.php?page=treate_abstract&frompage=edit_session_cluster&sessionid=661&paperid=3361",
abstract = "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.",
notes = "
http://www.euro2006.org/
",
}
@Article{Deschaine:2008:GPEM,
author = "Larry M. Deschaine",
title = "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",
journal = "Genetic Programming and Evolvable Machines",
year = "2008",
volume = "9",
number = "4",
pages = "371--372",
month = dec,
note = "Book Review",
keywords = "genetic algorithms, genetic programming, evolvable
hardware",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-008-9068-8",
size = "2 pages",
notes = "review of \cite{TinaYu:2008:book}",
}
@InCollection{deshpande:2002:CJSGASBS,
author = "Nishant Deshpande",
title = "Comparison of a Job-Shop Scheduler using Genetic
Algorithms with a {SLACK} Based Scheduler",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2002",
year = "2002",
editor = "John R. Koza",
pages = "73--82",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms",
URL = "http://www.genetic-programming.org/sp2002/Deshpande.pdf",
notes = "part of \cite{koza:2002:gagp}",
}
@InProceedings{desjarlais:1999:CSOUGAST,
author = "Lisa M. Desjarlais and Mohammad-R. Akbarzadeh-T. and
Craig W. Wright",
title = "Control System Optimization Using Genetic Algorithms
within the SoftLab Toolkit",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1774",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "real world applications, poster papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-781.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-781.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@Article{deSousa:2004:GPEM,
author = "Janaina S. {de Sousa} and Lalinka {de C. T. Gomes} and
George B. Bezerra and Leandro N. {de Castro} and
Fernando J. {Von Zuben}",
title = "An Immune-Evolutionary Algorithm for Multiple
Rearrangements of Gene Expression Data",
journal = "Genetic Programming and Evolvable Machines",
year = "2004",
volume = "5",
number = "2",
pages = "157--179",
month = jun,
keywords = "genetic algorithms, genetic programming, gene
expression, microarray, artificial immune systems,
clustering, evolutionary algorithms",
ISSN = "1389-2576",
doi = "doi:10.1023/B:GENP.0000023686.59617.57",
abstract = "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.",
notes = "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",
}
@InCollection{deSouza:2009:EC,
title = "Genetic Programming and Boosting Technique to Improve
Time Series Forecasting",
author = "Luzia Vidal {de Souza} and Aurora T. R. Pozo and
Anselmo C. Neto and Joel M. C. {da Rosa}",
booktitle = "Evolutionary Computation",
publisher = "InTech",
year = "2009",
editor = "Wellington Pinheiro dos Santos",
chapter = "6",
month = oct,
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-953-307-008-7",
URL = "http://www.intechopen.com/download/pdf/pdfs_id/10932",
URL = "http://www.intechopen.com/articles/show/title/genetic-programming-and-boosting-technique-to-improve-time-series-forecasting",
notes = "http://www.intechopen.com/books/show/title/evolutionary-computation",
bibsource = "OAI-PMH server at www.intechopen.com",
language = "eng",
oai = "oai:intechopen.com:10932",
size = "18 pages",
}
@Article{deSouza:2010:AI,
title = "Applying correlation to enhance boosting technique
using genetic programming as base learner",
author = "Luzia Vidal {de Souza} and Aurora Pozo and Joel
Mauricio Correa {da Rosa} and Anselmo Chaves Neto",
journal = "Applied Intelligence",
year = "2010",
number = "3",
volume = "33",
pages = "291--301",
keywords = "genetic algorithms, genetic programming",
publisher = "Springer Netherlands",
ISSN = "0924-669X",
doi = "doi:10.1007/s10489-009-0166-y",
size = "11 pages",
abstract = "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.",
affiliation = "University of Parana (UFPR), CP 19:081, CEP: 81531-970
Curitiba, Brazil",
}
@InProceedings{dessi:1999:AAASDGP,
author = "Antonello Dessi and Antonella Giani and Antonina
Starita",
title = "An Analysis of Automatic Subroutine Discovery in
Genetic Programming",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "996--1001",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-432.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-432.ps",
size = "6 pages",
abstract = "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.",
notes = "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)",
}
@Article{DeStefano:2002:PRL,
author = "C. {De Stefano} and A. Della Cioppa and A. Marcelli",
title = "Character preclassification based on genetic
programming",
journal = "Pattern Recognition Letters",
year = "2002",
volume = "23",
pages = "1439--1448",
number = "12",
abstract = "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.",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6V15-45J91MV-4/2/3e5c2ac0c51428d0f7ea9fc0142f6790",
keywords = "genetic algorithms, genetic programming, Character
recognition, Preclassification",
doi = "doi:10.1016/S0167-8655(02)00104-6",
}
@InProceedings{devaney:1995:mpimake,
author = "Judith E. Devaney",
title = "Converting pvmmake to mpimake under {LAM}, and {MPI}
and Parallel Genetic Programming",
booktitle = "MPI Developers Conference",
year = "1995",
editor = "Andrew Lumsdaine",
address = "University of Notre Dame",
month = "22-23 " # jun,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cse.nd.edu/mpidc95/proceedings/papers/postscript/devaney.ps",
URL = "http://citeseer.ist.psu.edu/devaney95experience.html",
abstract = "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.",
notes = "Data from
http://www.cse.nd.edu/mpidc95/proceedings/abstracts/html/devaney/
4 Nov 1997",
}
@InProceedings{devaney:2001:gpe,
author = "Judith Devaney and John Hagedorn and Olivier Nicolas
and Gagan Garg and Aurelien Samson and Martial Michel",
title = "A Genetic Programming Ecosystem",
booktitle = "Proceedings 15th International Parallel and
Distributed Processing Symposium, Abstracts and CDROM",
year = "2001",
pages = "1323--1330",
address = "Los Alamitos, CA, USA",
howpublished = "Abstracts and CD-ROM",
month = "23-27 " # apr,
publisher = "IEEE Computer Society",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7695-0990-8",
URL = "http://math.nist.gov/mcsd/savg/papers/bio.pdf",
URL = "http://math.nist.gov/mcsd/savg/papers/bio.pp.gz",
note = "IPDPS2001:WS",
abstract = "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.",
}
@InProceedings{devaney:2002:gecco:lbp,
title = "The Role of Genetic Programming in Describing the
Microscopic Structure of Hydrating Plaster",
author = "Judith E. Devaney and John G. Hagedorn",
booktitle = "Late Breaking Papers at the Genetic and Evolutionary
Computation Conference ({GECCO-2002})",
editor = "Erick Cant{\'u}-Paz",
year = "2002",
month = jul,
pages = "91--98",
address = "New York, NY",
publisher = "AAAI",
publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
keywords = "genetic algorithms, genetic programming",
notes = "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",
}
@InProceedings{conf/dis/DevaneyH02,
author = "Judith Ellen Devaney and John G. Hagedorn",
title = "Discovery in Hydrating Plaster Using Machine Learning
Methods",
booktitle = "5th International Conference on Discovery Science, DS
2002",
year = "2002",
editor = "Steffen Lange and Ken Satoh and Carl H. Smith",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "2534",
pages = "47--58",
address = "L{\"u}beck, Germany",
month = nov # " 24-26",
keywords = "genetic algorithms, genetic programming",
isbn13 = "3-540-00188-3",
URL = "http://math.nist.gov/mcsd/savg/papers/discov2002.pdf",
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.138.2341",
URL = "http://link.springer.de/link/service/series/0558/bibs/2534/25340047.htm",
doi = "doi:10.1007/3-540-36182-0_7",
bibsource = "DBLP, http://dblp.uni-trier.de",
bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
contributor = "CiteSeerX",
language = "en",
oai = "oai:CiteSeerXPSU:10.1.1.138.2341",
abstract = "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.",
}
@InProceedings{eurogp06:DevertBredecheSchoenauer,
author = "Alexandre Devert and Nicolas Bredeche and Marc
Schoenauer",
title = "Blindbuilder : {A} new encoding to evolve Lego-like
structures",
editor = "Pierre Collet and Marco Tomassini and Marc Ebner and
Steven Gustafson and Anik\'o Ek\'art",
booktitle = "Proceedings of the 9th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3905",
year = "2006",
address = "Budapest, Hungary",
month = "10 - 12 " # apr,
organisation = "EvoNet",
keywords = "genetic algorithms, genetic programming, context free
grammar",
ISBN = "3-540-33143-3",
pages = "61--72",
URL = "http://hal.ccsd.cnrs.fr/docs/00/05/44/74/PDF/article.pdf",
URL = "http://hal.inria.fr/inria-00000995/en/",
URL = "http://link.springer.de/link/service/series/0558/papers/3905/39050061.pdf",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
annote = "Alexandre Devert ",
bibsource = "OAI-PMH server at hal.ccsd.cnrs.fr",
contributor = "Alexandre Devert ",
coverage = "genetic programming",
identifier = "inria-00000995 (version 1)",
oai = "oai:hal.ccsd.cnrs.fr:inria-00000995_v1",
subject = "Computer Science/Artificial Intelligence; Computer
Science/Learning",
type = "ARTCOLLOQUE",
notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in
conjunction with EvoCOP2006 and EvoWorkshops2006",
}
@InProceedings{Devert:2006:ASPGP,
title = "Evolution design of buildable objects with blind
builder: an empirical study",
author = "Alexandre Devert and Nicolas Bredeche and Marc
Schoenauer",
booktitle = "Proceedings of the Third Asian-Pacific workshop on
Genetic Programming",
year = "2006",
editor = "The Long Pham and Hai Khoi Le and Xuan Hoai Nguyen",
pages = "98--109",
ISSN = "18590209",
address = "Military Technical Academy, Hanoi, VietNam",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/aspgp06/devert-bredeche-schoenauer-ASPGP2006.pdf",
size = "12 pages",
abstract = "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.",
notes = "http://www.aspgp.org",
}
@Misc{deVisscher:2011:arXiv,
title = "Automatic anomaly detection in high energy collider
data",
author = "Simon {de Visscher} and Michel Herquet",
year = "2011",
month = apr # "~13",
keywords = "genetic algorithms, genetic programming, high energy
physics, phenomenology, experiment, data analysis",
abstract = "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.",
bibsource = "OAI-PMH server at export.arxiv.org",
oai = "oai:arXiv.org:1104.2404",
URL = "http://arxiv.org/abs/1104.2404",
notes = "Comment: 5 pages, 2 figures",
}
@InProceedings{devylder:2003:gecco:workshop,
title = "Learning of Manipulation Behaviour by Demonstration
using Genetic Programming",
author = "Bart {De Vylder}",
pages = "268--271",
booktitle = "{GECCO 2003}: Proceedings of the Bird of a Feather
Workshops, Genetic and Evolutionary Computation
Conference",
editor = "Alwyn M. Barry",
year = "2003",
month = "11 " # jul,
publisher = "AAAI",
address = "Chigaco",
publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
notes = "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",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{dewell:1999:gnc,
author = "Larry D. Dewell and P. K. Menon",
title = "Low-Thrust Orbit Transfer Optimization Using Genetic
Search",
booktitle = "AIAA Guidance, Navigation and Control Conference",
year = "1999",
address = "Portland, OR, USA",
publisher = "American Institute of Aeronautics and Astronautics",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.optisyn.com/research/papers/papers/1999/gnc_99.pdf",
URL = "http://citeseer.ist.psu.edu/513854.html",
size = "7 pages",
abstract = "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.",
}
@InCollection{kinnear:DHaeseleer,
title = "Effects of Locality in Individual and Population
Evolution",
author = "Patrik D'haeseleer and Jason Bluming",
booktitle = "Advances in Genetic Programming",
publisher = "MIT Press",
editor = "Kenneth E. {Kinnear, Jr.}",
year = "1994",
chapter = "8",
keywords = "genetic algorithms, genetic programming",
pages = "177--198",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262111888",
size = "22 pages",
}
@InProceedings{Dhaeseleer:1994:cpcGP,
author = "Patrik D'haeseleer",
title = "Context preserving crossover in genetic programming",
booktitle = "Proceedings of the 1994 IEEE World Congress on
Computational Intelligence",
year = "1994",
volume = "1",
pages = "256--261",
address = "Orlando, Florida, USA",
month = "27-29 " # jun,
publisher = "IEEE Press",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/WCCI94_CPC.ps.Z",
URL = "http://www.cs.unm.edu/~patrik/WCCI94_CPC.mac.ps",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=00350006",
doi = "doi:10.1109/ICEC.1994.350006",
keywords = "genetic algorithms, genetic programming, S-expression
tree, context-preserving crossover, crossover
operators, matching coordinates, node coordinate
scheme, subtrees,optimisation, path planning,
programming, trees (mathematics)",
size = "6 pages",
abstract = "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.",
notes = "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.
",
}
@InCollection{Dharma:1997:amctsa,
author = "Prisdha Dharma",
title = "Automatic Model Construction for Time Series Analysis
via Genetic Algorithm",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1997",
publisher = "Stanford Bookstore",
year = "1997",
editor = "John R. Koza",
pages = "28--35",
address = "Stanford, California, 94305-3079 USA",
month = "17 " # mar,
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-205981-2",
notes = "part of \cite{koza:1997:GAGPs}",
}
@InCollection{dhingra:2002:ESIDAO,
author = "Philip Dhingra",
title = "Evolution of Simple Intelligence Distribution in
Artificial Organisms",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2002",
year = "2002",
editor = "John R. Koza",
pages = "83--92",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.genetic-programming.org/sp2002/Dhingra.pdf",
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.1093",
language = "en",
oai = "oai:CiteSeerXPSU:10.1.1.141.1093",
abstract = "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.",
notes = "part of \cite{koza: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",
}
@Article{Di2009612,
author = "Wenhui Di and Bo Sun and Lixin Xu",
title = "Dynamic Simulations of Nonlinear Multi-Domain Systems
Based on Genetic Programming and Bond Graphs",
journal = "Tsinghua Science \& Technology",
volume = "14",
number = "5",
pages = "612--616",
year = "2009",
ISSN = "1007-0214",
doi = "doi:10.1016/S1007-0214(09)70125-7",
URL = "http://www.sciencedirect.com/science/article/B7RKT-4XBR35X-B/2/f79f7984ea487a2629d93cc7ae6e2651",
keywords = "genetic algorithms, genetic programming, bond graph
(BG), evolutionary computation, system simulation",
abstract = "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.",
}
@InProceedings{dichio:2005:gecco,
author = "Riccardo Poli and Cecilia {Di Chio} and William B.
Langdon",
title = "Exploring extended particle swarms: a genetic
programming approach",
booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on
Genetic and evolutionary computation",
year = "2005",
editor = "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",
volume = "1",
ISBN = "1-59593-010-8",
pages = "169--176",
address = "Washington DC, USA",
URL = "http://www.cs.essex.ac.uk/staff/poli/papers/geccopso2005.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p169.pdf",
doi = "doi:10.1145/1068009.1068036",
publisher = "ACM Press",
publisher_address = "New York, NY, 10286-1405, USA",
month = "25-29 " # jun,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, Swarm
Intelligence, particle swarm optimisation, PSO,
performance",
size = "8 pages",
abstract = "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 \cite{poli: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
\cite{poli: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.",
notes = "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",
}
@InProceedings{DiChio:2005:gsice,
author = "Cecilia {Di Chio} and Riccardo Poli and William B.
Langdon",
title = "Evolution of Force-Generating Equations for {PSO}
using {GP}",
booktitle = "AI*IA Workshop on Evolutionary Computation,
Evoluzionistico GSICE05",
year = "2005",
editor = "Sara Manzoni and Matteo Palmonari and Fabio Sartori",
address = "University of Milan Bicocca, Italy",
month = "20 " # sep,
keywords = "genetic algorithms, genetic programming, XPS",
ISBN = "88-900910-0-2",
URL = "http://www.cs.essex.ac.uk/staff/poli/papers/gsice2005.pdf",
size = "10 pages",
abstract = "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.",
notes = "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",
}
@InProceedings{DiChio:2006:evophd,
author = "Cecilia {Di Chio}",
title = "Extended Particle Swarm to Simulate Biology-Like
Systems",
booktitle = "European Graduate Student Workshop on Evolutionary
Computation",
year = "2006",
editor = "Mario Giacobini and Jano {van Hemert}",
pages = "31--43",
address = "Budapest, Hungary",
month = "10 " # apr,
keywords = "genetic algorithms, genetic programming, PSO, XPS",
URL = "http://www.vanhemert.co.uk/publications/EvoPhD2006.pdf",
size = "13 pages",
abstract = "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.",
notes = "http://evonet.lri.fr/eurogp2006/?page=evophd",
}
@InProceedings{eurogp07:DiChio,
author = "Cecilia {Di Chio} and Paolo {Di Chio}",
title = "Group-Foraging with Particle Swarms and Genetic
Programming",
editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and
Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar",
booktitle = "Proceedings of the 10th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "4445",
year = "2007",
address = "Valencia, Spain",
month = "11-13 " # apr,
pages = "331--340",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-71602-5",
isbn13 = "978-3-540-71602-0",
doi = "doi:10.1007/978-3-540-71605-1_31",
abstract = "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.",
notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in
conjunction with EvoCOP2007, EvoBIO2007 and
EvoWorkshops2007",
}
@InCollection{dickinson:1994:d-i,
author = "Andrew Dickinson",
title = "Evolution of Damage-Immune Programs using Genetic
Programming",
booktitle = "Genetic Algorithms at Stanford 1994",
year = "1994",
editor = "John R. Koza",
pages = "21--30",
address = "Stanford, California, 94305-3079 USA",
month = dec,
organisation = "Stanford University",
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-187263-3",
notes = "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",
}
@InCollection{dickson:1999:EOGASS,
author = "Andrew Dickson",
title = "Evolution of Optimum Genetic Algorithms for Specific
Spaces",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1999",
year = "1999",
editor = "John R. Koza",
pages = "41--48",
address = "Stanford, California, 94305-3079 USA",
month = "15 " # mar,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "part of \cite{koza:1999:GAGPs}",
}
@InProceedings{digby:1999:EAABGC,
author = "David Digby and William Seffens",
title = "Evolutionary Algorithm Analysis of the Biological
Genetic Codes",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1440",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "artificial life, adaptive behavior and agents, poster
papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-013.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-013.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@TechReport{dignum:2004:CSM400,
author = "Stephen Dignum and Riccardo Poli",
title = "Multi-agent Foreign Exchange Market Modelling via
{GP}",
institution = "Department of Computer Science, University of Essex",
year = "2004",
number = "CSM-400",
address = "Colchester, UK",
keywords = "genetic algorithms, genetic programming",
URL = "http://cswww.essex.ac.uk/technical-reports/2004/csm400.pdf",
abstract = "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.",
size = "12 pages",
}
@InProceedings{dignum:mfe:gecco2004,
author = "Stephen Dignum and Riccardo Poli",
title = "Multi-agent Foreign Exchange Market Modelling Via
{GP}",
booktitle = "Genetic and Evolutionary Computation -- GECCO-2004,
Part I",
year = "2004",
editor = "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",
series = "Lecture Notes in Computer Science",
pages = "255--256",
address = "Seattle, WA, USA",
publisher_address = "Heidelberg",
month = "26-30 " # jun,
organisation = "ISGEC",
publisher = "Springer-Verlag",
volume = "3102",
ISBN = "3-540-22344-4",
ISSN = "0302-9743",
URL = "http://link.springer.de/link/service/series/0558/bibs/3102/31020255.htm",
size = "2",
keywords = "genetic algorithms, genetic programming, Poster",
notes = "GECCO-2004 A joint meeting of the thirteenth
international conference on genetic algorithms
(ICGA-2004) and the ninth annual genetic programming
conference (GP-2004)",
}
@InProceedings{1277277,
author = "Stephen Dignum and Riccardo Poli",
title = "Generalisation of the limiting distribution of program
sizes in tree-based genetic programming and analysis of
its effects on bloat",
booktitle = "GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation",
year = "2007",
editor = "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",
volume = "2",
isbn13 = "978-1-59593-697-4",
pages = "1588--1595",
address = "London",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1588.pdf",
doi = "doi:10.1145/1276958.1277277",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
month = "7-11 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, bloat,
crossover Bias, initialisation, program Size
distribution",
abstract = "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.",
notes = "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",
}
@InProceedings{Dignum:2008:eurogp,
title = "Operator Equalisation and Bloat Free {GP}",
author = "Stephen Dignum and Riccardo Poli",
bibdate = "2008-04-15",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#DignumP08",
booktitle = "Proceedings of the 11th European Conference on Genetic
Programming, EuroGP 2008",
address = "Naples",
month = "26-28 " # mar,
publisher = "Springer",
year = "2008",
volume = "4971",
editor = "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",
isbn13 = "978-3-540-78670-2",
pages = "110--121",
series = "Lecture Notes in Computer Science",
doi = "doi:10.1007/978-3-540-78671-9_10",
keywords = "genetic algorithms, genetic programming",
notes = "Also known as \cite{conf/eurogp/DignumP08}
Part of \cite{conf/eurogp/2008} EuroGP'2008 held in
conjunction with EvoCOP2008, EvoBIO2008 and
EvoWorkshops2008",
}
@InProceedings{Dignum:2008:eurogp2,
title = "Crossover, Sampling, Bloat and the Harmful Effects of
Size Limits",
author = "Stephen Dignum and Riccardo Poli",
bibdate = "2008-04-15",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#DignumP08a",
booktitle = "Proceedings of the 11th European Conference on Genetic
Programming, EuroGP 2008",
address = "Naples",
month = "26-28 " # mar,
publisher = "Springer",
year = "2008",
volume = "4971",
editor = "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",
isbn13 = "978-3-540-78670-2",
pages = "158--169",
series = "Lecture Notes in Computer Science",
doi = "doi:10.1007/978-3-540-78671-9_14",
keywords = "genetic algorithms, genetic programming",
notes = "Also known as \cite{conf/eurogp/DignumP08a}
Part of \cite{conf/eurogp/2008} EuroGP'2008 held in
conjunction with EvoCOP2008, EvoBIO2008 and
EvoWorkshops2008",
}
@InProceedings{Dignum:2008:EvoPHD,
author = "Stephen Dignum",
title = "An Analysis of Genetic Programming Operator Bias
regarding the Sampling of Program Size with Potential
Applications",
booktitle = "EvoPhD 2008",
year = "2008",
editor = "Jano {van Hemert} and Mario Giacobini and Cecilia {Di
Chio}",
address = "Naples",
month = "27 " # mar,
keywords = "genetic algorithms, genetic programming",
notes = "EvoPHD'2008 held in conjunction with EuroGP-2008,
EvoCOP2008, EvoBIO2008 and EvoWorkshops2008",
}
@InProceedings{Dignum:2008:PPSN,
author = "Stephen Dignum and Riccardo Poli",
title = "Sub-Tree Swapping Crossover, Allele Diffusion and {GP}
Convergence",
booktitle = "Parallel Problem Solving from Nature - PPSN X",
year = "2008",
editor = "Gunter Rudolph and Thomas Jansen and Simon Lucas and
Carlo Poloni and Nicola Beume",
volume = "5199",
series = "LNCS",
pages = "368--377",
address = "Dortmund",
month = "13-17 " # sep,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, Search,
Crossover Bias, Allele Diffusion, Convergence",
ISBN = "3-540-87699-5",
doi = "doi:10.1007/978-3-540-87700-4_37",
abstract = "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.",
notes = "PPSN X",
}
@InProceedings{Dignum:2010:EuroGP,
author = "Stephen Dignum and Riccardo Poli",
title = "Sub-Tree Swapping Crossover and Arity Histogram
Distributions",
booktitle = "Proceedings of the 13th European Conference on Genetic
Programming, EuroGP 2010",
year = "2010",
editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara
Silva and Stephen Dignum and A. Sima Uyar",
volume = "6021",
series = "LNCS",
pages = "38--49",
address = "Istanbul",
month = "7-9 " # apr,
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-12147-0",
doi = "doi:10.1007/978-3-642-12148-7_4",
abstract = "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.",
notes = "Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010
held in conjunction with EvoCOP2010 EvoBIO2010 and
EvoApplications2010",
}
@InProceedings{dijk:1999:OTDGAGA,
author = "S. van Dijk and D. Thierens and M. de Berg",
title = "On The Design of Genetic Algorithms for Geographical
Applications",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "188--195",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-809.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-809.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{conf/ipcv/DileepC10,
author = "K. V. S. Dileep and Venkatachalam Chandrasekaran",
title = "Learning Data Dependent Composite Kernels for Robust
Image Retrieval - {A} Genetic Programming Approach",
booktitle = "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",
publisher = "CSREA Press",
year = "2010",
editor = "Hamid R. Arabnia and Leonidas Deligiannidis and Gerald
Schaefer and Ashu M. G. Solo",
isbn13 = "1-60132-154-6",
pages = "294--299",
keywords = "genetic algorithms, kernel methods, composite kernel,
learning the kernel, image retrieval",
broken_url = "ftp://amd64gcc.dyndns.org/WORLDCOMP10/2010%20Papers/IPC3842.pdf",
abstract = "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.",
notes = "Despite abstract this is a GA not a GP",
bibdate = "2010-12-08",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/ipcv/ipcv2010.html#DileepC10",
}
@InProceedings{dill:1997:grmGA,
author = "Karen M. Dill and Marek A. Perkowski",
title = "Minimization of {GRM} Forms with a Genetic Algorithm",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "Genetic Algorithms",
pages = "362",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
notes = "GP-97",
}
@InProceedings{Dill:1997:PACRIM,
author = "Karen M. Dill and James H. Herzog and Marek A.
Perkowski",
title = "Genetic programming and its applications to the
synthesis of digital logic",
booktitle = "IEEE Pacific Rim Conference on Communications,
Computers and Signal Processing, PACRIM 1997",
year = "1997",
volume = "2",
pages = "823--826",
address = "Victoria, BC, Canada",
month = "20-22 " # aug,
note = "Networking the Pacific Rim, 10 Years PACRIM
1987-1997",
keywords = "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",
ISBN = "0-7803-3905-3",
doi = "doi:10.1109/PACRIM.1997.620386",
size = "4 pages",
abstract = "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.",
}
@InCollection{dillon:1995:EGASSSTP,
author = "Thomas Dillon",
title = "Evolution of General Algorithmic Solutions for Simple
Sliding Tile Puzzles",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1995",
year = "1995",
editor = "John R. Koza",
pages = "65--75",
address = "Stanford, California, 94305-3079 USA",
month = "11 " # dec,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-195720-5",
notes = "part of \cite{koza:1995:gagp}",
}
@Article{Dimitriu:2009:MMP,
author = "R. C. Dimitriu and H. K. D. H. Bhadeshia and C. Fillon
and C. Poloni",
title = "Strength of Ferritic Steels: Neural Networks and
Genetic Programming",
journal = "Materials and Manufacturing Processes",
year = "2009",
volume = "24",
number = "1",
pages = "10--15",
month = jan,
keywords = "genetic algorithms, genetic programming, ANN, Creep
strength, Ferritic steels, Hot strength, Neural
networks, Steel",
ISSN = "1042-6914",
URL = "http://www.msm.cam.ac.uk/phasetrans/2009/Dimitriu.html",
doi = "doi:10.1080/10426910802539796",
size = "6 pages",
abstract = "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.",
notes = "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",
}
@InProceedings{dimopoulos:1999:ESPGPF,
author = "Christos Dimopoulos and Ali M. S. Zalzala",
title = "Evolving Scheduling Policies through a Genetic
Programming Framework",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1231",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, poster
papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-448.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-448.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{dimopoulos:1999:AGPHOTTP,
author = "Christos Dimopoulos and Ali M. S. Zalzala",
title = "A Genetic Programming Heuristic for the One-Machine
Total Tardiness Problem",
booktitle = "Proceedings of the Congress on Evolutionary
Computation",
year = "1999",
editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc
Schoenauer and Xin Yao and Ali Zalzala",
volume = "3",
pages = "2207--2214",
address = "Mayflower Hotel, Washington D.C., USA",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "6-9 " # jul,
organisation = "Congress on Evolutionary Computation, IEEE / Neural
Networks Council, Evolutionary Programming Society,
Galesia, IEE",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, manufacturing
optimization",
ISBN = "0-7803-5536-9 (softbound)",
ISBN = "0-7803-5537-7 (Microfiche)",
notes = "CEC-99 - A joint meeting of the IEEE, Evolutionary
Programming Society, Galesia, and the IEE.
Library of Congress Number = 99-61143",
}
@InProceedings{chrnei99,
author = "Christos Dimopoulos and Neil Mort",
title = "Genetic programming for cellular manufacturing",
booktitle = "Proceedings of the 2nd Workshop on European Scientific
and Industrial Collaboration (WESIC-99)",
year = "1999",
email = "chris_dimop@hotmail.com",
keywords = "genetic algorithms, genetic programming, cellular
manufacturing",
abstract = "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",
}
@Article{chrams00,
author = "Christos Dimopoulos and A M S Zalzala",
title = "Recent developments in evolutionary computation for
manufacturing optimisation: problems, solutions and
comparisons",
journal = "IEEE Transactions on Evolutionary Computation",
year = "2000",
volume = "4",
number = "2",
pages = "93--113",
email = "chris_dimop@hotmail.com",
keywords = "genetic algorithms, genetic programming, evolutionary
computation, manufacturing optimization",
abstract = "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",
}
@InProceedings{chrnei00,
author = "Christos Dimopoulos and Neil Mort",
title = "Solving cell-formation problems under alternative
quality criteria and constraints with a genetic
programming-based hierarchical clustering algorithm",
booktitle = "Proceedings of the Sixth International Conference on
Control, Automation, Robotics and Vision",
year = "2000",
keywords = "genetic algorithms, genetic programming, cell
formation",
abstract = "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",
}
@InProceedings{dimmort00,
author = "Christos Dimopoulos and Neil Mort",
title = "Evolving similarity coefficients for the solution of
cellular manufacturing problems",
booktitle = "Proceedings of the Congress on Evolutionary
Computation (CEC 2000)",
year = "2000",
pages = "617--624",
address = "La Jolla Marriott Hotel La Jolla, California, USA",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "6-9 " # jul,
organisation = "IEEE Neural Network Council (NNC), Evolutionary
Programming Society (EPS), Institution of Electrical
Engineers (IEE)",
publisher = "IEEE Press",
email = "chris_dimop@hotmail.com",
keywords = "genetic algorithms, genetic programming, cell
formation, similarity coefficients, engineering
applications",
ISBN = "0-7803-6375-2",
abstract = "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",
notes = "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",
}
@InProceedings{dimmortacd,
author = "Christos Dimopoulos and Neil Mort",
title = "A genetic programming-based hierarchical clustering
procedure for the solution of the cell-formation
problem",
booktitle = "Adaptive Computing in Design and Manufacture (ACDM
2000)",
year = "2000",
pages = "211--222",
email = "chris_dimop@hotmail.com",
keywords = "genetic algorithms, genetic programming, cellular
manufacturing",
abstract = "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",
}
@Article{Dimopoulos:2001:AES,
author = "C. Dimopoulos and A. M. S. Zalzala",
title = "Investigating the use of genetic programming for a
classic one-machine scheduling problem",
journal = "Advances in Engineering Software",
volume = "32",
pages = "489--498",
year = "2001",
number = "6",
month = jun,
email = "cop97cd@sheffield.ac.uk",
keywords = "genetic algorithms, genetic programming, Evolutionary
computation, Manufacturing optimisation, Tardiness,
Scheduling",
URL = "http://www.sciencedirect.com/science/article/B6V1P-42YFC02-7/1/6be8f2e3206dccb17801b7a7833a6299",
ISSN = "0965-9978",
doi = "doi:10.1016/S0965-9978(00)00109-5",
abstract = "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.",
}
@Article{chrnei01,
author = "Christos Dimopoulos and Neil Mort",
title = "A hierarchical clustering methodology based on genetic
programming for the solution of simple cell-formation
problems",
journal = "International Journal of Production Research",
year = "2001",
volume = "39",
number = "1",
pages = "1--19",
email = "chris_dimop@hotmail.com",
keywords = "genetic algorithms, genetic programming",
ISSN = "00207543",
doi = "doi:10.1080/00207540150208835",
abstract = "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",
}
@InProceedings{dimopoulos:2005:JCIS,
author = "Christos Dimopoulos",
title = "A Genetic Programming methodology for the solution of
the multi-objective cell-formation problem",
booktitle = "Proceedings of the 8th Joint Conference in Information
Systems (JCIS 2005)",
year = "2005",
editor = "Heng-Da Cheng",
pages = "1487--1494",
address = "Salt Lake City, USA",
month = "21-25 " # jul,
email = "dimopoulos@cycollege.ac.cy",
keywords = "genetic algorithms, genetic programming",
notes = "homepage
sting.cycollege.ac.cy/~dimopoulos/main.htm
http://www.jcis.org/jcis_program/master_schedule.pdf",
}
@InProceedings{dimopoulos:2005:ICPR,
author = "Christos Dimopoulos",
title = "A Novel Approach for the Solution of the
Multiobjective Cell-Formation Problem",
booktitle = "Proceedings of the International Conference of
Production Research (ICPR 05)",
year = "2005",
email = "dimopoulos@cycollege.ac.cy",
keywords = "genetic algorithms, genetic programming, cellular
manufacturing, production research, multiobjective
optimisation",
URL = "http://www.lania.mx/~ccoello/EMOO/dimopoulos05.pdf.gz",
size = "6 pages",
abstract = "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",
notes = "http://icpr18.unisa.it/ Tuesday, August 2 -
16.00/18.00 - Room M Session 45 Cellular
Manufacturing",
}
@TechReport{Ding:2003:XBZRs,
author = "Li-ying Ding and Yu-gang Li and Fang-yu Han",
title = "Combinational Application of Genetic Programming and
Simulated Annealing in Distillation Process Synthesis",
institution = "Qingdao University of Science and Technology",
year = "2003",
type = "Journal of Qingdao Institute of Chemical Technology",
volume = "24",
number = "Supplement",
address = "China",
month = sep,
keywords = "genetic algorithms, genetic programming, SA, simulated
annealing, distillation synthesis,heat integration",
URL = "http://xbzr.qust.edu.cn/WEB2003-zeng/03zk11.htm",
abstract = "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.",
notes = "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",
}
@TechReport{Li-yingDing:2003:XBZRo,
author = "Li-ying Ding and Yu-gang Li and Fang-yu Han",
title = "Design of Complex Distillation Process Based on
Genetic Programming",
institution = "Qingdao University of Science and Technology",
year = "2003",
type = "Journal of Qingdao Institute of Chemical Technology",
volume = "24",
number = "5",
month = oct,
keywords = "genetic algorithms, genetic programming, complex
distillation process",
URL = "http://xbzr.qust.edu.cn/WEB2003-5/ee5-2.HTM",
abstract = "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.",
notes = "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",
}
@Misc{arXiv:quant-ph/0610105,
author = "Shengchao Ding and Zhi Jin and Qing Yang",
title = "Evolving Quantum Oracles with Hybrid Quantum-inspired
Evolutionary Algorithm",
howpublished = "arXiv",
year = "2008",
month = "13 " # oct,
note = "arXiv:quant-ph/0610105 v1",
keywords = "genetic algorithms, genetic programming",
URL = "http://arxiv.org/PS_cache/quant-ph/pdf/0610/0610105v1.pdf",
size = "9 pages",
abstract = "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.",
notes = "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",
}
@InProceedings{eurogp06:DiosanOltean,
author = "Laura Dio\c{s}an and Mihai Oltean",
title = "Evolving crossover operators for function
optimization",
editor = "Pierre Collet and Marco Tomassini and Marc Ebner and
Steven Gustafson and Anik\'o Ek\'art",
booktitle = "Proceedings of the 9th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3905",
year = "2006",
address = "Budapest, Hungary",
month = "10 - 12 " # apr,
organisation = "EvoNet",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-33143-3",
pages = "97--108",
URL = "http://link.springer.de/link/service/series/0558/papers/3905/39050097.pdf",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in
conjunction with EvoCOP2006 and EvoWorkshops2006",
}
@InProceedings{1277332,
author = "Laura Diosan and Mihai Oltean and Alexandrina Rogozan
and Jean Pierre Pecuchet",
title = "Genetically designed multiple-kernels for improving
the {SVM} performance",
booktitle = "GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation",
year = "2007",
editor = "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",
volume = "2",
isbn13 = "978-1-59593-697-4",
pages = "1873--1873",
address = "London",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1873.pdf",
doi = "doi:10.1145/1276958.1277332",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
month = "7-11 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming,
Genetics-Based Machine Learning: Poster, kernel,
Support Vector Machines, SVM",
abstract = "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.",
notes = "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.",
}
@InProceedings{Diosan:2007:ICMLA,
title = "Evolving kernel functions for {SVM}s by genetic
programming",
author = "Laura Diosan and Alexandrina Rogozan and Jean-Pierre
Pecuchet",
booktitle = "Sixth International Conference on Machine Learning and
Applications, ICMLA 2007",
year = "2007",
month = "13-15 " # dec,
pages = "19--24",
address = "Cincinnati, Ohio, USA",
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming, support
vector machines, GP chromosome, SVM kernel functions,
evolved kernel, kernel expression, mathematical
expression, tree encoding",
doi = "doi:10.1109/ICMLA.2007.70",
abstract = "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.",
notes = "also known as \cite{4457202}.
http://www.icmla-conference.org/icmla07/",
}
@InProceedings{conf/evoW/DiosanRP08,
title = "Optimising Multiple Kernels for {SVM} by Genetic
Programming",
author = "Laura Diosan and Alexandrina Rogozan and Jean-Pierre
Pecuchet",
booktitle = "Proceedings of the 8th European Conference,
Evolutionary Computation in Combinatorial Optimization,
Evo{COP}",
bibdate = "2008-04-15",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/evoW/evocop2008.html#DiosanRP08",
publisher = "Springer",
year = "2008",
volume = "4972",
editor = "Jano I. van Hemert and Carlos Cotta",
isbn13 = "978-3-540-78603-0",
pages = "230--241",
series = "Lecture Notes in Computer Science",
doi = "doi:10.1007/978-3-540-78604-7_20",
address = "Naples, Italy",
month = mar # " 26-28",
keywords = "genetic algorithms, genetic programming",
}
@Article{Diosan:2009:GPEM,
author = "Laura Diosan and Mihai Oltean",
title = "Evolutionary design of Evolutionary Algorithms",
journal = "Genetic Programming and Evolvable Machines",
year = "2009",
volume = "10",
number = "3",
pages = "263--306",
month = sep,
keywords = "genetic algorithms, genetic programming, Evolving
evolutionary algorithms, Meta genetic programming,
Function optimization",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-009-9081-6",
abstract = "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.",
}
@Article{Diosan:2010:JAIT,
author = "Laura Diosan and Alexandrina Rogozan and Jean Pierre
Pecuchet",
title = "Learning {SVM} with Complex Multiple Kernels Evolved
by Genetic Programming",
journal = "International Journal on Artificial Intelligence
Tools",
year = "2010",
volume = "19",
number = "5",
pages = "647--677",
keywords = "genetic algorithms, genetic programming, Multiple
kernel learning, hybrid model, SVM",
doi = "doi:10.1142/S0218213010000352",
abstract = "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.",
notes = "IJAIT Laboratoire d'Informatique, de Traitement de
l'Information et des Systemes, EA 4108, Institut
National des Sciences Appliquees, Rouen, France",
}
@Misc{DiPaola:2006:,
author = "Steve DiPaola",
title = "Evolving Portrait Painter Programs using Genetic
Programming to Explore Computer Creativity",
year = "2006?",
keywords = "genetic algorithms, genetic programming, cartesian
genetic programming",
URL = "http://www.units.muohio.edu/codeconference/papers/papers/idmapaper1.pdf",
size = "7 pages",
abstract = "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.",
notes = "iDMAa, Journal of the International Digital Media and
Arts Association, volume 3 published by lulu.com????",
}
@InProceedings{1274009,
author = "Steve R. DiPaola and Liane Gabora",
title = "Incorporating characteristics of human creativity into
an evolutionary art algorithm",
booktitle = "Late breaking paper at Genetic and Evolutionary
Computation Conference {(GECCO'2007)}",
year = "2007",
month = "7-11 " # jul,
editor = "Peter A. N. Bosman",
isbn13 = "978-1-59593-698-1",
pages = "2450--2456",
address = "London, United Kingdom",
keywords = "genetic algorithms, genetic programming, creative
evolutionary systems, evolutionary art, mechanisms of
creativity",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2450.pdf",
doi = "doi:10.1145/1274000.1274009",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Distributed on CD-ROM at GECCO-2007 ACM Order No.
910071",
}
@Article{DiPaola:2009:GPEM,
author = "Steve DiPaola and Liane Gabora",
title = "Incorporating characteristics of human creativity into
an evolutionary art algorithm",
journal = "Genetic Programming and Evolvable Machines",
year = "2009",
volume = "10",
number = "2",
pages = "97--110",
month = jun,
keywords = "genetic algorithms, genetic programming, Creative
evolutionary systems, Mechanisms of creativity,
Cognitive science, Evolutionary art",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-008-9074-x",
abstract = "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.",
}
@InCollection{DiPaola:2011:CGP,
author = "Steve DiPaola and Nathan Sorenson",
title = "{CGP}, Creativity and Art",
booktitle = "Cartesian Genetic Programming",
publisher = "Springer",
editor = "Julian F. Miller",
year = "2011",
series = "Natural Computing Series",
chapter = "10",
pages = "293--307",
keywords = "genetic algorithms, genetic programming, Cartesian
Genetic Programming",
isbn13 = "978-3-642-17309-7",
URL = "http://www.springer.com/computer/theoretical+computer+science/book/978-3-642-17309-7",
doi = "doi:10.1007/978-3-642-17310-3_10",
abstract = "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.",
notes = "part of \cite{Miller:CGP}",
}
@PhdThesis{diplock:thesis,
author = "Gary Diplock",
title = "The application of evolutionary computing techniques
to spatial interaction modelling",
school = "Leeds University, UK",
year = "1996",
month = Sep,
email = "garyd@gmap.leeds.ac.uk",
keywords = "genetic algorithms, genetic programming",
URL = "ftp://gam.leeds.ac.uk/pub/gary/thesis/thesis.zip
broken",
notes = "
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
",
}
@InProceedings{dittrich:1998:lmrrm,
author = "Peter Dittrich and Andreas Burgel and Wolfgang
Banzhaf",
title = "Learning to Move a Robot with Random Morphology",
booktitle = "Proceedings of the First European Workshop on
Evolutionary Robotics",
year = "1998",
editor = "Phil Husbands and Jean-Arcady Meyer",
volume = "1468",
series = "LNCS",
pages = "165--178",
address = "Paris",
publisher_address = "Berlin",
month = "16-17 " # apr,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-64957-3",
URL = "http://www.cs.mun.ca/~banzhaf/papers/evorobot_final.pdf",
size = "14 pages",
notes = "EvoRobot'98 See also \cite{dittrich:1998:rmr}",
}
@Article{dittrich:1998:rmr,
author = "Peter Dittrich and Andreas Burgel and Wolfgang
Banzhaf",
title = "Random Morphology Robot - {A} Test Platform for Online
Evolution",
journal = "Robots and Autonomous Systems",
year = "1998",
note = "To appear",
keywords = "genetic algorithms, genetic programming",
notes = "See also \cite{dittrich:1998:lmrrm}",
}
@InProceedings{dittrich:1999:DPFLGCRMR,
author = "Peter Dittrich and Andre Skusa and Wolfgang Kantschik
and Wolfgang Banzhaf",
title = "Dynamical Properties of the Fitness Landscape of a
{GP} Controlled Random Morphology Robot",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1002--1008",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, evolvable
hardware, evolutionary robotics, on-line evolution,
dynamical fitness landscape, reference fitness",
ISBN = "1-55860-611-4",
URL = "http://citeseer.ist.psu.edu/362288.html",
URL = "http://citeseer.ist.psu.edu/358932.html",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-454.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-454.ps",
abstract = "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.",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@Article{oai:CiteSeerPSU:444392,
author = "Peter Dittrich and Thomas Kron and Christian Kuck and
Wolfgang Banzhaf",
title = "Iterated Mutual Observation with Genetic Programming",
journal = "Sozionik Aktuell",
year = "2001",
volume = "2",
month = jul,
keywords = "genetic algorithms, genetic programming",
citeseer-isreferencedby = "oai:CiteSeerPSU:91154;
oai:CiteSeerPSU:64418",
citeseer-references = "oai:CiteSeerPSU:468369; oai:CiteSeerPSU:354356;
oai:CiteSeerPSU:68864",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:444392",
rights = "unrestricted",
URL = "www.informatik.uni-hamburg.de/TGI/forschung/projekte/sozionik/journal/2/gp.pdf",
URL = "http://citeseer.ist.psu.edu/444392.html",
abstract = "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.",
notes = "http://www.sozionik-aktuell.de/",
}
@Article{Dittrich:2001:AL,
author = "Peter Dittrich and Jens Ziegler and Wolfgang Banzhaf",
title = "Artificial Chemistries -- {A} Review",
journal = "Artificial Life",
year = "2001",
volume = "7",
number = "3",
pages = "225--275",
month = "Summer",
keywords = "complex systems, evolution, self-organisation,
emergence, molecular simulation, origin of life,
chemical computing",
doi = "doi:10.1162/106454601753238636",
abstract = "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.",
}
@Article{Dittrich:2003:JASSS,
author = "Peter Dittrich and Thomas Kron and Wolfgang Banzhaf",
title = "On the Scalability of Social Order",
journal = "Journal of Artificial Societies and Social
Simulation",
year = "2003",
volume = "6",
number = "1",
month = jan,
keywords = "genetic algorithms, genetic programming, Artificial
Chemistry, Coordination, Double Contingency, Learning,
Networks, Self-organization, System Theory",
ISSN = "1460-7425",
URL = "http://jasss.soc.surrey.ac.uk/6/1/3.html",
abstract = "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.",
notes = "Is this GP?",
}
@InProceedings{divina:2001:gecco,
title = "Knowledge Based Evolutionary Programming for Inductive
Learning in First-Order Logic",
author = "Federico Divina and Elena Marchiori",
pages = "173",
year = "2001",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2001)",
editor = "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",
address = "San Francisco, California, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "7-11 " # jul,
keywords = "genetic algorithms, genetic programming: Poster",
ISBN = "1-55860-774-9",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d02.pdf",
notes = "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 \cite{spector:2001:GECCO}",
}
@PhdThesis{Divina:thesis,
author = "Federico Divina",
title = "Hybrid Genetic Relational Search for Inductive
Learning",
year = "2004",
school = "Department of Computer Science, Vrije Universiteit",
address = "Amsterdam, the Netherlands",
keywords = "genetic algorithms, genetic programming",
URL = "https://dare.ubvu.vu.nl/bitstream/1871/10280/1/divina_thesis.pdf",
size = "188 pages",
abstract = "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.",
}
@InProceedings{eurogp:Divina05,
author = "Federico Divina",
editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre
Collet and Jano I. {van Hemert} and Marco Tomassini",
title = "Assessing the Effectiveness of Incorporating Knowledge
in an Evolutionary Concept Learner",
booktitle = "Proceedings of the 8th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3447",
year = "2005",
address = "Lausanne, Switzerland",
month = "30 " # mar # " - 1 " # apr,
organisation = "EvoNet",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-25436-6",
pages = "13--24",
URL = "http://www.cs.vu.nl/~divina/Publications/EuroGP-divina.pdf",
URL = "http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=13",
doi = "doi:10.1007/b107383",
bibsource = "DBLP, http://dblp.uni-trier.de",
size = "12 pages",
abstract = "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.",
notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in
conjunction with EvoCOP2005 and EvoWorkshops2005",
}
@PhdThesis{Djupdal:thesis,
author = "Asbjoern Djupdal",
title = "Evolving Static Hardware Redundancy for Defect
Tolerant {FPGAs}",
school = "Department of Computer and Information Science,
Faculty of Information Technology, Mathematics and
Electrical Engineering, Norwegian University of Science
and Technology",
year = "2008",
address = "Trondheim",
month = "24 " # apr,
isbn13 = "978-82-471-6874-5",
keywords = "genetic algorithms, genetic programming, cartesian
genetic programming, EHW",
URL = "http://www.idi.ntnu.no/research/doctor_theses/djupdal.pdf",
size = "136 pages",
abstract = "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.",
notes = "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",
}
@Article{Djupdal:2011:GPEM,
author = "Asbjoern Djupdal and Pauline Haddow",
title = "The route to a defect tolerant {LUT} through
artificial evolution",
journal = "Genetic Programming and Evolvable Machines",
year = "2011",
volume = "12",
number = "3",
pages = "281--303",
month = sep,
note = "Special Issue Title: Evolvable Hardware Challenges",
keywords = "genetic algorithms, genetic programming, evolvable
hardware",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-011-9129-2",
size = "23 pages",
abstract = "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.",
affiliation = "CRAB Lab, IDI, NTNU, Trondheim, Norway",
}
@Article{Do2008194,
author = "Duong Q. Do and Raymond C. Rowe and Peter York",
title = "Modelling drug dissolution from controlled release
products using genetic programming",
journal = "International Journal of Pharmaceutics",
volume = "351",
number = "1-2",
pages = "194--200",
year = "2008",
ISSN = "0378-5173",
doi = "doi:10.1016/j.ijpharm.2007.09.044",
URL = "http://www.sciencedirect.com/science/article/B6T7W-4PWF0M5-1/2/1931c3725d1a803010a1d39e29117a1",
keywords = "genetic algorithms, genetic programming, Statistical
methods, Modeling, Controlled release, Formulation",
abstract = "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.",
}
@Article{Dobnik-Dubrovski:2002:TRL,
author = "Polona {Dobnik Dubrovski} and Miran Brezocnik",
title = "Using genetic programming to predict the macroporosity
of woven cotton fabrics",
journal = "Textile research journal",
year = "2002",
volume = "72",
number = "3",
pages = "187--194",
month = mar,
email = "mbrezocnik@uni-mb.si",
publisher = "Sage",
keywords = "genetic algorithms, genetic programming, woven cotton
fabrics, macroporosity, modelling",
ISSN = "0040-5175",
URL = "http://cat.inist.fr/?aModele=afficheN&cpsidt=13560450",
doi = "doi:10.1177/004051750207200301",
abstract = "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.",
}
@InProceedings{Doherty2006,
author = "Darren Doherty and Colm O'Riordan",
title = "Evolving Agent--Based Team Tactics for Combative
Computer Games",
booktitle = "Proceedings of the 17th Irish Artificial Intelligence
and Cognitive Science Conference",
pages = "52--61",
year = "2006",
location = "Belfast, Ireland",
editor = "D. A. Bell and P. Milligan and P. P. Sage",
address = "Queen's University, Belfast",
month = "11th-13th " # sep,
keywords = "genetic algorithms, genetic programming, team
evolution",
notes = "http://www.cs.qub.ac.uk/aics06/aics.html",
organisation = "Artificial Intelligence Association of Ireland",
}
@InProceedings{Doherty2006I,
author = "Darren Doherty and Colm O'Riordan",
title = "Evolving Tactical Behaviours for Teams of Agents in
Single Player Action Games",
booktitle = "Proceedings of the 9th International Conference on
Computer Games: AI, Animation, Mobile, Educational \&
Serious Games",
year = "2006",
pages = "121--126",
location = "Dublin, Ireland",
editor = "Qasim Mehdi and Fred Mtenzi and Bryan Duggan and Hugh
McAtamney",
address = "Dublin Institute of Technology",
month = "22nd-24th " # nov,
keywords = "genetic algorithms, genetic programming, team
evolution",
notes = "http://www.comp.dit.ie/cgames/",
organisation = "University of Wolverhampton",
ISBN = "0-9549016-2-2",
}
@InProceedings{1277347,
author = "Darren Doherty and Colm O'Riordan",
title = "A phenotypic analysis of {GP}-evolved team
behaviours",
booktitle = "GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation",
year = "2007",
editor = "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",
volume = "2",
isbn13 = "978-1-59593-697-4",
pages = "1951--1958",
address = "London",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1951.pdf",
doi = "doi:10.1145/1276958.1277347",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
month = "7-11 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, Real-World
Applications, AI, artificial intelligence, cooperative
agents, phenotypic analysis, tactical team behaviour",
abstract = "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.",
notes = "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",
}
@InProceedings{Doherty2007I,
author = "Darren Doherty and Colm O'Riordan",
title = "Evolving Team Behaviours in Environments of Varying
Difficulty",
booktitle = "Proceedings of the 18th Irish Artificial Intelligence
and Cognitive Science Conference",
year = "2007",
pages = "61--70",
location = "Dublin, Ireland",
editor = "Sarah Jane Delany and Michael Madden",
address = "Dublin Institute of Technology",
month = "29th-31st " # aug,
keywords = "genetic algorithms, genetic programming, team
evolution",
notes = "http://www.comp.dit.ie/aics07/program.html",
organisation = "Artificial Intelligence Association of Ireland",
}
@InProceedings{Doherty2008b,
author = "Darren Doherty",
title = "Evolving Tactical Teams for Shooter Games using
Genetic Programming",
booktitle = "Proceedings of the 3rd European Graduate Student
Workshop on Evolutionary Computation",
year = "2008",
pages = "29--42",
location = "Naples, Italy",
editor = "Jano {Van Hemert} and Mario Giacobini and Cecilia {Di
Chio}",
address = "University of Naples Federico II",
month = "27 " # mar,
keywords = "genetic algorithms, genetic programming, team
evolution",
organisation = "Evostar",
notes = "EvoPHD'2008 held in conjunction with EuroGP-2008,
EvoCOP2008, EvoBIO2008 and EvoWorkshops2008",
}
@Article{Doherty:2009:ieeeTCIAIG,
author = "Darren Doherty and Colm O'Riordan",
title = "Effects of Shared Perception on the Evolution of Squad
Behaviors",
journal = "IEEE Transactions on Computational Intelligence and AI
in Games",
year = "2009",
month = mar,
volume = "1",
number = "1",
pages = "50--62",
keywords = "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",
doi = "doi:10.1109/TCIAIG.2009.2018701",
ISSN = "1943-068X",
abstract = "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.",
notes = "Also known as \cite{4804730}",
}
@InCollection{doherty:2003:FAUGPCRI,
author = "C. Gregory Doherty",
title = "Fundamental Analysis Using Genetic Programming for
Classification Rule Induction",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2003",
year = "2003",
editor = "John R. Koza",
pages = "45--51",
address = "Stanford, California, 94305-3079 USA",
month = "4 " # dec,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.genetic-programming.org/sp2003/Doherty.pdf",
notes = "part of \cite{koza:2003:gagp}",
}
@InProceedings{dolado:1998:GPNNlrspe,
author = "J. J. Dolado and L. Fernandez",
title = "Genetic Programming, Neural Networks and Linear
Regression in Software Project Estimation",
booktitle = "International Conference on Software Process
Improvement, Research, Education and Training",
year = "1998",
editor = "C. Hawkins and M. Ross and G. Staples and J. B.
Thompson",
pages = "157--171",
address = "London",
month = "10-11 " # sep,
publisher = "British Computer Society",
keywords = "genetic algorithms, genetic programming, neural
networks, linear regression, SBSE",
ISBN = "1-902505-03-4",
URL = "http://www.sc.ehu.es/jiwdocoj/docs/inspir98.pdf",
size = "1 Mb",
notes = "INSPIRE 98
http://www2.unl.ac.uk/~11georgiadou/inspire98/",
}
@InProceedings{dolado:1999:lmsce,
author = "J. Javier Dolado",
title = "Limits to the Methods in Software Cost Estimation",
booktitle = "Proceedings of the 1st International Workshop on Soft
Computing Applied to Software Engineering",
year = "1999",
editor = "Conor Ryan and Jim Buckley",
pages = "63--68",
address = "University of Limerick, Ireland",
month = "12-14 " # apr,
organisation = "SCARE",
publisher = "Limerick University Press",
keywords = "genetic algorithms, genetic programming, SBSE",
ISBN = "1-874653-52-6",
URL = "http://www.sc.ehu.es/jiwdocoj/docs/dolado-scase99.ps",
URL = "http://citeseer.ist.psu.edu/271064.html",
size = "330 Kb",
abstract = "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.",
notes = "http://scare.csis.ul.ie/scase99/ SCASE'99",
}
@InProceedings{dolado:1999:ICEIS,
author = "J. Javier Dolado and Luis Fernandez and M. Carmen
Otero and Leire Urkola",
title = "Software Effort Estimation: the Elusive Goal in
Project Management",
booktitle = "International Conference on Enterprise Information
Systems 1999",
year = "1999",
pages = "412--418",
keywords = "genetic algorithms, genetic programming",
ISBN = "972-98050-0-8",
URL = "http://www.sc.ehu.es/jiwdocoj/docs/dofeotur.ps",
abstract = "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.",
notes = "http://www.iceis.org/iceis2003/abstracts_1999.htm",
}
@Article{Dolado:2000:vcmsse,
author = "Jose Javier Dolado",
title = "A validation of the component-based method for
software size estimation",
journal = "IEEE Transactions on Software Engineering",
year = "2000",
volume = "26",
number = "10",
pages = "1006--1021",
month = oct,
keywords = "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",
ISSN = "0098-5589",
URL = "http://ieeexplore.ieee.org/iel5/32/19037/00879821.pdf",
size = "16 pages",
abstract = "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.",
notes = "data files http://www.sc.ehu.es/jiwdocoj/cbm.htm",
}
@Article{Dolado:2001:SCF,
author = "Jose J. Dolado",
title = "On the Problem of the Software Cost Function",
journal = "Information and Software Technology",
year = "2001",
volume = "43",
number = "1",
pages = "61--72",
month = "1 " # jan,
keywords = "genetic algorithms, genetic programming, SBSE,
software cost function, Cost estimation, Empirical
research",
ISSN = "0950-5849",
URL = "http://www.elsevier.com/locate/issn/09505849",
doi = "doi:10.1016/S0950-5849(00)00137-3",
URL = "http://www.sciencedirect.com/science/article/B6V0B-41NK8BD-5/2/6d97db872ced4148a359673dc3b060c6",
size = "12 pages",
abstract = "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.",
}
@InCollection{dolin:2000:CPCESIITDLC,
author = "Brad Dolin",
title = "Co-Evolution of Populations of Chasers and Evaders
that use Sonic Intensity and Interaural Time Difference
as Localization Cues",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2000",
year = "2000",
editor = "John R. Koza",
pages = "117--124",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "part of \cite{koza:2000:gagp}",
}
@InProceedings{dolin:2001:eh,
author = "Brad Dolin and Forrest H {Bennett III} and Eleanor G.
Rieffel",
title = "Methods for evolving robust distributed robot control
software: coevolutionary and single population
techniques",
booktitle = "The Third NASA/DoD workshop on Evolvable Hardware",
year = "2001",
editor = "Didier Keymeulen and Adrian Stoica and Jason Lohn and
Ricardo S. Zebulum",
pages = "21--29",
address = "Long Beach, California",
publisher_address = "1730 Massachusetts Avenue, N.W., Washington, DC,
20036-1992, USA",
month = "12-14 " # jul,
organisation = "Jet Propulsion Laboratory, California Institute of
Technology",
publisher = "IEEE Computer Society",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7695-1180-5",
notes = "EH2001 http://cism.jpl.nasa.gov/ehw/events/nasaeh01/
Note misspeling of Brad Dolin as {"}Dofin, B.{"}.",
}
@InProceedings{DBLP:conf/sac/DolinBR02,
author = "Brad Dolin and Forrest H. Bennett III and Eleanor G.
Rieffel",
title = "Co-evolving an effective fitness sample: experiments
in symbolic regression and distributed robot control",
booktitle = "Proceedings of the 2002 ACM Symposium on Applied
Computing (SAC)",
year = "2002",
pages = "553--559",
address = "Madrid, Spain",
month = mar # " 10-14",
publisher = "ACM",
bibsource = "DBLP, http://dblp.uni-trier.de",
keywords = "genetic algorithms, genetic programming, co-evolution,
fitness cases, symbolic regression, robot control,
distributed control",
ISBN = "1-58113-445-2",
doi = "doi:10.1145/508791.508899",
abstract = "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.",
}
@Article{dolin:2002:GPEM,
author = "Brad Dolin and J. J. Merelo",
title = "Resource Review: {A} Web-Based Tour of Genetic
Programming",
journal = "Genetic Programming and Evolvable Machines",
year = "2002",
volume = "3",
number = "3",
pages = "311--313",
month = sep,
keywords = "genetic algorithms, genetic programming",
ISSN = "1389-2576",
URL = "http://www.cs.bgu.ac.il/~sipper/courses/papers/GPweb.pdf",
doi = "doi:10.1023/A:1020167426088",
size = "3 pages",
abstract = "Summary of some introductions to GP, tutorials and
demos, implementations and useful links for GP
research",
notes = "Article ID: 5091793",
}
@InProceedings{dolin:ppsn2002:pp142,
author = "Brad Dolin and Maribel Garcia Arenas and Juan J.
Merelo Guervos",
title = "Opposites Attract: Complementary Phenotype Selection
for Crossover in Genetic Programming",
booktitle = "Parallel Problem Solving from Nature - PPSN VII",
address = "Granada, Spain",
month = "7-11 " # sep,
pages = "142--152",
year = "2002",
editor = "Juan J. Merelo-Guervos and Panagiotis Adamidis and
Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and
Hans-Paul Schwefel",
number = "2439",
series = "Lecture Notes in Computer Science, LNCS",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, Evolutionary
computing, Selection",
ISBN = "3-540-44139-5",
annote = "Available from
http://link.springer.de/link/service/series/0558/papers/2439/243900142.pdf",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2439&spage=142",
}
@InProceedings{Dolinsky:1998:ukmr,
author = "J.-U. Dolinsky and G. J. Colquhoun and I. D.
Jenkinson",
title = "A comparison of techniques for modelling robot
dynamics",
booktitle = "Proceedings of the 14th national conference on
manufacturing research",
year = "1998",
address = "University of Derby, UK",
keywords = "ANN",
notes = "copy in \cite{Dolinsky:thesis}",
}
@InProceedings{Dolinsky:2000:MATADOR,
author = "J.-U. Dolinsky and G. J. Colquhoun and I. D.
Jenkinson",
title = "Structural identification and calibration of kinematic
robot models by genetic search",
booktitle = "Proceedings of the 33rd international MATADOR
conference",
year = "2000",
address = "University of Manchester, Institute for Science and
Technology (UMIST), UK",
keywords = "genetic algorithms, genetic programming",
notes = "copy in \cite{Dolinsky:thesis}",
}
@PhdThesis{Dolinsky:thesis,
author = "Jens-Uwe Dolinsky",
title = "The Development Of {A} Genetic Programming Method For
Kinematic Robot Calibration",
school = "Liverpool John Moores University",
year = "2001",
address = "UK",
month = mar,
keywords = "genetic algorithms, genetic programming, coevolution,
stochastic inference, robotrak",
URL = "http://www.mb.hs-wismar.de/cea/phd/dolinsky_thesis.pdf",
URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.70.7361",
size = "183 pages",
abstract = "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.",
notes = "http://www.ljmu.ac.uk/GERI/80097.htm",
}
@Article{Dolinsky:2007:CI,
author = "J. U. Dolinsky and I. D. Jenkinson and G. J.
Colquhoun",
title = "Application of genetic programming to the calibration
of industrial robots",
journal = "Computers in Industry",
year = "2007",
volume = "58",
number = "3",
pages = "255--264",
month = apr,
publisher = "Elsevier Science Publishers B. V.",
publisher_address = "Amsterdam, The Netherlands",
keywords = "genetic algorithms, genetic programming, Inverse
static kinematic calibration, Distal supervised
learning, Co-evolution",
ISSN = "0166-3615",
doi = "doi:10.1016/j.compind.2006.06.003",
abstract = "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.",
notes = "Codeplay Ltd., Edinburgh, UK
School of Engineering, Liverpool John Moores
University, Byrom Street, Liverpool L3 3AF, UK",
}
@MastersThesis{domingos:thesis,
author = "Roberto Pinheiro Domingos",
title = "Non-Linear Nuclear Engineering Models as an
Application of Genetic Programming",
school = "Universidade Federal Rio de Janeiro",
year = "1997",
month = mar,
keywords = "genetic algorithms, genetic programming",
size = "pages",
notes = "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.",
}
@PhdThesis{domingos:phdthesis,
author = "Roberto Pinheiro Domingos",
title = "Evolutionary Neuro-Fuzzy Models Applied to Nuclear
Engineering Process Identification and Control",
school = "COPPE, Universidade Federal Rio de Janeiro",
year = "2003",
address = "Rua Vilela Tavares 253 apto 801 Lins - Rio de Janeiro
-RJ-BRASIL",
month = jun,
email = "roberto.domingos@terra.com.br",
keywords = "genetic algorithms, genetic programming, additive
neurofuzzy",
size = "161 pages",
abstract = "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.",
notes = "neurofuzzy genetic programming
Email Mon, 07 Jul 2003 20:25:57 -0300 confirms this as
PhD thesis.",
}
@Article{domingos:2003:ASC,
author = "Roberto P. Domingos and Gustavo H. F. Caldas and
Claudio M. N. A. Pereira and Roberto Schirru",
title = "{PWR's} Xenon oscillation control through a fuzzy
expert system automatically designed by means of
genetic programming",
journal = "Applied Soft Computing",
year = "2003",
volume = "3",
number = "4",
pages = "317--323",
month = dec,
keywords = "genetic algorithms, genetic programming, Axial xenon
oscillations control; Fuzzy logic",
URL = "http://www.sciencedirect.com/science/article/B6W86-49MX1MH-1/2/50727e0c9a470ae05a1e62675e4555d7",
doi = "doi:10.1016/j.asoc.2003.05.002",
abstract = "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.",
}
@Article{Domingos:2005:PNE,
author = "Roberto P. Domingos and Roberto Schirru and Aquilino
Senra Martinez",
title = "Soft computing systems applied to {PWR}'s xenon",
journal = "Progress in Nuclear Energy",
year = "2005",
volume = "46",
number = "3-4",
pages = "297--308",
keywords = "genetic algorithms, genetic programming, evolutionary
computation, control, xenon oscillation",
doi = "doi:10.1016/j.pnucene.2005.03.011",
abstract = "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.",
}
@InCollection{donald:1995:AEACFI,
author = "Keith Mac Donald",
title = "An Evolutionary Approach to {CPU} Fault Isolation",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1995",
year = "1995",
editor = "John R. Koza",
pages = "199--208",
address = "Stanford, California, 94305-3079 USA",
month = "11 " # dec,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-195720-5",
notes = "part of \cite{koza:1995:gagp}",
}
@Article{Donarski2010987,
author = "James A. Donarski and Stephen A. Jones and Mark
Harrison and Malcolm Driffield and Adrian J. Charlton",
title = "Identification of botanical biomarkers found in
Corsican honey",
journal = "Food Chemistry",
volume = "118",
number = "4",
pages = "987--994",
year = "2010",
month = "15 " # feb,
note = "Food Authenticity \& Traceability, Edited by Simon
Kelly, Claude Guillou and Paul Brereton",
ISSN = "0308-8146",
doi = "doi:10.1016/j.foodchem.2008.10.033",
URL = "http://www.sciencedirect.com/science/article/B6T6R-4TRK0VB-1/2/c32107c8f3b0b36745ea2bd369053d04",
keywords = "genetic algorithms, genetic programming, NMR
spectroscopy, Honey, Kynurenic acid, Chestnut,
Geographical origin, Botanical origin",
abstract = "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.",
}
@Article{Dong:1997:EPSR,
author = "Gyu Lee Dong and Whi Lee Byong and Heung Chang Soon",
title = "Genetic programming model for long-term forecasting of
electric power demand",
journal = "Electric Power Systems Research",
year = "1997",
volume = "40",
pages = "17--22",
number = "1",
abstract = "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.",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6V30-3WDCJBW-3/2/c71881481512566c7b47d81606334180",
keywords = "genetic algorithms, genetic programming, Forecasting,
Electric demand",
doi = "doi:10.1016/S0378-7796(96)01125-X",
}
@InProceedings{Dong:2009:MASS,
author = "Hong-Bin Dong and Jia Chen",
title = "Improved Genetic Programming Based on Lineage
Information",
booktitle = "International Conference on Management and Service
Science, MASS '09",
year = "2009",
month = sep,
address = "Wuhan, China",
pages = "1--5",
keywords = "genetic algorithms, genetic programming, chromosome,
effective search method, lineage information",
doi = "doi:10.1109/ICMSS.2009.5304998",
abstract = "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.",
notes = "Also known as \cite{5304998}",
}
@InProceedings{Dorado:2002:EvoWorkshops,
author = "Julian Dorado and Juan R. Rabu$\tilde{n}$al and
Jer\'onimo Puertas and Antonino Santos and Daniel
Rivero",
title = "Prediction and Modelling of the Flow of a Typical
Urban Basin through Genetic Programming",
booktitle = "Applications of Evolutionary Computing, Proceedings of
EvoWorkshops2002: EvoCOP, EvoIASP, EvoSTim/EvoPLAN",
year = "2002",
editor = "Stefano Cagnoni and Jens Gottlieb and Emma Hart and
Martin Middendorf and G{"}unther Raidl",
volume = "2279",
series = "LNCS",
pages = "190--201",
address = "Kinsale, Ireland",
publisher_address = "Berlin",
month = "3-4 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, evolutionary
computation, applications, hydrology, rain-fall
run-off, sewage, flooding alarm, transference function,
hydraulic enginnering, kinematic wave, unit hydographs,
STGP",
ISBN = "3-540-43432-1",
URL = "http://link.springer-ny.com/link/service/series/0558/bibs/2279/22790190.htm",
URL = "http://link.springer-ny.com/link/service/series/0558/papers/2279/22790190.pdf",
size = "12 pages",
abstract = "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.",
notes = "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.",
}
@InProceedings{dorado:2002:IJCNN,
author = "Julian Dorado and Juan R. Rabunal and Daniel Rivero
and Antonino Santos and Alejandro Pazos",
title = "Automatic Recurrent {ANN} Rule Extraction with Genetic
Programming",
booktitle = "Proceedings of the 2002 International Joint Conference
on Neural Networks IJCNN'02",
pages = "1552--1557",
year = "2002",
month = "12-17 " # may,
address = "Hilton Hawaiian Village Hotel, Honolulu, Hawaii",
publisher = "IEEE Press",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
organisation = "IEEE",
ISBN = "0-7803-7278-6",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "IJCNN 2002 Held in connection with the World Congress
on Computational Intelligence (WCCI 2002)",
}
@InProceedings{dorado:ppsn2002:pp485,
author = "Julian Dorado and Juan R. Rabunal and Antonino Santos
and Alejandro Pazos and Daniel Rivero",
title = "Automatic Recurrent {ANN} Rule Extraction with Genetic
Programming",
booktitle = "Parallel Problem Solving from Nature - PPSN VII",
address = "Granada, Spain",
month = "7-11 " # sep,
pages = "485--494",
year = "2002",
editor = "Juan J. Merelo-Guervos and Panagiotis Adamidis and
Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and
Hans-Paul Schwefel",
number = "2439",
series = "Lecture Notes in Computer Science, LNCS",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, Neural
Networks",
ISBN = "3-540-44139-5",
annote = "Available from
http://link.springer.de/link/service/series/0558/papers/2439/243900485.pdf",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2439&spage=485",
}
@Book{dorigo.97,
author = "Marco Dorigo and Marco Colombetti",
title = "Robot Shaping: An Experiment in Behavior Engineering",
publisher = "MIT Press/Bradford Books",
year = "1997",
notes = "
",
}
@TechReport{dorin:1994:GPr,
author = "Alan Dorin",
title = "Koza, {J}. ``Genetic Programming'' (review)",
institution = "School of Computer Science and Software Engineering,
Monash University",
address = "Clayton, Australia 3168",
year = "1994",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.monash.edu.au/~aland/reviews/koza.rev.html",
notes = "www only",
size = "0.5 pages",
}
@Article{dosi:1999:nepal:er,
author = "Giovanni Dosi and Luigi Marengo and Andrea Bassanini
and Marco Valente",
title = "Norms as emergent properties of adaptive learning: The
case of economic routines",
journal = "Journal of Evolutionary Economics",
year = "1999",
volume = "9",
number = "1",
pages = "5--26",
keywords = "genetic algorithms, genetic programming,
computability, oligopoly",
ISSN = "0936-9937",
doi = "doi:10.1007/s001910050073",
abstract = "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.",
}
@InProceedings{dosSantos:2008:SIBGRAPI,
author = "Jefersson Alex {dos Santos} and Cristiano Dalmaschio
Ferreira and Ricardo {da Silva Torres}",
title = "A Genetic Programming Approach for Relevance Feedback
in Region-Based Image Retrieval Systems",
booktitle = "XXI Brazilian Symposium on Computer Graphics and Image
Processing, SIBGRAPI '08",
year = "2008",
month = oct,
pages = "155--162",
keywords = "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",
doi = "doi:10.1109/SIBGRAPI.2008.15",
ISSN = "1530-1834",
abstract = "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.",
notes = "Also known as \cite{4654155}",
}
@Article{Santos2010,
author = "J. A. {dos Santos} and C. D. Ferreira and R. {da S.
Torres} and M. A. Goncalves and R. A. C. Lamparelli",
title = "A Relevance Feedback Method based on Genetic
Programming for Classification of Remote Sensing
Images",
journal = "Information Sciences",
year = "2011",
volume = "181",
number = "12",
pages = "2671--2684",
month = "1 " # jul,
ISSN = "0020-0255",
doi = "doi:10.1016/j.ins.2010.02.003",
URL = "http://www.sciencedirect.com/science/article/B6V0C-4YBMF9K-2/2/7be908a0802e1675ad8e8258bfbc4e01",
keywords = "genetic algorithms, genetic programming, content-based
image retrieval, region descriptors, relevance
feedback, remote sensing image classification",
size = "14 pages",
abstract = "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.",
}
@InProceedings{coelho:1998:xcsf,
author = "Leandro {dos Santos Coelho} and Antonio Augusto
Rodrigues Coelho",
title = "An Experimental and Comparative Study of Fuzzy {PID}
Controller Structures",
booktitle = "Advances in Soft Computing - Engineering Design and
Manufacturing",
year = "1998",
editor = "R. Roy and T. Furuhashi and P. K. Chawdhry",
month = "21-30 " # jun,
keywords = "Fuzzy logic control, Fuzzy PID Control, Experimental
process, Control applications.",
ISBN = "1-85233-062-7",
broken_url = "https://www.cranfield.ac.uk/wsc3/tech-sessions/papers/ic-2/ic-2.htm",
abstract = "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.",
notes = "WSC3",
}
@Article{Coelho20091434,
author = "Leandro {dos Santos Coelho} and Marcelo Wicthoff
Pessoa",
title = "Nonlinear model identification of an experimental
ball-and-tube system using a genetic programming
approach",
journal = "Mechanical Systems and Signal Processing",
volume = "23",
number = "5",
pages = "1434--1446",
year = "2009",
ISSN = "0888-3270",
doi = "doi:10.1016/j.ymssp.2009.02.005",
URL = "http://www.sciencedirect.com/science/article/B6WN1-4VNH3WJ-1/2/f2de8e8814271f4e5d58e4cee49bd291",
keywords = "genetic algorithms, genetic programming, System
identification, Nonlinear models, Evolutionary
algorithm",
abstract = "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.",
}
@InProceedings{conf/eurogp/DoucetteH08,
title = "{GP} Classification under Imbalanced Data sets: Active
Sub-sampling and {AUC} Approximation",
author = "John Doucette and Malcolm I. Heywood",
bibdate = "2008-04-15",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#DoucetteH08",
booktitle = "Proceedings of the 11th European Conference on Genetic
Programming, EuroGP 2008",
address = "Naples",
month = "26-28 " # mar,
publisher = "Springer",
year = "2008",
volume = "4971",
editor = "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",
isbn13 = "978-3-540-78670-2",
pages = "266--277",
series = "Lecture Notes in Computer Science",
doi = "doi:10.1007/978-3-540-78671-9_23",
keywords = "genetic algorithms, genetic programming",
notes = "Part of \cite{conf/eurogp/2008} EuroGP'2008 held in
conjunction with EvoCOP2008, EvoBIO2008 and
EvoWorkshops2008",
}
@InProceedings{DBLP:conf/gecco/DoucetteLH09,
author = "John Doucette and Peter Lichodzijewski and Malcolm I.
Heywood",
title = "Benchmarking coevolutionary teaming under
classification problems with large attribute spaces",
booktitle = "GECCO '09: Proceedings of the 11th Annual conference
on Genetic and evolutionary computation",
year = "2009",
editor = "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",
pages = "1901--1902",
address = "Montreal",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "8-12 " # jul,
organisation = "SigEvo",
keywords = "genetic algorithms, genetic programming, Poster",
isbn13 = "978-1-60558-325-9",
bibsource = "DBLP, http://dblp.uni-trier.de",
doi = "doi:10.1145/1569901.1570226",
abstract = "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.",
notes = "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.",
}
@InCollection{Doucette:2009:GPTP,
author = "John Doucette and Peter Lichodzijewski and Malcolm
Heywood",
title = "Evolving Coevolutionary Classifiers under Large
Attribute Spaces",
booktitle = "Genetic Programming Theory and Practice {VII}",
year = "2009",
editor = "Rick L. Riolo and Una-May O'Reilly and Trent
McConaghy",
series = "Genetic and Evolutionary Computation",
address = "Ann Arbor",
month = "14-16 " # may,
publisher = "Springer",
chapter = "3",
pages = "37--54",
keywords = "genetic algorithms, genetic programming, Problem
Decomposition, Bid-based Cooperative Behaviors,
Symbiotic Coevolution, Subspace Classifier, Large
Attribute Spaces",
notes = "part of \cite{Riolo:2009:GPTP}",
}
@InProceedings{Doucette:2010:EuroGP,
author = "John Doucette and Malcolm Heywood",
title = "Novelty-based Fitness: An Evaluation under the Santa
Fe Trail",
booktitle = "Proceedings of the 13th European Conference on Genetic
Programming, EuroGP 2010",
year = "2010",
editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara
Silva and Stephen Dignum and A. Sima Uyar",
volume = "6021",
series = "LNCS",
pages = "50--61",
address = "Istanbul",
month = "7-9 " # apr,
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-12147-0",
doi = "doi:10.1007/978-3-642-12148-7_5",
abstract = "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.",
notes = "Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010
held in conjunction with EvoCOP2010 EvoBIO2010 and
EvoApplications2010",
}
@InProceedings{Doucette:2011:RtAhtAeoEEPSuaEGST,
title = "Revisiting the Acrobot `height' task: An example of
Efficient Evolutionary Policy Search under an Episodic
Goal Seeking Task",
author = "John Doucette and Malcolm Heywood",
pages = "468--475",
booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary
Computation",
year = "2011",
editor = "Alice E. Smith",
month = "5-8 " # jun,
address = "New Orleans, USA",
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming, Adaptive
dynamic programming and reinforcement learning,
Coevolution and collective behaviour",
abstract = "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.",
notes = "CEC2011 sponsored by the IEEE Computational
Intelligence Society, and previously sponsored by the
EPS and the IET.",
}
@Article{Doucette:2012:GPEM,
author = "John A. Doucette and Andrew R. McIntyre and Peter
Lichodzijewski and Malcolm I. Heywood",
title = "Symbiotic coevolutionary genetic programming: a
benchmarking study under large attribute spaces",
journal = "Genetic Programming and Evolvable Machines",
year = "2012",
volume = "13",
number = "1",
pages = "71--101",
month = mar,
note = "Special Section on Evolutionary Algorithms for Data
Mining",
keywords = "genetic algorithms, genetic programming, Feature
subspace selection, Problem decomposition, Symbiosis,
Coevolution, Model complexity, Classification",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-011-9151-4",
size = "31 pages",
abstract = "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.",
affiliation = "David R. Cheriton School of Computer Science,
University of Waterloo, Waterloo, ON, Canada",
}
@InProceedings{oai:CiteSeerPSU:501552,
author = "George Dounias and Hubertus Axer and Beth Bjerregaard
and Diedrich {Graf von Keyserlingk} and Jan Jantzen and
Athanasios Tsakonas",
title = "Genetic Programming for the Generation of Crisp and
Fuzzy Rule Bases in Classification and Diagnosis of
Medical Data",
booktitle = "First International NAISO Congress on Neuro Fuzzy
Technologies",
year = "2002",
address = "Havana, Cuba",
month = "16-19 " # jan,
organisation = "NAISO (Natural and Artificial Intelligence Systems
Organization)",
keywords = "genetic algorithms, genetic programming",
citeseer-isreferencedby = "oai:CiteSeerPSU:147332;
oai:CiteSeerPSU:120858; oai:CiteSeerPSU:78616;
oai:CiteSeerPSU:473489; oai:CiteSeerPSU:104720;
oai:CiteSeerPSU:161453",
citeseer-references = "oai:CiteSeerPSU:172707; oai:CiteSeerPSU:345471;
oai:CiteSeerPSU:259217",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:501552",
rights = "unrestricted",
URL = "http://www2.ba.aegean.gr/members/tsakonas/DTJABK_Cuba2002.pdf",
URL = "http://citeseer.ist.psu.edu/501552.html",
abstract = "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.",
notes = "http://www.icsc.ab.ca/conferences/nf2002/",
}
@Article{doValleSimoes_2007_GPEM,
author = "Eduardo {do Valle Simoes}",
title = "Evolvable hardware, Springer, Genetic and Evolutionary
Computation Series, edited by Tetsuya Higuchi, Yong Liu
and Xin Yao, 224 pp, {ISBN} 0-387-24386-0",
journal = "Genetic Programming and Evolvable Machines",
year = "2007",
volume = "8",
number = "3",
pages = "287--288",
month = sep,
note = "Book review",
keywords = "genetic algorithms, genetic programming, evolvable
hardware",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-007-9032-z",
size = "2 pages",
}
@InProceedings{Dower:2011:GECCO,
author = "Steve Dower and Clinton J. Woodward",
title = "{ESDL}: a simple description language for
population-based evolutionary computation",
booktitle = "GECCO '11: Proceedings of the 13th annual conference
on Genetic and evolutionary computation",
year = "2011",
editor = "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",
isbn13 = "978-1-4503-0557-0",
pages = "1045--1052",
keywords = "genetic algorithms, genetic programming",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001576.2001718",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{2001718} GECCO-2011 A joint
meeting of the twentieth international conference on
genetic algorithms (ICGA-2011) and the sixteenth annual
genetic programming conference (GP-2011)",
}
@InProceedings{Downey:2009:IVCNZ,
title = "Multiclass object classification for computer vision
using Linear Genetic Programming",
author = "Carlton Downey and Mengjie Zhang",
year = "2009",
pages = "73--78",
booktitle = "Proceeding of the 24th International Conference Image
and Vision Computing New Zealand, IVCNZ '09",
month = "23-25 " # nov,
address = "Wellington",
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-4697-1",
ISSN = "2151-2205",
doi = "doi:10.1109/IVCNZ.2009.5378356",
abstract = "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.",
notes = "Also known as \cite{5378356}",
}
@InProceedings{Downey:2010:gecco,
author = "Carlton Downey and Mengjie Zhang and Will N. Browne",
title = "New crossover operators in linear genetic programming
for multiclass object classification",
booktitle = "GECCO '10: Proceedings of the 12th annual conference
on Genetic and evolutionary computation",
year = "2010",
editor = "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",
isbn13 = "978-1-4503-0072-8",
pages = "885--892",
keywords = "genetic algorithms, genetic programming",
month = "7-11 " # jul,
organisation = "SIGEVO",
address = "Portland, Oregon, USA",
doi = "doi:10.1145/1830483.1830644",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{1830644} GECCO-2010 A joint
meeting of the nineteenth international conference on
genetic algorithms (ICGA-2010) and the fifteenth annual
genetic programming conference (GP-2010)",
}
@InProceedings{downey:2011:EuroGP,
author = "Carlton Downey and Mengjie Zhang",
title = "Parallel Linear Genetic Programming",
booktitle = "Proceedings of the 14th European Conference on Genetic
Programming, EuroGP 2011",
year = "2011",
month = "27-29 " # apr,
editor = "Sara Silva and James A. Foster and Miguel Nicolau and
Mario Giacobini and Penousal Machado",
series = "LNCS",
volume = "6621",
publisher = "Springer Verlag",
address = "Turin, Italy",
pages = "178--189",
organisation = "EvoStar",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-20406-7",
doi = "doi:10.1007/978-3-642-20407-4_16",
abstract = "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.",
notes = "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 \cite{Silva:2011:GP} EuroGP'2011 held in
conjunction with EvoCOP2011 EvoBIO2011 and
EvoApplications2011",
}
@InProceedings{Downey:2011:ETCfLGP,
title = "Execution Trace Caching for Linear Genetic
Programming",
author = "Carlton Downey and Mengjie Zhang",
pages = "1191--1198",
booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary
Computation",
year = "2011",
editor = "Alice E. Smith",
month = "5-8 " # jun,
address = "New Orleans, USA",
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming",
abstract = "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",
notes = "CEC2011 sponsored by the IEEE Computational
Intelligence Society, and previously sponsored by the
EPS and the IET.",
}
@InProceedings{Downey:2011:GECCOcomp,
author = "Carlton Downey and Mengjie Zhang",
title = "Caching for parallel linear genetic programming",
booktitle = "GECCO '11: Proceedings of the 13th annual conference
companion on Genetic and evolutionary computation",
year = "2011",
editor = "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",
isbn13 = "978-1-4503-0690-4",
keywords = "genetic algorithms, genetic programming: Poster",
pages = "201--202",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001858.2001970",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{2001970} Distributed on CD-ROM at
GECCO-2011.
ACM Order Number 910112.",
}
@Article{Downey:2012:GPEM,
author = "Carlton Downey and Mengjie Zhang and Jing Liu",
title = "Parallel linear genetic programming for multi-class
classification",
journal = "Genetic Programming and Evolvable Machines",
note = "Online first",
keywords = "genetic algorithms, genetic programming, Linear
genetic programming, Classification, Parallel
structure, Caching",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-012-9162-9",
size = "30 pages",
abstract = "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.",
notes = "Jing Liu = http://see.xidian.edu.cn/faculty/liujing/",
affiliation = "School of Engineering and Computer Science, Victoria
University of Wellington, Wellington, New Zealand",
}
@InProceedings{downing:1998:GPGAes,
author = "Keith Downing",
title = "Combining Genetic Programming and Genetic Algorithms
for Ecological Simulation",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "48--53",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-548-7",
notes = "GP-98",
}
@Article{Downing:1998:EMS,
author = "Keith Downing",
title = "Using evolutionary computational techniques in
environmental modelling",
journal = "Environmental Modelling and Software",
year = "1998",
volume = "13",
pages = "519--528",
number = "5-6",
keywords = "genetic algorithms, genetic programming, Evolutionary
computation, Evolutionary ecology",
owner = "wlangdon",
ISSN = "1364-8152",
URL = "http://www.sciencedirect.com/science/article/B6VHC-3VGHBS1-1G/2/20d163b7dea17eb9b21f06211acd3188",
doi = "doi:10.1016/S1364-8152(98)00050-4",
abstract = "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.",
}
@InProceedings{downing:2001:gecco,
title = "Adaptive Genetic Programs via Reinforcement Learning",
author = "Keith L. Downing",
pages = "19--26",
year = "2001",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2001)",
editor = "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",
address = "San Francisco, California, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "7-11 " # jul,
keywords = "genetic algorithms, genetic programming, Reinforcement
Learning, Baldwin Effect, Lamarckianism, Hybrid
Adaptive Systems",
ISBN = "1-55860-774-9",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d01.pdf",
notes = "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 \cite{spector:2001:GECCO}",
}
@Article{downing:2001:GPEM,
author = "Keith L. Downing",
title = "Reinforced Genetic Programming",
journal = "Genetic Programming and Evolvable Machines",
year = "2001",
volume = "2",
number = "3",
pages = "259--288",
month = sep,
keywords = "genetic algorithms, genetic programming, reinforcement
learning, the Baldwin Effect, Lamarckism",
ISSN = "1389-2576",
URL = "http://www.idi.ntnu.no/grupper/ai/eval/reinforcedGP/gpem.pdf",
URL = "http://www.idi.ntnu.no/grupper/ai/eval/reinforcedGP/",
doi = "doi:10.1023/A:1011953410319",
size = "27 pages",
abstract = "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.",
notes = "Article ID: 357595",
}
@Article{downing:2005:GPEM,
author = "Keith L. Downing",
title = "Tantrix: {A} Minute to Learn, 100 (Genetic Algorithm)
Generations to Master",
journal = "Genetic Programming and Evolvable Machines",
year = "2005",
volume = "6",
number = "4",
pages = "381--406",
month = dec,
keywords = "genetic algorithms, indirect-encoded genomes",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-005-4803-x",
size = "26 pages",
abstract = "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.",
}
@InProceedings{downing:2005:CEC,
author = "Richard Mark Downing",
title = "Evolving Binary Decision Diagrams using Implicit
Neutrality",
booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary
Computation",
year = "2005",
editor = "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",
volume = "3",
pages = "2107--2113",
address = "Edinburgh, UK",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "2-5 " # sep,
organisation = "IEEE Computational Intelligence Society, Institution
of Electrical Engineers (IEE), Evolutionary Programming
Society (EPS)",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-9363-5",
URL = "http://www.cs.bham.ac.uk/~rmd/pubs/evolvingbddsCEC2005.pdf",
size = "7 pages",
abstract = "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.",
notes = "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.",
}
@InProceedings{Downing:2006:CEC,
author = "Richard M. Downing",
title = "Neutrality and gradualism: encouraging exploration and
exploitation simultaneously with Binary Decision
Diagrams",
booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary
Computation",
year = "2006",
pages = "615--622",
address = "Vancouver",
month = "6-21 " # jul,
publisher = "IEEE Press",
email = "rmd@cs.bham.ac.uk",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-9487-9",
URL = "http://www.cs.bham.ac.uk/~rmd/pubs/gradualism.pdf",
abstract = "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.",
notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
the IEE.",
}
@InProceedings{Downing:PPSN:2006,
author = "Richard M. Downing",
title = "Evolving Binary Decision Diagrams with emergent
variable orderings",
booktitle = "Parallel Problem Solving from Nature - PPSN IX",
year = "2006",
editor = "Thomas Philip Runarsson and Hans-Georg Beyer and
Edmund Burke and Juan J. Merelo-Guervos and L. Darrell
Whitley and Xin Yao",
volume = "4193",
pages = "798--807",
series = "LNCS",
address = "Reykjavik, Iceland",
publisher_address = "Berlin",
month = "9-13 " # sep,
publisher = "Springer-Verlag",
email = "rmd@cs.bham.ac.uk",
ISBN = "3-540-38990-3",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.bham.ac.uk/~rmd/pubs/ppsn06.pdf",
doi = "doi:10.1007/11844297_81",
size = "10 pages",
abstract = "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.",
notes = "PPSN-IX",
}
@InProceedings{eurogp07:downing,
author = "Richard M. Downing",
title = "On population size and neutrality: facilitating the
evolution of evolvability",
editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and
Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar",
booktitle = "Proceedings of the 10th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "4445",
year = "2007",
address = "Valencia, Spain",
month = "11-13 " # apr,
pages = "181--192",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-71602-5",
isbn13 = "978-3-540-71602-0",
doi = "doi:10.1007/978-3-540-71605-1_17",
abstract = "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.",
notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in
conjunction with EvoCOP2007, EvoBIO2007 and
EvoWorkshops2007",
}
@InProceedings{conf/eurogp/Downing08,
title = "Evolvability Via Modularity-Induced Mutational
Focussing",
author = "Richard M. Downing",
bibdate = "2008-04-15",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#Downing08",
booktitle = "Proceedings of the 11th European Conference on Genetic
Programming, EuroGP 2008",
address = "Naples",
month = "26-28 " # mar,
publisher = "Springer",
year = "2008",
volume = "4971",
editor = "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",
isbn13 = "978-3-540-78670-2",
pages = "194--205",
series = "Lecture Notes in Computer Science",
doi = "doi:10.1007/978-3-540-78671-9_17",
keywords = "genetic algorithms, genetic programming",
notes = "Part of \cite{conf/eurogp/2008} EuroGP'2008 held in
conjunction with EvoCOP2008, EvoBIO2008 and
EvoWorkshops2008",
}
@InProceedings{dracopoulos:1996:sGPpBSP,
author = "Dimitris C. Dracopoulos and Simon Kent",
title = "Speeding up Genetic Programming: {A} Parallel {BSP}
Implementation",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "421",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
size = "1 page",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96
5 page version available via
citeseer.ist.psu.edu/233993.html
",
}
@InProceedings{Dracopoulos:1997:es,
author = "Dimitris C. Dracopoulos",
title = "Evolutionary Control of a Satellite",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "77--81",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/Dracopoulos_1997_es.pdf",
size = "5 pages",
notes = "GP-97",
}
@Book{dracopoulos:1997:elanac,
author = "Dimitris C. Dracopoulos",
title = "Evolutionary Learning Algorithms for Neural Adaptive
Control",
publisher = "Springer Verlag",
year = "1997",
series = "Perspectives in Neural Computing",
address = "P.O. Box 31 13 40, D-10643 Berlin, Germany",
month = aug,
email = "orders@springer.de",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-76161-6",
URL = "http://www.amazon.co.uk/exec/obidos/ASIN/3540761616/qid%3D1106423488/202-4979008-1846244",
abstract = "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.",
notes = "Chapter 7 deals with genetic algorithms, including 8
pages on genetic programming. These include solving the
problem described in \cite{Dracopoulos:1997:es}",
size = "212 pages",
}
@InCollection{dracopoulos:1997:GAGPc,
author = "Dimitris C. Dracopoulos",
title = "Genetic Algorithms and Genetic Programming for
Control",
booktitle = "Evolutionary Algorithms in Engineering Applications",
publisher = "Springer-Verlag",
year = "1997",
editor = "Dipankar Dasupta and Zbigniew Michalewicz",
pages = "329--343",
address = "Berlin",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-62021-4",
isbn13 = "978-3-540-62021-1",
URL = "http://www.springer.com/computer/swe/book/978-3-540-62021-1",
notes = "brief survey of GA and GP in control. Principly
concentrates upon using GP to control a tumbling
satellite",
}
@InProceedings{Dracopoulos:2007:WCE,
title = "Autolanding of Commercial Aircrafts by Genetic
Programming",
author = "Dimitris C. Dracopoulos",
booktitle = "Proceedings of the World Congress on Engineering, WCE
2007",
year = "2007",
volume = "I",
address = "London",
month = jul # " 2-4",
keywords = "genetic algorithms, genetic programming, autolanding,
aircraft, intelligent control, evolutionary control",
isbn13 = "978-988-98671-5-7",
URL = "http://www.iaeng.org/publication/WCE2007/WCE2007_pp83-86.pdf",
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.148.6342",
bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
contributor = "CiteSeerX",
language = "en",
oai = "oai:CiteSeerXPSU:10.1.1.148.6342",
pages = "83--86",
abstract = "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.",
}
@InProceedings{Dracopoulos:2010:PPSN,
author = "Dimitris Dracopoulos and Riccardo Piccoli",
title = "Bioreactor Control by Genetic Programming",
booktitle = "PPSN 2010 11th International Conference on Parallel
Problem Solving From Nature",
year = "2010",
editor = "Robert Schaefer and Carlos Cotta and Joanna Kolodziej
and Guenter Rudolph",
publisher = "Springer",
pages = "181--188",
series = "Lecture Notes in Computer Science",
address = "Krakow, Poland",
month = "11-15 " # sep,
volume = "6239",
keywords = "genetic algorithms, genetic programming, bioreactor
control, nonlinear control",
doi = "doi:10.1007/978-3-642-15871-1_19",
abstract = "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.",
affiliation = "School of Electronics and Computer Science, University
of Westminster, London, UK",
}
@InProceedings{dracopoulos:2012:EuroGP,
author = "Dimitris C. Dracopoulos and Dimitrios Effraimidis",
title = "Genetic Programming for Generalised Helicopter
Hovering Control",
booktitle = "Proceedings of the 15th European Conference on Genetic
Programming, EuroGP 2012",
year = "2012",
month = "11-13 " # apr,
editor = "Alberto Moraglio and Sara Silva and Krzysztof Krawiec
and Penousal Machado and Carlos Cotta",
series = "LNCS",
volume = "7244",
publisher = "Springer Verlag",
address = "Malaga, Spain",
pages = "25--36",
organisation = "EvoStar",
isbn13 = "978-3-642-29138-8",
doi = "doi:10.1007/978-3-642-29139-5_3",
keywords = "genetic algorithms, genetic programming, Helicopter
hovering, Nonlinear control, Neuroevolutionary control,
Reinforcement learning",
abstract = "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.",
notes = "Part of \cite{Moraglio:2012:GP} EuroGP'2012 held in
conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012
and EvoApplications2012",
}
@InProceedings{drechsler:1996:GAshtOKFDD,
author = "Rold Drechsler and Bernd Becker and Nicole Gockel",
title = "A Genetic Algorithm for the Construction of Small and
Highly Testable {OKFDD} Circuits",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "Genetic Algorithms",
pages = "473--478",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96 GA paper",
}
@InProceedings{drechsler:2001:EuroGP,
author = "Nicole Drechsler and Frank Schmiedle and Daniel Grosse
and Rolf Drechsler",
title = "Heuristic Learning based on Genetic Programming",
booktitle = "Genetic Programming, Proceedings of EuroGP'2001",
year = "2001",
editor = "Julian F. Miller and Marco Tomassini and Pier Luca
Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
William B. Langdon",
volume = "2038",
series = "LNCS",
pages = "1--10",
address = "Lake Como, Italy",
publisher_address = "Berlin",
month = "18-20 " # apr,
organisation = "EvoNET",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, Heuristic
Learning, VLSI CAD, BDD, Binary Decision Diagrams",
ISBN = "3-540-41899-7",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=1",
size = "10 pages",
abstract = "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.",
notes = "EuroGP'2001, part of \cite{miller:2001:gp}",
}
@TechReport{drecourt:1999uANNGPrrmTR,
author = "Jean-Philippe Drecourt",
title = "Application Of Neural Networks And Genetic Programming
To Rainfall Runoff modeling",
institution = "Danish Hydraulic Institute (Hydro-Informatics
Technologies HIT)",
year = "1999",
type = "D2K Technical Report",
number = "D2K-0699-1",
month = jun,
keywords = "genetic algorithms, genetic programming",
broken = "http://projects.dhi.dk/d2k/Publications/D2K-TR-0699-01.pdf",
abstract = "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.",
notes = "See also \cite{drecourt:1999uANNGPrrm}",
size = "38 pages",
}
@InProceedings{drecourt:1999uANNGPrrm,
author = "J-P. Drecourt",
title = "Using Artificial Neural Networks and Genetic
Programming in rainfall/runoff modeling",
booktitle = "3rd DHI Software Conference \& DHI Software Courses",
year = "1999",
address = "Helsingor, Denmark",
month = "7-11 " # jun,
organisation = "Danish Hydraulic Institute",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.dhi.dk/softcon/abstract/102.doc",
abstract = "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.",
notes = "http://www.dhi.dk/softcon/index.htm See also
\cite{drecourt:1999uANNGPrrmTR}",
}
@InProceedings{Dreschler:1997:BEA,
author = "Rolf Dreschler and Nicole Gockel and Elke Mackensen
and Bernd Becker",
title = "{BEA}: Specialized Hardware for Implementation of
Evolutionary Algorithms",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "Evolvable Hardware",
pages = "491",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
notes = "GP-97",
}
@Article{Drigas:2009:IJSHC,
title = "Decade review (1999-2009): progress of application of
artificial intelligence tools in student diagnosis",
author = "Athanasios S. Drigas and Katerina Argyri and John
Vrettaros",
publisher = "Inderscience Publishers",
year = "2009",
volume = "1",
journal = "International Journal of Social and Humanistic
Computing",
issue = "2",
pages = "175--191",
keywords = "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",
ISSN = "1752-6132",
URL = "http://www.inderscience.com/link.php?id=31006",
doi = "doi:10.1504/IJSHC.2009.031006",
language = "eng",
bibsource = "OAI-PMH server at www.inderscience.com",
abstract = "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.",
notes = "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",
}
@InCollection{driscoll:2003:GPTP,
author = "Joseph A. Driscoll and Bill Worzel and Duncan
MacLean",
title = "Classification of Gene Expression Data with Genetic
Programming",
booktitle = "Genetic Programming Theory and Practice",
publisher = "Kluwer",
year = "2003",
editor = "Rick L. Riolo and Bill Worzel",
chapter = "3",
pages = "25--42",
keywords = "genetic algorithms, genetic programming,
classification, molecular diagnostics",
abstract = "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.",
notes = "Part of \cite{RioloWorzel:2003}",
size = "pages",
}
@InProceedings{drost:2000:mbea,
author = "Stefan Droste and Dirk Wiesmann",
title = "Metric Based Evolutionary Algorithms",
booktitle = "Genetic Programming, Proceedings of EuroGP'2000",
year = "2000",
editor = "Riccardo Poli and Wolfgang Banzhaf and William B.
Langdon and Julian F. Miller and Peter Nordin and
Terence C. Fogarty",
volume = "1802",
series = "LNCS",
pages = "29--43",
address = "Edinburgh",
publisher_address = "Berlin",
month = "15-16 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-67339-3",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=29",
abstract = "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.",
notes = "EuroGP'2000, part of \cite{poli:2000:GP}",
}
@InProceedings{Droste:1997:eGPbf,
author = "Stefan Droste",
title = "Efficient Genetic Programming for Finding Good
Generalizing {Boolean} Functions",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "82--87",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
URL = "https://eldorado.uni-dortmund.de/dspace/bitstream/2003/5323/1/gp97.pdf",
URL = "http://citeseer.ist.psu.edu/326196.html",
size = "pages",
abstract = "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.",
notes = "GP-97",
}
@InProceedings{droste:1998:GPgq,
author = "Stefan Droste",
title = "Genetic Programming with Guaranteed Quality",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "54--59",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-548-7",
URL = "https://eldorado.uni-dortmund.de/dspace/bitstream/2003/5321/2/ci1597_doc.pdf",
URL = "http://citeseer.ist.psu.edu/324287.html",
abstract = "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.",
notes = "GP-98",
}
@TechReport{oai:CiteSeerPSU:323494,
author = "Stefan Droste and Dirk Wiesmann",
title = "On Representation and Genetic Operators in
Evolutionary Algorithms",
institution = "Collaborative Research Center 531, University of
Dortmund",
year = "1998",
type = "Computational Intelligence",
number = "CI-41/98",
address = "Germany",
month = jul,
keywords = "genetic algorithms, genetic programming",
URL = "https://eldorado.uni-dortmund.de/bitstream/2003/5341/2/ci4198_doc.pdf",
URL = "http://citeseer.ist.psu.edu/323494.html",
citeseer-isreferencedby = "oai:CiteSeerPSU:307380;
oai:CiteSeerPSU:329669; oai:CiteSeerPSU:566191;
oai:CiteSeerPSU:446781; oai:CiteSeerPSU:503477;
oai:CiteSeerPSU:307655; oai:CiteSeerPSU:535886;
oai:CiteSeerPSU:533810; oai:CiteSeerPSU:429223;
oai:CiteSeerPSU:568622; oai:CiteSeerPSU:410451;
oai:CiteSeerPSU:560526; oai:CiteSeerPSU:390701;
oai:CiteSeerPSU:245841; oai:CiteSeerPSU:467673;
oai:CiteSeerPSU:437423; oai:CiteSeerPSU:499412;
oai:CiteSeerPSU:544364; oai:CiteSeerPSU:442759;
oai:CiteSeerPSU:425758; oai:CiteSeerPSU:491280;
oai:CiteSeerPSU:458877; oai:CiteSeerPSU:376503;
oai:CiteSeerPSU:320772; oai:CiteSeerPSU:311105;
oai:CiteSeerPSU:564187; oai:CiteSeerPSU:503375;
oai:CiteSeerPSU:279898; oai:CiteSeerPSU:531371;
oai:CiteSeerPSU:443995; oai:CiteSeerPSU:326622;
oai:CiteSeerPSU:447917; oai:CiteSeerPSU:501036;
oai:CiteSeerPSU:551069; oai:CiteSeerPSU:534318;
oai:CiteSeerPSU:412981; oai:CiteSeerPSU:525337;
oai:CiteSeerPSU:431041; oai:CiteSeerPSU:39575;
oai:CiteSeerPSU:545421; oai:CiteSeerPSU:409722;
oai:CiteSeerPSU:551823; oai:CiteSeerPSU:422170",
citeseer-isreferencedby = "oai:CiteSeerPSU:479554;
oai:CiteSeerPSU:303654; oai:CiteSeerPSU:543930;
oai:CiteSeerPSU:539151; oai:CiteSeerPSU:330474;
oai:CiteSeerPSU:563371; oai:CiteSeerPSU:501369;
oai:CiteSeerPSU:297037; oai:CiteSeerPSU:372595;
oai:CiteSeerPSU:420664; oai:CiteSeerPSU:462007;
oai:CiteSeerPSU:459145; oai:CiteSeerPSU:341471;
oai:CiteSeerPSU:550055",
citeseer-references = "oai:CiteSeerPSU:21876; oai:CiteSeerPSU:250966;
oai:CiteSeerPSU:311874; oai:CiteSeerPSU:326196;
oai:CiteSeerPSU:324287; oai:CiteSeerPSU:32327;
oai:CiteSeerPSU:265991; oai:CiteSeerPSU:126133;
oai:CiteSeerPSU:347272; oai:CiteSeerPSU:125144",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:323494",
rights = "unrestricted",
abstract = "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.",
size = "34 pages",
}
@InProceedings{droste:1999:PNFLBALFA,
author = "Stefan Droste and Thomas Jansen and Ingo Wegener",
title = "Perhaps Not a Free Lunch But At Least a Free
Appetizer",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "833--839",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "evolution strategies and evolutionary programming",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/droste98perhaps.pdf",
URL = "http://arc.cs.odu.edu:8080/dp9/getrecord/oai_dc/3050294235/oai:eldorado:0x00000307",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@Misc{oai:CiteSeerPSU:411824,
author = "Stefan Droste and Dominic Heutelbeck and Ingo
Wegener",
title = "Distributed Hybrid Genetic programming for learning
{Boolean} Functions",
institution = "Department of Computer Science/XI, University of
Dortment",
year = "2000",
number = "CI-90/00",
address = "44221 Dortmund, Germany",
month = aug,
keywords = "genetic algorithms, genetic programming",
URL = "https://eldorado.uni-dortmund.de/dspace/bitstream/2003/5393/1/ci90.pdf",
URL = "http://citeseer.ist.psu.edu/411824.html",
abstract = "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.",
size = "pages",
}
@InProceedings{DrostePPSN2000,
author = "Stefan Droste and Dominic Heutelbeck and Ingo
Wegener",
title = "Distributed Hybrid Genetic Programming for Learning
{Boolean} Functions",
booktitle = "Parallel Problem Solving from Nature - PPSN VI 6th
International Conference",
editor = "Marc Schoenauer and Kalyanmoy Deb and G{\"u}nter
Rudolph and Xin Yao and Evelyne Lutton and Juan Julian
Merelo and Hans-Paul Schwefel",
year = "2000",
publisher = "Springer Verlag",
address = "Paris, France",
month = "16-20 " # sep,
volume = "1917",
series = "LNCS",
pages = "181--190",
keywords = "genetic algorithms, genetic programming",
URL = "http://ls2-www.cs.uni-dortmund.de/~wegener/papers/Paper93.ps",
URL = "http://eldorado.uni-dortmund.de/0x81d98002_0x00034a39",
URL = "http://citeseer.ist.psu.edu/322232.html",
abstract = "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",
}
@InCollection{droste:2003:ACI,
author = "Stefan Droste and Thomas Jansen and G{\"u}nter Rudolph
and Hans-Paul Schwefel and Karsten Tinnefeld and Ingo
Wegener",
title = "Theory of Evolutionary Algorithms and Genetic
Programming",
booktitle = "Advances in Computational Intelligence: Theory and
Practice",
publisher = "Springer",
year = "2003",
editor = "Hans-Paul Schwefel and Ingo Wegener and Klaus
Weinert",
series = "Natural Computing Series",
chapter = "5",
pages = "107--144",
keywords = "genetic algorithms, genetic programming, NFL,
Evolutionary Algorithms, Multiobjective Evolutionary
Algorithms, Crossover, Takeover Times",
ISBN = "3-540-43269-8",
notes = "Dynamization and Adaptation. Black-box Optimisation.
Metric-Based EA (MBEA) and an Application in GP",
}
@Article{Drstvensek:2004:JMPT,
author = "I. Drstvensek and I. Pahole and J. Balic",
title = "A model of data flow in lower {CIM} levels",
journal = "Journal of Materials Processing Technology",
year = "2004",
volume = "157-158",
pages = "123--130",
abstract = "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.",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6TGJ-4DTM097-5/2/79f4a5e8d987732d6aaad71154b9cf18",
month = "20 " # dec,
keywords = "genetic algorithms, genetic programming",
ISSN = "0924-0136",
doi = "doi:10.1016/j.jmatprotec.2004.09.010",
}
@InProceedings{Drstvensek:2005:TMT,
author = "Igor Drstvensek and Tomaz Brajlih and Miha Kovacic and
Joze Balic",
title = "Assurance of Accuracy at Polymerisation of
Photopolymers",
booktitle = "9th International Research/Expert Conference Trends in
the Development Machinery and Associated Technology",
year = "2005",
editor = "Sabahudin Ekinovic",
pages = "677--680",
address = "Antalya, Turkey",
month = "26-30 " # sep,
organisation = "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",
keywords = "genetic algorithms, genetic programming",
ISBN = "9958-617-28-5",
notes = "
TMT05-107
http://www.mf.unze.ba/tmt2005/submitted3.html",
}
@InProceedings{1277278,
author = "Jan Drugowitsch and Alwyn M. Barry",
title = "Mixing independent classifiers",
booktitle = "GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation",
year = "2007",
editor = "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",
volume = "2",
isbn13 = "978-1-59593-697-4",
pages = "1596--1603",
address = "London",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1596.pdf",
doi = "doi:10.1145/1276958.1277278",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
month = "7-11 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, information
fusion, learning classifier system (LCS), XCS",
abstract = "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.",
notes = "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",
}
@InProceedings{Drunpob:2005:WWERC,
author = "A. Drunpob and N. B. Chang and M. Beaman",
title = "Stream Flowrate Prediction Using Genetic Programming
Model in a Semi-Arid Coastal Watershed",
booktitle = "World Water and Environmental Resources Congress
2005",
year = "2005",
editor = "Raymond Walton",
address = "Anchorage, Alaska, USA",
month = may # " 15-19",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1061/40792(173)352",
abstract = "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.",
notes = "c2005 ASCE",
}
@InProceedings{DuDXXWC:2009:GEC,
author = "Xin Du and Lixin Ding and Chen Wang Xie and Xing Xu
and Shenwen Wang and Li Chen2",
title = "Convergence analysis of gene expression programming
based on maintaining elitist",
booktitle = "GEC '09: Proceedings of the first ACM/SIGEVO Summit on
Genetic and Evolutionary Computation",
year = "2009",
editor = "Lihong Xu and Erik D. Goodman and Guoliang Chen and
Darrell Whitley and Yongsheng Ding",
bibsource = "DBLP, http://dblp.uni-trier.de",
pages = "823--826",
address = "Shanghai, China",
organisation = "SigEvo",
doi = "doi:10.1145/1543834.1543952",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = jun # " 12-14",
isbn13 = "978-1-60558-326-6",
keywords = "genetic algorithms, genetic programming, Poster, Gene
Expression Programming",
abstract = "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.",
notes = "Also known as \cite{DBLP:conf/gecco/DuDXXWC09} part of
\cite{DBLP:conf/gec/2009}",
}
@InProceedings{conf/adma/DuanTZWZ06,
title = "Distance Guided Classification with Gene Expression
Programming",
author = "Lei Duan and Changjie Tang and Tianqing Zhang and
Dagang Wei and Huan Zhang",
booktitle = "Advanced Data Mining and Applications, Proceedings of
the Second International Conference, {ADMA}",
publisher = "Springer",
year = "2006",
volume = "4093",
editor = "Xue Li and Osmar R. Za{\"i}ane and Zhanhuai Li",
pages = "239--246",
series = "Lecture Notes in Computer Science",
address = "Xi'an, China",
month = aug # " 14-16",
bibdate = "2006-08-21",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/adma/adma2006.html#DuanTZWZ06",
keywords = "genetic algorithms, genetic programming, Gene
Expression Programming",
ISBN = "3-540-37025-0",
doi = "doi:10.1007/11811305_26",
abstract = "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.",
}
@InProceedings{conf/icnc/DuanTTZZ09,
title = "An Effective Microarray Data Classifier Based on Gene
Expression Programming",
author = "Lei Duan and Changjie Tang and Liang Tang and Jie Zuo
and Tianqing Zhang",
booktitle = "Fifth International Conference on Natural Computation,
2009. ICNC '09",
year = "2009",
editor = "Haiying Wang and Kay Soon Low and Kexin Wei and
Junqing Sun",
month = "14-16 " # aug,
address = "Tianjian, China",
publisher = "IEEE Computer Society",
isbn13 = "978-0-7695-3736-8",
keywords = "genetic algorithms, genetic programming, gene
expression programming",
bibdate = "2010-01-22",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/icnc/icnc2009-4.html#DuanTTZZ09",
pages = "523--527",
doi = "doi:10.1109/ICNC.2009.267",
}
@InProceedings{conf/adma/DuanTTZZ09,
title = "Mining Class Contrast Functions by Gene Expression
Programming",
author = "Lei Duan and Changjie Tang and Liang Tang and Tianqing
Zhang and Jie Zuo",
booktitle = "Proceedings 5th International Conference Advanced Data
Mining and Applications {ADMA} 2009",
year = "2009",
volume = "5678",
editor = "Ronghuai Huang and Qiang Yang and Jian Pei and
Jo{\~a}o Gama and Xiaofeng Meng and Xue Li",
pages = "116--127",
series = "Lecture Notes in Computer Science",
address = "Beijing, China",
month = aug # " 17-19",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, gene
expression programming",
isbn13 = "978-3-642-03347-6",
doi = "doi:10.1007/978-3-642-03348-3",
bibdate = "2009-08-18",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/adma/adma2009.html#DuanTTZZ09",
}
@InProceedings{duan:2001:gecco,
title = "Estimating Stock Price Predictability Using Genetic
Programming",
author = "Minglei Duan and Richard J. Povinelli",
pages = "174",
year = "2001",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2001)",
editor = "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",
address = "San Francisco, California, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "7-11 " # jul,
keywords = "genetic algorithms, genetic programming: Poster, time
series, data mining, prediction",
ISBN = "1-55860-774-9",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d02.pdf",
notes = "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 \cite{spector:2001:GECCO}",
}
@InProceedings{duan_annie2001a,
author = "Minglei Duan and Richard Povinelli",
title = "Nonlinear Modeling: Genetic Programming vs. Fast
Evolutionary Programming",
booktitle = "Intelligent Engineering Systems Through Artificial
Neural Networks (ANNIE 2001)",
year = "2001",
editor = "Cihan H. Dagli",
pages = "171--176",
address = "St. Louis, Missouri, USA",
month = "4-7 " # nov,
organisation = "University of Missouri-Rolla, Smart Engineering
Systems Laboratory Department of Engineering
Management
In Cooperation with IEEE Neural Networks Council",
keywords = "genetic algorithms, genetic programming",
URL = "http://povinelli.eece.mu.edu/publications/papers/annie2001a.pdf",
notes = "cf http://web.umr.edu/~annie/annie01/ ANNIE01 session
TP3.3C
Marquette University, Milwaukee, WI, USA
GPsys \cite{qureshi:thesis} (java)
Sunspot; Mackey-glass; Compaq (NYSE) and microsoft
(NASDAQ) stock prices. GP >= EP",
}
@InProceedings{duan_annie2001b,
author = "Minglei Duan and Richard Povinelli",
title = "Estimating Time Series Predictability Using Genetic
Programming",
booktitle = "Intelligent Engineering Systems Through Artificial
Neural Networks (ANNIE 2001)",
year = "2001",
editor = "Cihan H. Dagli",
pages = "215--220",
address = "St. Louis, Missouri, USA",
month = "4-7 " # nov,
organisation = "University of Missouri-Rolla, Smart Engineering
Systems Laboratory Department of Engineering
Management
In Cooperation with IEEE Neural Networks Council",
keywords = "genetic algorithms, genetic programming",
URL = "http://povinelli.eece.mu.edu/publications/papers/annie2001b.pdf",
notes = "cf http://web.umr.edu/~annie/annie01/ ANNIE01 session
WP1.3C
Marquette University, Milwaukee, WI, USA
GPsys \cite{qureshi:thesis} (java)
Compaq (NYSE) and general eletric GE 1999 stock
prices",
}
@Article{Dubcakova:2011:GPEM,
author = "Renata Dubcakova",
title = "Eureqa: software review",
journal = "Genetic Programming and Evolvable Machines",
year = "2011",
volume = "12",
number = "2",
pages = "173--178",
month = jun,
keywords = "genetic algorithms, genetic programming",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-010-9124-z",
size = "6 pages",
}
@Article{Dubreuil:2006:SMC,
author = "Marc Dubreuil and Christian Gagne and Marc Parizeau",
title = "Analysis of a Master-Slave Architecture for
Distributed Evolutionary Computations",
journal = "IEEE Transactions on Systems, Man, and Cybernetics:
Part B - Cybernetics",
year = "2006",
volume = "36",
number = "1",
pages = "229--235",
month = feb,
keywords = "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",
ISSN = "1083-4419",
URL = "http://vision.gel.ulaval.ca/~parizeau/Publications/SMC06.pdf",
doi = "doi:10.1109/TSMCB.2005.856724",
size = "7 pages",
abstract = "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.",
}
@Article{Duerrenmatt:2012:WST,
author = "David J. Duerrenmatt and Willi Gujer",
title = "Automatic reactor model synthesis with genetic
programming",
journal = "Water Science \& Technology",
year = "2012",
volume = "65",
number = "4",
pages = "765--772",
keywords = "genetic algorithms, genetic programming, grammar-based
genetic programming, hydraulic reactor systems,
modelling, operating data",
ISSN = "0273-1223",
URL = "http://www.iwaponline.com/wst/06504/0765/065040765.pdf",
doi = "doi:10.2166/wst.2012.913",
size = "8 pages",
abstract = "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.",
notes = "Sewage treatment plant",
}
@InProceedings{duffy:1999:CEF,
author = "John Duffy and Jim Engle-Warnick",
title = "Using Symbolic Regression to Infer Strategies from
Experimental Data",
booktitle = "Fifth International Conference: Computing in Economics
and Finance",
year = "1999",
editor = "David A. Belsley and Christopher F. Baum",
pages = "150",
address = "Boston College, MA, USA",
month = "24-26 " # jun,
note = "Book of Abstracts",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.pitt.edu/~jduffy/docs/Usr.pdf",
URL = "http://www.pitt.edu/~jduffy/docs/Usr.ps",
URL = "http://citeseer.ist.psu.edu/304022.html",
abstract = "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.",
notes = "CEF'99 See also \cite{duffy:1999:srised}
http://fmwww.bc.edu/cef99/sess/chen.cfp.html",
size = "21 pages",
}
@InCollection{duffy:1999:srised,
author = "John Duffy and Jim Engle-Warnick",
title = "Using Symbolic Regression to Infer Strategies from
Experimental Data",
booktitle = "Evolutionary Computation in Economics and Finance",
publisher = "Physica Verlag",
year = "2002",
editor = "Shu-Heng Chen",
volume = "100",
series = "Studies in Fuzziness and Soft Computing",
chapter = "4",
pages = "61--84",
month = "2002",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-7908-1476-8",
URL = "http://www.pitt.edu/~jduffy/docs/Usr.ps",
abstract = "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.",
notes = "Presented at CEF'99 (see \cite{duffy: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",
size = "21 pages",
}
@InProceedings{dulewicz:2001:HIS,
title = "Evolving Natural Language Parser with Genetic
Programming",
author = "Grzegorz Dulewicz and Olgierd Unold",
editor = "Ajith Abraham and Mario Koppen",
booktitle = "2001 International Workshop on Hybrid Intelligent
Systems",
series = "LNCS",
pages = "361--378",
publisher = "Springer-Verlag",
address = "Adelaide, Australia",
publisher_address = "Berlin",
month = "11-12 " # dec,
year = "2001",
broken = "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-7908-1480-6",
URL = "http://www.amazon.com/Hybrid-Information-Systems-Ajith-Abraham/dp/3790814806/ref=sr_1_8?s=books&ie=UTF8&qid=1326475568&sr=1-8",
ISBN = "3-7908-1480-6",
keywords = "genetic algorithms, genetic programming, natural
language processing, edge encoding",
abstract = "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.",
notes = "HIS01",
}
@InProceedings{Dumont:2000:primeca,
author = "G. Dumont and Frederic Chapelle and O. Chocron and
Philippe Bidaud",
title = "Prototypage virtuel d'un micro-endoscope",
booktitle = "Journee thematique PRIMECA",
address = "Valenciennes, France",
month = mar,
note = "in french",
size = "5 pages",
keywords = "genetic algorithms",
year = "2000",
}
@InProceedings{Dumont:2000:jpmr,
author = "G. Dumont and Frederic Chapelle",
title = "Simulation multi-physique pour la conception en
micro-robotique",
booktitle = "Journees du Pole Micro-robotique",
address = "Cachan, France",
month = jun,
note = "in french",
size = "9 pages",
keywords = "genetic algorithms",
year = "2000",
}
@InProceedings{Dumont:2001:isr,
author = "Georges Dumont and Frederic Chapelle and Philippe
Bidaud",
title = "Toward virtual prototyping of active endoscopes",
booktitle = "International Symposium on Robotics (ISR'01)",
address = "Seoul, Korea",
month = "19-20 " # apr,
organization = "International Federation of Robotics",
pages = "821--826",
keywords = "genetic algorithms",
year = "2001",
notes = "http://isr2001.kist.re.kr/Teams/isr2001/sessionprogram.htm",
}
@InProceedings{Dunay:1994:rliGP,
author = "Bertrand Daniel Dunay and Frederick E. Petry and Bill
P Buckles",
title = "Regular language induction with genetic programming",
booktitle = "Proceedings of the 1994 IEEE World Congress on
Computational Intelligence",
year = "1994",
pages = "396--400",
volume = "1",
address = "Orlando, Florida, USA",
month = "27-29 " # jun,
publisher = "IEEE Press",
keywords = "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",
doi = "doi:10.1109/ICEC.1994.349918",
size = "5 pages",
abstract = "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",
notes = "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.",
}
@InProceedings{dunay:1995:scpga,
author = "Bertrand Daniel Dunay and Frederic E. Petry",
title = "Solving Complex Problems with Genetic Algorithms",
booktitle = "Genetic Algorithms: Proceedings of the Sixth
International Conference (ICGA95)",
year = "1995",
editor = "Larry J. Eshelman",
pages = "264--270",
address = "Pittsburgh, PA, USA",
publisher_address = "San Francisco, CA, USA",
month = "15-19 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-370-0",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/dunay_1995_scpga.pdf",
size = "7 pages",
abstract = "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).",
}
@Article{Dunn20061209,
author = "Enrique Dunn and Gustavo Olague and Evelyne Lutton",
title = "Parisian camera placement for vision metrology",
journal = "Pattern Recognition Letters",
volume = "27",
number = "11",
pages = "1209--1219",
year = "2006",
note = "Evolutionary Computer Vision and Image Understanding",
ISSN = "0167-8655",
doi = "DOI:10.1016/j.patrec.2005.07.019",
URL = "http://www.sciencedirect.com/science/article/B6V15-4HX477K-2/2/e82b5b25f9a7a82607ac4b30c9fb9c45",
keywords = "genetic algorithms, genetic programming, Camera
placement, Accurate 3D reconstruction, Photogrammetric
network design, Evolutionary computation, Parisian
approach",
abstract = "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.",
}
@InProceedings{dunning:1996:eanlp,
author = "Ted E. Dunning and Mark W. Davis",
title = "Evolutionary Algorithms for Natural Language
Processing",
booktitle = "Late Breaking Papers at the Genetic Programming 1996
Conference Stanford University July 28-31, 1996",
year = "1996",
editor = "John R. Koza",
pages = "16--23",
address = "Stanford University, CA, USA",
publisher_address = "Stanford University, Stanford, California
94305-3079, USA",
month = "28--31 " # jul,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming, NLP",
ISBN = "0-18-201031-7",
notes = "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",
}
@InProceedings{dupas:1999:RAO,
author = "R. Dupas and G. Cavory and G. Goncalves",
title = "Real-World Applications. Optimising the throughput of
a manufacturing production line using a genetic
algortihm",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1775",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "real world applications, poster papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-717.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-717.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{10.1109/CRV.2006.32,
author = "Jean-Francois Dupuis and Marc Parizeau",
title = "Evolving a Vision-Based Line-Following Robot
Controller",
year = "2006",
publisher = "IEEE Computer Society",
booktitle = "The 3rd Canadian Conference on Computer and Robot
Vision (CRV'06)",
pages = "75",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7695-2542-3",
URL = "http://vision.gel.ulaval.ca/~jfdupuis/pubs/jfdupuisCRV2006.pdf",
doi = "doi:10.1109/CRV.2006.32",
size = "7 pages",
abstract = "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.",
}
@PhdThesis{Dupuis2011ab,
author = "Jean-Francois Dupuis",
title = "Automated Design of Hybrid Systems Using Evolutionary
Computation",
school = "Department of Management Engineering, Engineering
Design and Product Development (K\&P), Technical
University of Denmark",
year = "2011",
address = "Lyngby, Denmark",
month = apr,
keywords = "genetic algorithms, genetic programming, bond graphs",
URL = "http://www.jfdupuis.info/files/Dupuis2011ab.pdf",
size = "172 pages",
abstract = "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.",
notes = "Three-tank system, DC-DC converter",
}
@Article{Dupuis:2011:ieeeTEC,
author = "Jean-Francois Dupuis and Zhun Fan and Erik D.
Goodman",
title = "Evolutionary Design of Both Topologies and Parameters
of a Hybrid Dynamical System",
journal = "IEEE Transactions on Evolutionary Computation",
note = "Accepted for future publication",
keywords = "genetic algorithms, genetic programming, Embryo,
Encoding, Junctions, Mechatronics, Switches, Automated
design, bond graphs, evolutionary design, hybrid
mechatronic systems",
ISSN = "1089-778X",
doi = "doi:10.1109/TEVC.2011.2159724",
size = "15 pages",
abstract = "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.",
notes = "also known as \cite{6045329}",
}
@InProceedings{durand:1998:gxpsf,
author = "Nicolas Durand and Jean-Marc Alliot",
title = "Genetic crossover operator for partially separable
functions",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "487--494",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms",
notes = "SGA-98",
}
@Misc{Durrett:2010:ccaGP2pmips,
author = "Greg Durrett and Frank Neumann and Una-May O'Reilly",
title = "Computational Complexity Analysis of Simple Genetic
Programming On Two Problems Modeling Isolated Program
Semantics",
year = "2010",
month = "27 " # jul,
note = "arXiv:1007.4636v1",
keywords = "genetic algorithms, genetic programming, Computational
Complexity, Data Structures and Algorithms",
URL = "http://arxiv.org/pdf/1007.4636v1",
size = "26 pages",
abstract = "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.",
notes = "See \cite{Durrett:2011:foga}",
}
@InProceedings{Durrett:2011:foga,
author = "Greg Durrett and Frank Neumann and Una-May O'Reilly",
title = "Computational Complexity Analysis of Simple Genetic
Programming On Two Problems Modeling Isolated Program
Semantics",
booktitle = "Foundations of Genetic Algorithms",
year = "2011",
editor = "Hans-Georg Beyer and W. B. Langdon",
pages = "69--80",
address = "Schwarzenberg, Austria",
month = "5-9 " # jan,
organisation = "SigEvo",
publisher = "ACM",
keywords = "genetic algorithms, genetic programming, Genetic
Programming Theory, Computational Complexity, Hill
Climbing",
isbn13 = "978-1-4503-0633-1",
doi = "doi:10.1145/1967654.1967661",
size = "12 pages",
abstract = "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.",
notes = "See \cite{Durrett:2010:ccaGP2pmips} ACM order number
910114",
}
@InProceedings{duyvesteyn:2005:CEC,
author = "Korneel Duyvesteyn and Uzay Kaymak",
title = "Genetic Programming in Economic Modelling",
booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary
Computation",
year = "2005",
editor = "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",
volume = "2",
pages = "1025--1031",
address = "Edinburgh, UK",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "2-5 " # sep,
organisation = "IEEE Computational Intelligence Society, Institution
of Electrical Engineers (IEE), Evolutionary Programming
Society (EPS)",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-9363-5",
abstract = "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.",
notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and
the EPS.",
}
@TechReport{Dworman:95-01-04,
author = "Garett Dworman and Steven Kimbrough and James Laing",
title = "An Application of Genetic Programming to Bargaining in
a Three-Agent Coalition Game",
institution = "Department of Operations and Information Management,
The Wharton School, University of Pennsylvania",
year = "1995",
number = "95-01-04",
address = "Philadelphia PA 19104-6366, USA",
keywords = "genetic algorithms, genetic programming",
URL = "http://opim.wharton.upenn.edu/risk/downloads/archive/arch62.pdf",
notes = "file cogs9506 30 Jan 1995 See also
\cite{dworman:1995:b3acg}",
size = "13 pages",
}
@InProceedings{dworman:1995:b3acg,
author = "Garett Dworman and Steven O. Kimbrough and James D.
Laing",
title = "Bargaining in a Three-Agent Coalition Game: An
Application of Genetic Programming",
booktitle = "Working Notes for the AAAI Symposium on Genetic
Programming",
year = "1995",
editor = "E. V. Siegel and J. R. Koza",
pages = "9--16",
address = "MIT, Cambridge, MA, USA",
publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA",
month = "10--12 " # nov,
publisher = "AAAI",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.aaai.org/Papers/Symposia/Fall/1995/FS-95-01/FS95-01-002.pdf",
URL = "http://www.aaai.org/Library/Symposia/Fall/fs95-01.php",
size = "8 page",
abstract = "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.",
notes = "AAAI-95f GP. Part of \cite{siegel:1995:aaai-fgp}. See
also \cite{Dworman:95-01-04}. {\em Telephone:}
415-328-3123 {\em Fax:} 415-321-4457 {\em email}
info@aaai.org {\em URL:} http://www.aaai.org/",
}
@TechReport{dworman:1995:iGPSgt,
author = "Garett Dworman and Steve O. Kimbrough and James D.
Laing",
title = "Implementation of a Genetic Programming System in a
Game-Theoretic Context",
institution = "University of Pennsylvania, Department of Operations
and Information Management",
year = "1995",
type = "working paper",
number = "95-01-02",
keywords = "genetic algorithms, genetic programming",
broken = "http://opim.wharton.upenn.edu/users/sok/comprats/GPWP01.ps",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/298/http:zSzzSzopim.wharton.upenn.eduzSz~dwormanzSzmy-paperszSzGPWP01.pdf/implementation-of-a-genetic.pdf",
URL = "http://citeseer.ist.psu.edu/169645.html",
URL = "http://opim.wharton.upenn.edu/home/wp/",
size = "14 pages",
}
@InProceedings{dworman:1996:admGPgtc,
author = "Garett Dworman and Steve O. Kimbrough and James D.
Laing",
title = "On Automated Discovery of Models Using Genetic
Programming in Game-Theoretic Contexts",
booktitle = "Proceedings of the 28th Hawaii International
Conference on System Sciences, Volume III: Information
Systems: Decision Support and Knowledge-based Systems",
year = "1995",
editor = "Jay F. {Nunamaker Jr.} and Ralph H. {Sprague Jr.}",
pages = "428--438",
publisher_address = "Los Alamitos, CA",
month = jan,
publisher = "IEEE Computer Society Press",
keywords = "genetic algorithms, genetic programming",
broken = "http://opim.wharton.upenn.edu/users/sok/comprats/HICSSGP6.ps",
broken = "http://opim.wharton.upenn.edu/users/sok/comprats/HICSSGP6-figures.eps",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/298/http:zSzzSzopim.wharton.upenn.eduzSz~dwormanzSzmy-paperszSzHICSSGP6.pdf/dworman95automated.pdf",
URL = "http://citeseer.ist.psu.edu/dworman95automated.html",
doi = "doi:10.1109/HICSS.1995.375625",
size = "13 pages",
abstract = "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.",
}
@InProceedings{dworman:1996:baa2cg,
author = "Garett Dworman and Steven O. Kimbrough and James D.
Laing",
title = "Bargaining by Artificial Agents in Two Coalition
Games: {A} Study in Genetic Programming for Electronic
Commerce",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "54--62",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
size = "9 pages",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96",
}
@Article{Dwyer19701199,
author = "J. M. Dwyer and I. R. Mackay",
title = "{ANTIGEN}-{BINDING} {LYMPHOCYTES} {IN} {HUMAN} {FETAL}
{THYMUS}",
journal = "The Lancet",
volume = "295",
number = "7658",
pages = "1199--1202",
year = "1970",
ISSN = "0140-6736",
doi = "doi:10.1016/S0140-6736(70)91787-3",
URL = "http://www.sciencedirect.com/science/article/B6T1B-498RPPJ-1MK/2/6cc03de5ebb144b1c653e0ffdc1720e8",
notes = "Not on GP",
}
@InProceedings{dzeroski:1995:dsiml,
author = "Saso Dzeroski and Ljupeo Todorovski and Igor
Petrovski",
title = "Dynamical System Identification with Machine
Learning",
booktitle = "Proceedings of the Workshop on Genetic Programming:
From Theory to Real-World Applications",
year = "1995",
editor = "Justinian P. Rosca",
pages = "50--63",
address = "Tahoe City, California, USA",
month = "9 " # jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/dzeroski_1995_dsiml.pdf",
size = "14 pages",
abstract = "LAGRANGE algorithm described, brusselator,
volterra-lotka model of population dynamics, monod
equations, pole balancing, system identification.",
notes = "part of \cite{rosca:1995:ml}",
}
@InProceedings{east:1999:IWOPUGA,
author = "E. William East",
title = "Infrastructure Work Order Planning Using Genetic
Algorithms",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1510--1516",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "real world applications",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-728.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-728.ps",
notes = "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",
}
@Article{Ebbels2009361,
title = "Bioinformatic methods in {NMR}-based metabolic
profiling",
author = "Timothy M. D. Ebbels and Rachel Cavill",
journal = "Progress in Nuclear Magnetic Resonance Spectroscopy",
volume = "55",
number = "4",
pages = "361--374",
year = "2009",
ISSN = "0079-6565",
doi = "doi:10.1016/j.pnmrs.2009.07.003",
URL = "http://www.sciencedirect.com/science/article/B6THC-4WTRS75-2/2/f678fb62de29b228e8d54c803da32b57",
keywords = "genetic algorithms, genetic programming, Metabonomics,
Metabolomics, Metabolic profiling, Bioinformatics,
Statistical methods, Modelling, Machine learning,
Pattern recognition",
}
@Article{Ebel1979131,
author = "Roland H. Ebel and William Wagoner and Henry F.
Hrubecky",
title = "Get ready for the {L}-bomb: {A} preliminary social
assessment of longevity technology",
journal = "Technological Forecasting and Social Change",
volume = "13",
number = "2",
pages = "131--148",
year = "1979",
ISSN = "0040-1625",
doi = "doi:10.1016/0040-1625(79)90108-2",
URL = "http://www.sciencedirect.com/science/article/B6V71-45P0D4G-2X/2/89b1c10893ac90101be199e8be7a7a53",
notes = "not on GP",
}
@InProceedings{Eberbach:1997:eGPdc,
author = "Eugene Eberbach",
title = "Enhancing Genetic Programming by \$-calculus",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "88",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/Eberbach_1997_eGPdc.pdf",
size = "1 page",
notes = "GP-97",
}
@InProceedings{eberbach:1998:xECGPALAADNAClc,
author = "Eugene Eberbach",
title = "Expressing Evolutionary Computation, Genetic
Programming, Artificial Life, Autonomous Agents and
{DNA}-Based Computing in l-Calculus",
booktitle = "Late Breaking Papers at the Genetic Programming 1998
Conference",
year = "1998",
editor = "John R. Koza",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "Stanford, CA, USA",
month = "22-25 " # jul,
publisher = "Stanford University Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "GP-98LB see updated version
\cite{eberbach:2000:eecgpalaadc}",
}
@InProceedings{eberbach:2000:eecgpalaadc,
author = "Eugene Eberbach",
title = "Expressing Evolutionary Computation, Genetic
Programming, Artificial Life, Autonomous Agents, and
{DNA}-Based Computing in \$-Calculus",
booktitle = "Proceedings of the 2000 Congress on Evolutionary
Computation CEC00",
year = "2000",
pages = "1361--1368",
address = "La Jolla Marriott Hotel La Jolla, California, USA",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "6-9 " # jul,
organisation = "IEEE Neural Network Council (NNC), Evolutionary
Programming Society (EPS), Institution of Electrical
Engineers (IEE)",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, new
paradigms",
ISBN = "0-7803-6375-2",
URL = "http://www.cis.umassd.edu/~eeberbach/papers/cec2000.ps",
URL = "http://citeseer.ist.psu.edu/491674.html",
size = "8 pages",
abstract = "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.",
notes = "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
\cite{eberbach:1998:xECGPALAADNAClc}
\$-calculus == cost-calculus",
}
@Article{Eberbach2007200,
author = "Eugene Eberbach",
title = "The \$-calculus process algebra for problem solving:
{A} paradigmatic shift in handling hard computational
problems",
journal = "Theoretical Computer Science",
volume = "383",
number = "2-3",
pages = "200--243",
year = "2007",
note = "Complexity of Algorithms and Computations",
ISSN = "0304-3975",
doi = "DOI:10.1016/j.tcs.2007.04.012",
URL = "http://www.sciencedirect.com/science/article/B6V1G-4NGKWGF-7/2/07c09787a0b898de98e171ac414f6ddc",
keywords = "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",
abstract = "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.",
notes = "GP one technique in many",
}
@InProceedings{Eberbach:2009:cec,
author = "Eugene Eberbach and Mark Burgin",
title = "Evolutionary Automata as Foundation of Evolutionary
Computation: Larry Fogel Was Right",
booktitle = "2009 IEEE Congress on Evolutionary Computation",
year = "2009",
editor = "Andy Tyrrell",
pages = "-",
address = "Trondheim, Norway",
month = "18-21 " # may,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-2959-2",
file = "P058.pdf",
abstract = "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!",
keywords = "genetic algorithms, genetic programming, EP",
notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and
the IET. IEEE Catalog Number: CFP09ICE-CDR",
}
@InBook{ebert:1998:tmpa,
author = "David S. Ebert and F. Kenton Musgrave and Darwyn
Peachey and Ken Perlin and Steven Worley",
title = "Texturing and Modeling, a Procedural Approach",
chapter = "19",
publisher = "Morgan Kaufmann",
year = "2002",
address = "3",
keywords = "genetic algorithms, genetic programming, genetic
textures",
ISBN = "1-55860-848-6",
notes = "http://www.texturingandmodeling.com/ART/MUSGRAVE/CH19/Chapter19Art.html",
}
@InProceedings{Ebner:1997a,
author = "Marc Ebner",
title = "Evolution of Hierarchical Translation-Invariant
Feature Detectors with an Application to Character
Recognition",
booktitle = "Mustererkennung 1997, 19. DAGM-Symposium",
year = "1997",
editor = "Erwin Paulus and Friedrich M. Wahl",
series = "Informatik Aktuell",
pages = "456--463",
address = "Braunschweig",
publisher_address = "Berlin",
month = "15-17 " # sep,
publisher = "Springer-Verlag",
publisher_address = "Berlin",
keywords = "genetic algorithms, genetic programming, evolution
strategies, structure evolution, feature detection",
ISBN = "3-540-63426-6",
bibsource = "DBLP, http://dblp.uni-trier.de",
URL = "http://wwwi2.informatik.uni-wuerzburg.de/staff/ebner/research/publications/uniTu/evolve.ps.gz",
size = "8 pages",
notes = "
",
}
@InProceedings{Ebner:1997b,
author = "Marc Ebner",
title = "On the Evolution of Edge Detectors for Robot Vision
using Genetic Programming",
booktitle = "Workshop SOAVE '97 - Selbstorganisation von Adaptivem
Verhalten, VDI Reihe 8 Nr. 663",
year = "1997",
pages = "127--134",
editor = "Horst-Michael Gro{\ss}",
address = "D{\"u}sseldorf",
publisher = "VDI Verlag",
keywords = "genetic algorithms, genetic programming, edge
detection",
ISBN = "3-18-366308-2",
URL = "http://www.ra.cs.uni-tuebingen.de/mitarb/ebner/research/publications/uniTu/gpedge.ps.gz",
size = "8 pages",
abstract = "Genetic programming has been applied to the task of
evolving edge detectors... Canny ...",
notes = "
",
}
@InProceedings{ebner:1998:eioGP,
author = "Marc Ebner",
title = "On the Evolution of Interest Operators using Genetic
Programming",
booktitle = "Late Breaking Papers at EuroGP'98: the First European
Workshop on Genetic Programming",
year = "1998",
editor = "Riccardo Poli and W. B. Langdon and Marc Schoenauer
and Terry Fogarty and Wolfgang Banzhaf",
pages = "6--10",
address = "Paris, France",
publisher_address = "School of Computer Science",
month = "14-15 " # apr,
publisher = "CSRP-98-10, The University of Birmingham, UK",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/csrp-98-10.pdf",
URL = "http://www2.informatik.uni-wuerzburg.de/staff/ebner/research/publications/uniTu/gpmoravec.ps.gz",
URL = "ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1998/CSRP-98-10.ps.gz",
URL = "http://citeseer.ist.psu.edu/158450.html",
size = "5 pages",
abstract = "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...",
notes = "EuroGP'98LB part of \cite{Poli:1998:egplb}",
}
@InProceedings{Ebner:1998c,
author = "Marc Ebner",
title = "Evolution of a control architecture for a mobile
robot",
booktitle = "Proceedings of the Second International Conference on
Evolvable Systems: From Biology to Hardware (ICES 98)",
year = "1998",
editor = "Moshe Sipper and Daniel Mange and Andres Perez-Uribe",
volume = "1478",
series = "LNCS",
pages = "303--310",
address = "Lausanne, Switzerland",
publisher_address = "Berlin",
month = "23-25 " # sep,
publisher = "Springer Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-64954-9",
URL = "http://www2.informatik.uni-wuerzburg.de/staff/ebner/research/publications/uniTu/gprealrob.ps.gz",
URL = "http://citeseer.ist.psu.edu/512626.html",
abstract = "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.",
notes = "ICES98",
}
@InProceedings{ebner:1999:eemrl,
author = "Marc Ebner",
title = "Evolving an Environment Model for Robot Localization",
booktitle = "Genetic Programming, Proceedings of EuroGP'99",
year = "1999",
editor = "Riccardo Poli and Peter Nordin and William B. Langdon
and Terence C. Fogarty",
volume = "1598",
series = "LNCS",
pages = "184--192",
address = "Goteborg, Sweden",
publisher_address = "Berlin",
month = "26-27 " # may,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-65899-8",
URL = "http://wwwi2.informatik.uni-wuerzburg.de/mitarbeiter/ebner/research/publications/uniTu/gplocstat.ps.gz",
URL = "http://citeseer.ist.psu.edu/395304.html",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1598&spage=184",
size = "9 pages",
abstract = "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).",
notes = "EuroGP'99, part of \cite{poli:1999:GP}",
}
@InProceedings{ebner:1999:etsio,
author = "Marc Ebner and Andreas Zell",
title = "Evolving a Task Specific Image Operator",
booktitle = "Evolutionary Image Analysis, Signal Processing and
Telecommunications: First European Workshop, EvoIASP'99
and EuroEcTel'99",
year = "1999",
editor = "Riccardo Poli and Hans-Michael Voigt and Stefano
Cagnoni and Dave Corne and George D. Smith and Terence
C. Fogarty",
volume = "1596",
series = "LNCS",
pages = "74--89",
address = "Goteborg, Sweden",
publisher_address = "Berlin",
month = "28-29 " # may,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-65837-8",
URL = "http://www-info2.informatik.uni-wuerzburg.de/mitarbeiter/marc/research/publications/uniTu/gpmoflow.ps.gz",
doi = "doi:10.1007/10704703_6",
URL = "http://citeseer.ist.psu.edu/392593.html",
abstract = "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.",
notes = "EvoIASP99'99",
}
@InProceedings{ebner:1999:EF,
author = "Marc Ebner and Andreas Zell",
title = "Evolving a behavior-based control architecture- From
simulations to the real world",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1009--1014",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-414.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-414.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{ebner:1999:OSSGPIRNSS,
author = "Marc Ebner",
title = "On the Search Space of Genetic Programming and Its
Relation to Nature's Search Space",
booktitle = "Proceedings of the Congress on Evolutionary
Computation",
year = "1999",
editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc
Schoenauer and Xin Yao and Ali Zalzala",
volume = "2",
pages = "1357--1361",
address = "Mayflower Hotel, Washington D.C., USA",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "6-9 " # jul,
organisation = "Congress on Evolutionary Computation, IEEE / Neural
Networks Council, Evolutionary Programming Society,
Galesia, IEE",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, models of
evolutionary computation",
ISBN = "0-7803-5536-9 (softbound)",
ISBN = "0-7803-5537-7 (Microfiche)",
notes = "CEC-99 - A joint meeting of the IEEE, Evolutionary
Programming Society, Galesia, and the IEE.
Library of Congress Number = 99-61143",
}
@PhdThesis{ebner:thesis,
author = "Marc Ebner",
title = "Steuerung eines mobilen Roboters mit evolvierten
Merkmalsdetektoren",
school = "Eberhard-Karls-Universit{\"{a}}t T{\"{a}}bingen",
year = "1999",
keywords = "genetic algorithms, genetic programming, computer
vision, biologically inspired systems",
URL = "http://www2.informatik.uni-wuerzburg.de/staff/ebner/research/publications/uniTu/diss.ps.gz",
size = "10426344",
}
@InProceedings{ebner:2001:EuroGP,
author = "Marc Ebner",
title = "Evolving Color Constancy for an Artificial Retina",
booktitle = "Genetic Programming, Proceedings of EuroGP'2001",
year = "2001",
editor = "Julian F. Miller and Marco Tomassini and Pier Luca
Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
William B. Langdon",
volume = "2038",
series = "LNCS",
pages = "11--22",
address = "Lake Como, Italy",
publisher_address = "Berlin",
month = "18-20 " # apr,
organisation = "EvoNET",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, Color
Constancy, Artificial Retina, Image Processing",
ISBN = "3-540-41899-7",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=11",
size = "12 pages",
abstract = "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.",
notes = "EuroGP'2001, part of \cite{miller:2001:gp}",
}
@InProceedings{Ebner:2001:ECAL,
author = "Marc Ebner",
title = "A Three-Dimensional Environment for Self-Reproducing
Programs",
booktitle = "Advances in Artificial Life, Proceedings 6th European
Conference, ECAL 2001",
year = "2001",
editor = "Jozef Kelemen and Petr Sosik",
volume = "2159",
series = "Lecture Notes in Computer Science",
pages = "306--315",
address = "Prague, Czech Republic",
month = sep # " 10-14",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming,
self-reproducing programs, artificial life",
ISBN = "3-540-42567-5",
URL = "http://wwwi2.informatik.uni-wuerzburg.de/staff/ebner/research/publications/uniWu/selfRep.ps.gz",
URL = "http://link.springer.de/link/service/series/0558/bibs/2159/21590306.htm",
size = "10 pages",
abstract = "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.",
}
@InProceedings{ebner:2002:EuroGP,
title = "Coevolution Produces an Arms Race Among Virtual
Plants",
author = "Marc Ebner and Adrian Grigore and Alexander Heffner
and J{\"u}rgen Albert",
editor = "James A. Foster and Evelyne Lutton and Julian Miller
and Conor Ryan and Andrea G. B. Tettamanzi",
booktitle = "Genetic Programming, Proceedings of the 5th European
Conference, EuroGP 2002",
volume = "2278",
series = "LNCS",
pages = "316--325",
publisher = "Springer-Verlag",
address = "Kinsale, Ireland",
publisher_address = "Berlin",
month = "3-5 " # apr,
year = "2002",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-43378-3",
URL = "http://wwwi2.informatik.uni-wuerzburg.de/mitarbeiter/ebner/research/publications/uniWu/evoPlant.ps.gz",
abstract = "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.",
notes = "EuroGP'2002, part of \cite{lutton:2002:GP}",
}
@InProceedings{ebner03,
author = "Marc Ebner",
title = "Evolutionary Design of Objects Using Scene Graphs",
booktitle = "Genetic Programming, Proceedings of EuroGP'2003",
year = "2003",
editor = "Conor Ryan and Terence Soule and Maarten Keijzer and
Edward Tsang and Riccardo Poli and Ernesto Costa",
volume = "2610",
series = "LNCS",
pages = "47--58",
address = "Essex",
publisher_address = "Berlin",
month = "14-16 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-00971-X",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=47",
abstract = "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.",
notes = "EuroGP'2003 held in conjunction with EvoWorkshops
2003
overview
http://wwwi2.informatik.uni-wuerzburg.de/mitarbeiter/ebner/research/evoRotor/evoRotor.html",
}
@InProceedings{Ebner:2003:ECAL,
author = "Marc Ebner",
title = "Evolution and Growth of Virtual Plants",
booktitle = "Advances in Artificial Life. 7th European Conference
on Artificial Life",
year = "2003",
editor = "Wolfgang Banzhaf and Thomas Christaller and Peter
Dittrich and Jan T. Kim and Jens Ziegler",
volume = "2801",
series = "Lecture Notes in Artificial Intelligence",
pages = "228--237",
address = "Dortmund, Germany",
month = "14-17 " # sep,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, virtual
plants, L-systems, co-evolution",
ISBN = "3-540-20057-6",
URL = "http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=2801&spage=228",
doi = "DOI:10.1007/b12035",
abstract = "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.",
notes = "ECAL-2003",
}
@Article{ebner:2003:GPEM,
author = "Marc Ebner",
title = "Book Review: {Illustrating} Evolutionary Computation
with Mathematica",
journal = "Genetic Programming and Evolvable Machines",
year = "2003",
volume = "4",
number = "3",
pages = "291--294",
month = sep,
keywords = "genetic algorithms, genetic programming",
ISSN = "1389-2576",
doi = "doi:10.1023/A:1025180508687",
notes = "Review of \cite{jacob:2001:iecm} Article ID: 5141126",
}
@InProceedings{eurogp:EbnerRA05,
author = "Marc Ebner and Markus Reinhardt and J{\"u}rgen
Albert",
editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre
Collet and Jano I. {van Hemert} and Marco Tomassini",
title = "Evolution of Vertex and Pixel Shaders",
booktitle = "Proceedings of the 8th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3447",
year = "2005",
address = "Lausanne, Switzerland",
month = "30 " # mar # " - 1 " # apr,
organisation = "EvoNet",
keywords = "genetic algorithms, genetic programming, GPU, linear
GP",
ISBN = "3-540-25436-6",
pages = "261--270",
URL = "http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=261",
doi = "doi:10.1007/b107383",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
notes = "Part of \cite{keijzer: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",
}
@Article{Ebner:2006:GPEM,
author = "Marc Ebner",
title = "Coevolution and the Red Queen effect shape virtual
plants",
journal = "Genetic Programming and Evolvable Machines",
year = "2006",
volume = "7",
number = "1",
pages = "103--123",
month = mar,
keywords = "genetic algorithms, genetic programming, Red Queen
effect, Coevolution, Lindenmayer systems, Artificial
plants",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-006-7013-2",
size = "21 pages",
abstract = "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.",
notes = "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.",
}
@Article{Ebner:2006:PRL,
author = "Marc Ebner",
title = "Evolving color constancy",
journal = "Pattern Recognition Letters",
year = "2006",
volume = "27",
number = "11",
pages = "1220--1229",
month = aug,
note = "Evolutionary Computer Vision and Image Understanding",
keywords = "genetic algorithms, genetic programming, Colour
constancy, Local space average colour",
doi = "doi:10.1016/j.patrec.2005.07.020",
abstract = "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.",
}
@Proceedings{ebner:2007:GP,
title = "Proceedings of the 10th European Conference on Genetic
Programming",
year = "2007",
editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and
Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar",
series = "Lecture Notes in Computer Science",
volume = "4445",
address = "Valencia, Spain",
publisher_address = "Berlin Heidelberg NewYork",
month = "11-13 " # apr,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
ISSN = "0302-9743",
ISBN = "3-540-71602-5",
isbn13 = "978-3-540-71602-0",
URL = "http://www.springerlink.com/content/978-3-540-71602-0/",
doi = "doi:10.1007/978-3-540-71605-1",
size = "390 pages",
notes = "EuroGP'2007 held in conjunction with EvoCOP2007,
EvoBIO2007 and EvoWorkshops2007",
}
@InBook{Ebner:2007:inCC,
author = "Marc Ebner",
title = "Color Constancy",
pages = "198--204",
publisher = "John Wiley \& Sons",
year = "2007",
series = "Imaging Science and Technology",
edition = "1",
month = apr,
keywords = "genetic algorithms, genetic programming",
ISBN = "0-470-05829-3",
}
@InProceedings{conf/eurogp/Ebner08,
title = "A Genetic Programming Approach to Deriving the
Spectral Sensitivity of an Optical System",
author = "Marc Ebner",
bibdate = "2008-04-15",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#Ebner08",
booktitle = "Proceedings of the 11th European Conference on Genetic
Programming, EuroGP 2008",
address = "Naples",
month = "26-28 " # mar,
publisher = "Springer",
year = "2008",
volume = "4971",
editor = "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",
isbn13 = "978-3-540-78670-2",
pages = "61--72",
series = "Lecture Notes in Computer Science",
doi = "doi:10.1007/978-3-540-78671-9_6",
keywords = "genetic algorithms, genetic programming",
notes = "Part of \cite{conf/eurogp/2008} EuroGP'2008 held in
conjunction with EvoCOP2008, EvoBIO2008 and
EvoWorkshops2008",
}
@InProceedings{Ebner:2008:SASOW,
author = "Marc Ebner",
title = "An Adaptive On-Line Evolutionary Visual System",
booktitle = "Second IEEE International Conference on Self-Adaptive
and Self-Organizing Systems Workshops, SASOW 2008",
year = "2008",
editor = "E. Hart and B. Paechter and J. Willies",
pages = "84--89",
address = "Venice",
month = "20-24 " # oct,
organisation = "IEEE",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, GPU, adaptive
online evolutionary visual system, evolutionary
computer vision, training images, adaptive systems,
computer vision, evolutionary computation",
doi = "doi:10.1109/SASOW.2008.18",
size = "6 pages",
abstract = "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.",
notes = "Workshop on Pervasive Adaptation. Also known as
\cite{4800658}",
}
@InProceedings{Ebner:2009:eurogp,
author = "Marc Ebner",
title = "A Real-Time Evolutionary Object Recognition System",
booktitle = "Proceedings of the 12th European Conference on Genetic
Programming, EuroGP 2009",
year = "2009",
editor = "Leonardo Vanneschi and Steven Gustafson and Alberto
Moraglio and Ivanoe {De Falco} and Marc Ebner",
volume = "5481",
series = "LNCS",
pages = "268--279",
address = "Tuebingen",
month = apr # " 15-17",
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, poster",
isbn13 = "978-3-642-01180-1",
doi = "doi:10.1007/978-3-642-01181-8_23",
notes = "Part of \cite{conf/eurogp/2009} EuroGP'2009 held in
conjunction with EvoCOP2009, EvoBIO2009 and
EvoWorkshops2009",
}
@InProceedings{Ebner:2009:CIG,
author = "Marc Ebner and Thorsten Tiede",
title = "Evolving driving controllers using Genetic
Programming",
booktitle = "IEEE Symposium on Computational Intelligence and
Games, CIG 2009",
year = "2009",
month = sep,
pages = "279--286",
keywords = "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",
abstract = "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.",
doi = "doi:10.1109/CIG.2009.5286465",
notes = "Also known as \cite{5286465}",
}
@InProceedings{DBLP:conf/acivs/Ebner09,
author = "Marc Ebner",
title = "Engineering of Computer Vision Algorithms Using
Evolutionary Algorithms",
booktitle = "Proceedings of the 11th International Conference on
Advanced Concepts for Intelligent Vision Systems, ACIVS
2009",
year = "2009",
editor = "Jacques Blanc-Talon and Wilfried Philips and Dan
Popescu and Paul Scheunders",
series = "Lecture Notes in Computer Science",
volume = "5807",
pages = "367--378",
address = "Bordeaux, France",
month = sep # " 28-" # oct # " 2",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, Cartesian
genetic programming, GPU, OpenGLSL",
isbn13 = "978-3-642-04696-4",
URL = "http://www.ra.cs.uni-tuebingen.de/mitarb/ebner/research/publications/uniTu2/EvoCVengineering.pdf",
doi = "doi:10.1007/978-3-642-04697-1_34",
size = "12 pages",
abstract = "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.",
notes = "Interactive evolution of image processing software.
Realtime 30 seconds. OpenGL shader language. mip
mapping. nVidia GeForce 9600 GT/PCI/SEE2
",
}
@InProceedings{Ebner:2010:EvoIASP,
author = "Marc Ebner",
title = "Towards Automated Learning of Object Detectors",
booktitle = "EvoIASP",
year = "2010",
editor = "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",
volume = "6024",
series = "LNCS",
pages = "231--240",
address = "Istanbul",
month = "7-9 " # apr,
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, cartesian
genetic programming",
isbn13 = "978-3-642-12238-5",
doi = "doi:10.1007/978-3-642-12239-2_24",
abstract = "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.",
notes = "EvoIASP'2010 held in conjunction with EuroGP'2010
EvoCOP2010 EvoBIO2010",
}
@InProceedings{Ebner:2010b,
author = "Marc Ebner",
title = "Evolving Object Detectors with a {GPU} Accelerated
Vision System",
booktitle = "Proceedings of the 9th International Conference
Evolvable Systems: From Biology to Hardware, ICES
2010",
year = "2010",
editor = "Gianluca Tempesti and Andy M. Tyrrell and Julian F.
Miller",
series = "Lecture Notes in Computer Science",
volume = "6274",
pages = "109--120",
address = "York",
month = sep # " 6-8",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-15322-8",
doi = "doi:10.1007/978-3-642-15323-5_10",
abstract = "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.",
affiliation = "Wilhelm-Schickard-Institut fur Informatik,
Eberhard-Karls-Universitat Tuebingen, Abt.
Rechnerarchitektur, Sand 1, 72076 Tbingen, Germany",
}
@InCollection{Ebstyne:1997:msm,
author = "Michael J. Ebstyne",
title = "Musings on Syncopation and Machines",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1997",
publisher = "Stanford Bookstore",
year = "1997",
editor = "John R. Koza",
pages = "36--46",
address = "Stanford, California, 94305-3079 USA",
month = "17 " # mar,
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-205981-2",
abstract = "music",
notes = "part of \cite{koza:1997:GAGPs}",
}
@InProceedings{edelson:1999:ECCFIGCUASIFPM,
author = "William Edelson and Michael L. Gargano",
title = "Efficient Calculation of Compute-Intensive Fitness In
Genetic Computations Using {A} Survival Indicator For
Population Members",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "784",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, classifier
systems, poster papers",
ISBN = "1-55860-611-4",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{edmonds:1995:fuzzy,
author = "Andrew N. Edmonds and Diana Burkhardt and Osei Adjei",
title = "Genetic Programming of Fuzzy Logic Production Rules",
booktitle = "1995 IEEE Conference on Evolutionary Computation",
year = "1995",
volume = "2",
pages = "765",
address = "Perth, Australia",
publisher_address = "Piscataway, NJ, USA",
month = "29 " # nov # " - 1 " # dec,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
URL = "ftp://ftp.scifi.co.uk/pub/docs/ICECPS.z broken",
URL = "http://www.scientio.com/resources/NNCM95.pdf",
URL = "http://ieeexplore.ieee.org/iel2/3507/10438/00487482.pdf",
abstract = "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.",
notes = "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?",
}
@InCollection{edmonds:1997:mbrea,
author = "Bruce Edmonds and Scott Moss",
title = "Modelling of Boundedly Rational Agents using
Evolutionary Programming Techniques",
booktitle = "Evolutionary Computing",
publisher = "Springer-Verlag",
year = "1997",
editor = "David Corne and Jonathan L. Shapiro",
volume = "1305",
series = "LNCS",
pages = "31--42",
address = "University of Manchester, UK",
month = "7-8 " # apr,
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-63476-2",
URL = "http://cogprints.ecs.soton.ac.uk/archive/00000509/",
URL = "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-63476-2",
abstract = "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.",
notes = "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",
size = "12 pages",
}
@TechReport{edmonds:1998:mGPcov,
author = "Bruce Edmonds",
title = "Meta-Genetic Programming: Co-evolving the Operators of
Variation",
institution = "Centre for Policy Modelling, Manchester Metropolitan
University, UK",
year = "1998",
type = "CPM Report",
number = "98-32",
address = "Aytoun St., Manchester, M1 3GH. UK",
month = jan,
keywords = "genetic algorithms, genetic programming, automatic
programming, genetic operators, co-evolution",
URL = "http://cogprints.org/513/00/mgp.pdf",
URL = "http://cogprints.ecs.soton.ac.uk/archive/00000513/",
URL = "http://www.cpm.mmu.ac.uk/cpmrep32.html",
abstract = "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.",
notes = "see \cite{edmonds:2001:mGPcov}",
size = "18 pages",
}
@InProceedings{edmonds:1998:gsrefb,
author = "Bruce Edmonds",
title = "Gossip, Sexual Recombination and the {El Farol Bar:}
modelling the emergence of heterogeneity",
booktitle = "Proceedings of the 1998 Conference on Computation in
Economics, Finance and Engineering",
year = "1998",
address = "Cambridge",
month = jun,
keywords = "genetic algorithms, genetic programming",
URL = "http://cogprints.ecs.soton.ac.uk/archive/00000514/",
abstract = "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.",
notes = "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 \cite{edmonds:1999:gsrefb}",
}
@Article{edmonds:1998:GP5,
author = "Bruce Edmonds",
title = "The Uses of Genetic Programming in Social Simulation:
{A} Review of Five Books",
journal = "Journal of Artificial Societies and Social
Simulation",
year = "1998",
volume = "1",
number = "4",
month = "31-" # oct,
note = "Book review",
keywords = "genetic algorithms, genetic programming",
URL = "http://jasss.soc.surrey.ac.uk/2/1/review1.html",
abstract = "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",
}
@Article{edmonds:1999:gsrefb,
author = "Bruce Edmonds",
title = "Gossip, Sexual Recombination and the {El Farol} bar:
modelling the emergence of heterogeneity",
journal = "Journal of Artificial Societies and Social
Simulation",
year = "1999",
volume = "2",
number = "3",
keywords = "genetic algorithms, genetic programming,
differentiation, El Farol, evolution, co-evolution,
emergence, heterogeneity, society, roles, social
structure, SDML, naming, creativity",
URL = "http://www.soc.surrey.ac.uk/JASSS/2/3/2.htm",
URL = "http://cogprints.ecs.soton.ac.uk/archive/00001775/",
size = "7 pages",
abstract = "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.",
notes = "See also \cite{edmonds:1998:gsrefb}",
}
@Article{edmonds:1999:r5GP,
author = "Bruce Edmonds",
title = "The Uses of Genetic Programming in Social Simulation:
{A} Review of Five Books",
journal = "The Journal of Artificial Societies and Social
Simulation",
year = "1999",
volume = "2",
number = "1",
month = jan,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.soc.surrey.ac.uk/JASSS/2/1/review1.html",
size = "40957 bytes",
abstract = "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 \cite{koza:book}
Genetic Programming II: Automatic Discovery of Reusable
Programs John R. Koza Cambridge, MA: The M.I.T. Press,
A Bradford Book 1994 \cite{koza:gp2}
Advances in Genetic Programming Edited by Kenneth E.
Kinnear Jr. Cambridge, MA: The M.I.T. Press, A Bradford
Book 1994 \cite{kinnear: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
\cite{book:1996:aigp2}
Genetic Programming and Data Structures William B.
Langdon Dordrecht: Kluwer Academic Publishers 1998
\cite{langdon:book}",
notes = "JASSS",
}
@Article{edmonds:2000:aigp,
author = "Bruce Edmonds",
title = "A Review of the ``Advances in Genetic Programming''
Series (Volumes 1, 2 and 3)",
journal = "Genetic Programming and Evolvable Machines",
year = "2000",
volume = "1",
number = "3",
pages = "289--296",
month = jul,
keywords = "genetic algorithms, genetic programming",
ISSN = "1389-2576",
doi = "doi:10.1023/A:1010018414986",
notes = "\cite{kinnear:book} \cite{book:1996:aigp2}
\cite{book:1999:aigp3} Article ID: 264705",
}
@InCollection{edmonds:2001:MUC,
author = "Bruce Edmonds",
title = "Learning Appropriate Contexts",
booktitle = "Modelling and Using Context: Third International and
Interdisciplinary Conference, CONTEXT",
publisher = "Springer-Verlag",
year = "2001",
editor = "Varol Akman and Paolo Bouquet and Richard Thomason and
Roger Young",
volume = "2116",
series = "LNAI",
pages = "143--155",
address = "Dundee, UK",
publisher_address = "Berlin / Heidelberg",
month = "27-30 " # jul,
email = "b.edmonds@mmu.ac.uk",
keywords = "genetic algorithms, genetic programming, learning,
conditions of application, context, evolutionary
computing, error",
ISBN = "3-540-42379-6",
URL = "http://cogprints.ecs.soton.ac.uk/archive/00001772/",
URL = "http://www.cpm.mmu.ac.uk/cpmrep78.html",
URL = "http://cfpm.org/pub/papers/lac.pdf",
URL = "http://citeseer.ist.psu.edu/534909.html",
size = "13 pages",
ISSN = "0302-9743",
URL = "http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=2116&spage=143",
abstract = "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.",
notes = "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 \cite{ulgtsdl}. 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 \cite{Edmonds:2001:CONTEXT}
combined with edmonds:2001:MUC",
}
@InProceedings{Edmonds:2001:IRC,
author = "Bruce Edmonds and Scott Moss",
title = "The Importance of Representing Cognitive Processes in
Multi-agent Models",
booktitle = "Artificial Neural Networks - ICANN 2001 :
International Conference, Proceedings",
year = "2001",
editor = "G. Dorffner and H. Bischof and K. Hornik",
volume = "2130",
series = "Lecture Notes in Computer Science",
pages = "759--766",
address = "Vienna, Austria",
month = aug # " 21-25",
keywords = "genetic algorithms, genetic programming, modelling,
methodology, agent, economics, neural net,
representation, prediction, explanation, cognition,
stock market, negotiation",
CODEN = "LNCSD9",
ISSN = "0302-9743",
bibdate = "Sat Feb 2 13:05:31 MST 2002",
URL = "http://cfpm.org/pub/papers/repcog.pdf",
URL = "http://citeseer.ist.psu.edu/540672.html",
URL = "http://link.springer-ny.com/link/service/series/0558/bibs/2130/21300759.htm",
URL = "http://link.springer-ny.com/link/service/series/0558/papers/2130/21300759.pdf",
acknowledgement = ack-nhfb,
abstract = "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.",
}
@Article{edmonds:2001:mGPcov,
author = "Bruce Edmonds",
title = "Meta-Genetic Programming: Co-evolving the Operators of
Variation",
journal = "Elektrik",
year = "2001",
volume = "9",
number = "1",
pages = "13--29",
month = may,
note = "Turkish Journal Electrical Engineering and Computer
Sciences",
keywords = "genetic algorithms, genetic programming, automatic
programming, genetic operators, co-evolution",
ISSN = "1300-0632",
URL = "http://cogprints.ecs.soton.ac.uk/archive/00001776/",
URL = "http://journals.tubitak.gov.tr/elektrik/issues/elk-01-9-1/elk-9-1-2-0008-5.pdf",
URL = "http://cogprints.ecs.soton.ac.uk/archive/00001776/00/mgp.pdf",
size = "18 pages",
abstract = "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.",
notes = "Elektrik http://www.tubitak.gov.tr/journals/elektrik/
see \cite{edmonds:1998:mGPcov}
",
}
@TechReport{ulgtsdl,
author = "Bruce Edmonds",
title = "Using Localised 'Gossip' to Structure Distributed
Learning",
institution = "Centre for Policy Modelling, Manchester Metropolitan
University Business School",
year = "2005",
type = "CPM Report",
number = "CPM-04-142",
address = "UK",
month = "15th " # may,
keywords = "genetic algorithms, genetic programming",
URL = "http://bruce.edmonds.name/ulgtsdl/ulgtsdl.pdf",
URL = "http://cfpm.org/cpmrep142.html",
abstract = "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.",
notes = "Presented at the {"}Engineering with Social
Metaphors{"} day of the AISB Symposium on Socially
Inspired Computing, University of Hertfordship, April
2005. \cite{edmonds:2005:esm}",
size = "12 pages",
notes = "
'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.",
}
@InProceedings{edmonds:2005:esm,
author = "Bruce Edmonds",
title = "Using Localised 'Gossip' to Structure Distributed
Learning",
booktitle = "AISB'05: Proceedings of the Joint Symposium on
Socially Inspired Computing (Engineering with Social
Metaphors)",
year = "2005",
editor = "Bruce Edmonds and Nigel Gilbert and Steven Gustafson
and David Hales and Natalio Krasnogor",
pages = "127--134",
address = "University of Hertfordshire, Hatfield, UK",
month = "12-15 " # apr,
organisation = "AISB",
note = "SSAISB 2005 Convention",
keywords = "genetic algorithms, genetic programming",
size = "8 pages",
notes = "see also CPM rep 142 \cite{ulgtsdl}. 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/",
}
@Article{Edmondson:2010:IJCNDS,
author = "James Edmondson and Douglas Schmidt",
title = "Multi-agent distributed adaptive resource allocation
({MADARA})",
journal = "International Journal of Communication Networks and
Distributed Systems",
year = "2010",
volume = "5",
number = "3",
pages = "229--245",
keywords = "genetic algorithms, genetic programming",
ISSN = "1754-3924",
URL = "http://www.inderscience.com/link.php?id=34946",
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.150.5912",
bibsource = "OAI-PMH server at www.inderscience.com",
language = "eng",
rights = "Inderscience Copyright",
abstract = "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.",
}
@Misc{Edvardsen:undergraduatethesis,
author = "Stian Edvardsen",
title = "Classification of Images using Color, {CBIR} Distance
Measures and Genetic Programming: An evolutionary
Experiment",
howpublished = "Undergraduate Theses from Norwegian University of
Science and Technology. Faculty of Information
Technology, Mathematics and Electrical Engineering,
Department of Computer and Information Science",
year = "2006",
month = jun,
type = "Undergraduate thesis Masteroppgave-level",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.diva-portal.org/diva/getDocument?urn_nbn_no_ntnu_diva-1132-1__fulltext.pdf",
URL = "http://urn.ub.uu.se/resolve?urn=urn:nbn:no:ntnu:diva-1132",
size = "151 pages",
}
@Article{edwards:1995:nature,
author = "A. W. F. Edwards",
title = "Forced Evolution",
journal = "Nature",
year = "1995",
volume = "375",
pages = "11",
month = "6 " # jul,
notes = "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.",
}
@InProceedings{eggermont:1999:affGPdm,
author = "J. Eggermont and A. E. Eiben and J. I. {van Hemert}",
title = "Adapting the Fitness Function in {GP} for Data
Mining",
booktitle = "Genetic Programming, Proceedings of EuroGP'99",
year = "1999",
editor = "Riccardo Poli and Peter Nordin and William B. Langdon
and Terence C. Fogarty",
volume = "1598",
series = "LNCS",
pages = "193--202",
address = "Goteborg, Sweden",
publisher_address = "Berlin",
month = "26-27 " # may,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, data mining",
ISBN = "3-540-65899-8",
URL = "http://www.liacs.nl/~jeggermo/publications/eurogp99.ps.gz",
URL = "http://www.vanhemert.co.uk/publications/eurogp99.Adapting_the_fitness_function_in_GP_for_data_mining.ps.gz",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1598&spage=193",
abstract = "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.",
notes = "EuroGP'99, part of \cite{poli:1999:GP}",
}
@InProceedings{EEH99b,
author = "Jeroen Eggermont and Agoston E. Eiben and Jano I. {van
Hemert}",
title = "A comparison of genetic programming variants for data
classification",
booktitle = "Advances in Intelligent Data Analysis, Third
International Symposium, IDA-99",
year = "1999",
editor = "David J. Hand and Joost N. Kok and Michael R.
Berthold",
volume = "1642",
series = "LNCS",
email = "jvhemert@cs.leidenuniv.nl",
pages = "281--290",
address = "Amsterdam, The Netherlands",
publisher_address = "Berlin",
month = "9--11 " # aug,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming,
classification, data mining",
URL = "http://www.liacs.nl/~jeggermo/publications/ida99.ps.gz",
URL = "http://www.vanhemert.co.uk/publications/ida99.A_comparison_of_genetic_programming_variants_for_data_classification.ps.gz",
ISBN = "3-540-66332-0",
abstract = "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.",
notes = "IDA-99, Booleanization of inputs, ML: Australian
credit, German Credit, Heart Disease, Pima. steady
state. SAW-ing",
}
@InProceedings{EEH99bnaic,
author = "J. Eggermont and A. E. Eiben and J. I. {van Hemert}",
title = "A comparison of genetic programming variants for data
classification",
booktitle = "Proceedings of the Eleventh Belgium/Netherlands
Conference on Artificial Intelligence (BNAIC'99)",
year = "1999",
editor = "Eric Postma and Marc Gyssens",
pages = "253--254",
address = "Kasteel Vaeshartelt, Maastricht, Holland",
month = "3-4 " # nov,
organisation = "BNVKI, Dutch and the Belgian AI Association",
keywords = "genetic algorithms, genetic programming, data mining,
classification",
URL = "http://www.liacs.nl/~jeggermo/publications/bnaic00.ps.gz",
URL = "http://www.vanhemert.co.uk/publications/bnaic99.shortpaper.Comparing_genetic_programming_variants_for_data_classification.ps.gz",
size = "2 pages",
abstract = "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",
notes = "resubmission of \cite{EEH99b}
http://www.cs.unimaas.nl/~bnvki/",
}
@InProceedings{eggermon:2000:bnaic,
author = "J. Eggermont and J. I. {van Hemert}",
title = "Stepwise Adaptation of Weights for Symbolic Regression
with Genetic Programming",
booktitle = "Proceedings of the Twelveth Belgium/Netherlands
Conference on Artificial Intelligence (BNAIC'00)",
year = "2000",
editor = "Antal {van den Bosch} and Hans Weigand",
pages = "259--266",
address = "De Efteling, Kaatsheuvel, Holland",
month = "1-2 " # nov,
organisation = "BNVKI, Dutch and the Belgian AI Association",
keywords = "genetic algorithms, genetic programming, data mining",
URL = "http://www.liacs.nl/~jeggermo/publications/bnaic00.ps.gz",
URL = "http://www.vanhemert.co.uk/publications/bnaic00.Stepwise_Adaptation_of_Weights_for_Symbolic_Regression_with_Genetic_Programming.ps.gz",
URL = "http://www.vanhemert.co.uk/publications/bnaic00.Stepwise_Adaptation_of_Weights_for_Symbolic_Regression_with_Genetic_Programming.pdf",
URL = "http://citeseer.ist.psu.edu/374087.html",
abstract = "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.",
}
@InProceedings{eggermont_adaptive:2001:EuroGP,
author = "Jeroen Eggermont and Jano I. {van Hemert}",
title = "Adaptive Genetic Programming Applied to New and
Existing Simple Regression Problems",
booktitle = "Genetic Programming, Proceedings of EuroGP'2001",
year = "2001",
editor = "Julian F. Miller and Marco Tomassini and Pier Luca
Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
William B. Langdon",
volume = "2038",
series = "LNCS",
pages = "23--35",
address = "Lake Como, Italy",
publisher_address = "Berlin",
month = "18-20 " # apr,
organisation = "EvoNET",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, Adaptation,
Symbolic Regression, Problem Generator, Program Trees,
data mining",
ISBN = "3-540-41899-7",
URL = "http://www.liacs.nl/~jeggermo/publications/eurogp2001-symreg.ps.gz",
URL = "http://www.vanhemert.co.uk/publications/eurogp2001.Adaptive_Genetic_Programming_Applied_to_New_and_Existing_Simple_Regression_Problems.ps.gz",
URL = "http://www.vanhemert.co.uk/publications/eurogp2001.Adaptive_Genetic_Programming_Applied_to_New_and_Existing_Simple_Regression_Problems.pdf",
URL = "http://link.springer.de/link/service/series/0558/papers/2038/20380023.pdf",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=23",
size = "13 pages",
abstract = "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.",
notes = "EuroGP'2001, part of \cite{miller:2001:gp}",
}
@InProceedings{eggermont:2001:EuroGP_dead,
author = "Jeroen Eggermont and Tom Lenaerts and Sanna Poyhonen
and Alexandre Termier",
title = "Raising the Dead; Extending Evolutionary Algorithms
with a Case-based Memory",
booktitle = "Genetic Programming, Proceedings of EuroGP'2001",
year = "2001",
editor = "Julian F. Miller and Marco Tomassini and Pier Luca
Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
William B. Langdon",
volume = "2038",
series = "LNCS",
pages = "280--290",
address = "Lake Como, Italy",
publisher_address = "Berlin",
month = "18-20 " # apr,
organisation = "EvoNET",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, Dynamic
Fitness, Global Memory",
ISBN = "3-540-41899-7",
URL = "http://www.liacs.nl/~jeggermo/publications/eurogp2001-dynamic.ps.gz",
URL = "http://www.lri.fr/~termier/publis/eurogp2001-dynamic.ps.gz",
URL = "http://link.springer.de/link/service/series/0558/papers/2038/20380280.pdf",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=280",
size = "11 pages",
abstract = "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.",
notes = "EuroGP'2001, part of \cite{miller:2001:gp}",
}
@InProceedings{eggermont:2002:EuroGP,
title = "Evolving Fuzzy Decision Trees with Genetic Programming
and Clustering",
author = "Jeroen Eggermont",
editor = "James A. Foster and Evelyne Lutton and Julian Miller
and Conor Ryan and Andrea G. B. Tettamanzi",
booktitle = "Genetic Programming, Proceedings of the 5th European
Conference, EuroGP 2002",
volume = "2278",
series = "LNCS",
pages = "71--82",
publisher = "Springer-Verlag",
address = "Kinsale, Ireland",
publisher_address = "Berlin",
month = "3-5 " # apr,
year = "2002",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-43378-3",
URL = "http://www.liacs.nl/~jeggermo/publications/eurogp2002.ps.gz",
URL = "http://link.springer-ny.com/link/service/series/0558/bibs/2278/22780071.htm",
URL = "http://link.springer-ny.com/link/service/series/0558/papers/2278/22780071.pdf",
abstract = "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.",
notes = "EuroGP'2002, part of \cite{lutton:2002:GP}",
}
@InProceedings{E02b,
author = "J. Eggermont",
title = "Evolving Fuzzy Decision Trees for Data
Classification",
booktitle = "Proceedings of the 14th Belgium/Netherlands Conference
on Artificial Intelligence (BNAIC'02)",
year = "2002",
editor = "Hendrik Blockeel and Marc Denecker",
address = "Leuven, Belgium",
month = "21-22 " # oct,
organisation = "BNVKI, Dutch and the Belgian AI Association",
keywords = "genetic algorithms, genetic programming",
size = "2 pages",
notes = "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).",
}
@InProceedings{EL02,
author = "J. Eggermont and T. Lenaerts",
title = "Dynamic Optimization using Evolutionary Algorithms
with a Case-based Memory",
booktitle = "Proceedings of the 14th Belgium/Netherlands Conference
on Artificial Intelligence (BNAIC'02)",
year = "2002",
editor = "Hendrik Blockeel and Marc Denecker",
address = "Leuven, Belgium",
month = "21-22 " # oct,
organisation = "BNVKI, Dutch and the Belgian AI Association",
keywords = "genetic algorithms, genetic programming, evolutionary
algorithms",
URL = "http://www.liacs.nl/~jeggermo/publications/bnaic02-dynamic.ps.gz",
size = "8 pages",
abstract = "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.",
notes = "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).",
}
@InProceedings{EKK04,
author = "J. Eggermont and J. N. Kok and W. A. Kosters",
title = "Genetic Programming for Data Classification:
{P}artitioning the Search Space",
booktitle = "Proceedings of the 2004 Symposium on Applied Computing
(ACM SAC'04)",
year = "2004",
pages = "1001--1005",
address = "Nicosia, Cyprus",
month = "14-17 " # mar,
organisation = "ACM",
keywords = "genetic algorithms, genetic programming, data
classification",
URL = "http://www.liacs.nl/~kosters/SAC2003final.pdf",
size = "5 pages",
abstract = "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.",
}
@InProceedings{eggermont:2003:bnaic,
author = "J. Eggermont and J. N. Kok and W. A. Kosters",
title = "Genetic Programming for Data Classification: Refining
the Search Space",
booktitle = "Proceedings of the Fivteenth Belgium/Netherlands
Conference on Artificial Intelligence (BNAIC'03)",
year = "2003",
editor = "T. Heskes and P. Lucas and L. Vuurpijl and W.
Wiegerinck",
pages = "123--130",
address = "Nijmegen, The Netherlands",
month = "23-24 " # oct,
organisation = "BNVKI, Dutch and the Belgian AI Association",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.liacs.nl/home/kosters/bnaic03-eggermont.ps",
size = "8 pages",
notes = "C4.5 ID3",
}
@InProceedings{eggermont:2004:sac,
author = "J. Eggermont and J. N. Kok and W. A. Kosters",
title = "Genetic Programming for Data Classification:
Partitioning the Search Space",
booktitle = "Proceedings of the 2004 Symposium on applied computing
(ACM SAC'04)",
year = "2004",
pages = "1001--1005",
address = "Nicosia, Cyprus",
month = "14-17 " # mar,
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{Eggermont:PPSN:2004,
author = "Jeroen Eggermont and Joost N. Kok and Walter A.
Kosters",
title = "Detecting and Pruning Introns for Faster Decision Tree
Evolution",
booktitle = "Parallel Problem Solving from Nature - PPSN VIII",
year = "2004",
editor = "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\v{n}o Ata
Kab\'an and Hans-Paul Schwefel",
volume = "3242",
pages = "1071--1080",
series = "LNCS",
address = "Birmingham, UK",
publisher_address = "Berlin",
month = "18-22 " # sep,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-23092-0",
URL = "http://www.liacs.nl/~kosters/ppsn8/ppsn2004.pdf",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3242&spage=1071",
doi = "doi:10.1007/b100601",
size = "10 pages",
abstract = "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.",
notes = "PPSN-VIII",
}
@PhdThesis{eggermont:thesis,
author = "Jeroen Eggermont",
title = "Data Mining using Genetic Programming: Classification
and Symbolic Regression",
school = "Institute for Programming research and Algorithmics,
Leiden Institute of Advanced Computer Science, Faculty
of Mathematics \& Natural Sciences, Leiden University",
year = "2005",
address = "The Netherlands",
month = "14 " # sep,
bibsource = "OAI-PMH server at openaccess.leidenuniv.nl",
contributor = "Jeroen Eggermont",
description = "Promotor: Prof. dr. J.N. Kok. Co-promotor: Dr. W.A.
Kosters. Referent: Dr. W.B. Langdon.; With Summary in
Dutch.",
format = "29005 bytes; 685481 bytes",
identifier = "Eggermont, J., 2005. Doctoral Thesis, Leiden
University; 90-9019760-5",
language = "en",
relation = "IPA Dissertation Series;2005-12",
keywords = "genetic algorithms, genetic programming, data mining",
URL = "https://openaccess.leidenuniv.nl/dspace/bitstream/1887/3393/1/proefschriftppi-eggermont.pdf",
ISBN = "90-9019760-5",
size = "179 pages",
abstract = "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 ×
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.",
notes = "IPA 1887/3393",
}
@Article{Eggermont:2009:GPEM,
author = "Jeroen Eggermont",
title = "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",
journal = "Genetic Programming and Evolvable Machines",
year = "2009",
volume = "10",
number = "1",
pages = "95--96",
month = mar,
keywords = "genetic algorithms, genetic programming",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-008-9071-0",
size = "2 pages",
}
@InCollection{eglit:1994:tpfts,
author = "Jason T. Eglit",
title = "Trend Prediction in Financial Time Series",
booktitle = "Genetic Algorithms at Stanford 1994",
year = "1994",
editor = "John R. Koza",
pages = "31--40",
address = "Stanford, California, 94305-3079 USA",
month = dec,
organisation = "Stanford University",
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-187263-3",
notes = "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",
}
@InProceedings{eguchi:2002:gecco:lbp,
title = "Multiagent Systems with Symbiotic Learning and
Evolution Using Genetic Network Programming",
author = "Toru Eguchi and Kotaro Hirasawa and Jinglu Hu and
Junichi Murata",
booktitle = "Late Breaking Papers at the Genetic and Evolutionary
Computation Conference ({GECCO-2002})",
editor = "Erick Cant{\'u}-Paz",
year = "2002",
month = jul,
pages = "130--137",
address = "New York, NY",
publisher = "AAAI",
publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
keywords = "genetic algorithms, genetic programming",
notes = "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",
}
@InProceedings{Eguchi:2004:EGSCSUGNP,
title = "Elevator Group Supervisory Control Systems Using
Genetic Network Programming",
author = "Toru Eguchi and Kotaro Hirasawa and Jinglu Hu and
Sandor Markon",
pages = "1661--1667",
booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary
Computation",
year = "2004",
publisher = "IEEE Press",
month = "20-23 " # jun,
address = "Portland, Oregon",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming, Real-world
applications, Theory of evolutionary algorithms",
abstract = "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.",
notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and
the IEE.",
}
@InProceedings{eguchi:2005:CEC,
author = "Toru Eguchi and Kotaro Hirasawa and Jinglu Hu and
Sandor Markon",
title = "Elevator Group Supervisory Control System Using
Genetic Network Programming with Functional
Localization",
booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary
Computation",
year = "2005",
editor = "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",
volume = "1",
pages = "328--335",
address = "Edinburgh, UK",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "2-5 " # sep,
organisation = "IEEE Computational Intelligence Society, Institution
of Electrical Engineers (IEE), Evolutionary Programming
Society (EPS)",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-9363-5",
abstract = "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.",
notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and
the EPS.",
}
@Article{DBLP:journals/tsmc/EguchiHHO06,
author = "Toru Eguchi and Kotaro Hirasawa and Jinglu Hu and
Nathan Ota",
title = "A study of evolutionary multiagent models based on
symbiosis",
journal = "IEEE Transactions on Systems, Man, and Cybernetics,
Part B",
volume = "36",
number = "1",
year = "2006",
pages = "179--193",
month = feb,
keywords = "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",
bibsource = "DBLP, http://dblp.uni-trier.de",
doi = "doi:10.1109/TSMCB.2005.856720",
size = "15 pages",
abstract = "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.",
}
@InCollection{ehlis:2000:EITPDRUE,
author = "Tobin Ehlis",
title = "Evolution of Intelligent Task Prioritization in a
Dynamic Randomly Updated Environment",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2000",
year = "2000",
editor = "John R. Koza",
pages = "125--134",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "part of \cite{koza:2000:gagp}",
}
@Article{article1175,
author = "Tobin Ehlis",
title = "Application of Genetic Programming to the ``Snake
Game''",
journal = "Gamedev.Net",
year = "2000",
number = "175",
keywords = "genetic algorithms, genetic programming, game
strategy",
URL = "http://www.gamedev.net/reference/articles/article1175.asp",
abstract = "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.",
notes = "this article was posted to GameDev.net: 8/10/2000
Cited by \cite{CS310GeneticAlgsProject}
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",
}
@Article{Ehrenberg:2012:SN,
author = "Rachel Ehrenberg",
title = "Software Scientist",
journal = "Science News",
year = "2012",
volume = "181",
pages = "20",
month = "14 " # jan,
keywords = "genetic algorithms, genetic programming, Eureqa",
URL = "http://www.sciencenews.org/view/feature/id/337207/title/Software_Scientist",
size = "1 page",
abstract = "With a little data, Eureqa generates fundamental laws
of nature",
notes = "...'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 \cite{Dubcakova:2011:GPEM}",
}
@TechReport{ehrenburg:1995:fls,
author = "Herman H. Ehrenburg and H. A. N. {van Maanen}",
title = "A Finite Automaton Learning System Using Genetic
Programming",
institution = "Department of Computer Science, CWI, Centrum voor
Wiskunde en Informmatica",
year = "1994",
type = "NeuroColt Tech Rep",
number = "CS-R9458",
address = "CWI, P.O. Box 94079, 1090 GB Amsterdam, The
Netherlands",
keywords = "genetic algorithms, genetic programming, Evolutionary
Computing, finite automata",
URL = "ftp://ftp.cwi.nl/pub/CWIreports/AA/CS-R9458.ps.Z",
URL = "http://ftp.cwi.nl/CWIreports/AA/CS-R9458.pdf",
URL = "http://www.neurocolt.org/abs/1995/../../tech_reps/1995/nc-tr-95-009.ps.gz",
URL = "http://citeseer.ist.psu.edu/427245.html",
abstract = "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.",
notes = "
Also available as NC-TR-95-009",
size = "40 pages",
}
@InProceedings{ehrenburg:1996:iDAGcGP,
author = "Herman Ehrenburg",
title = "Improved Directed Acyclic Graph Handling and the
Combine Operator in Genetic Programming",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming, DAG",
pages = "285--291",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
ISBN = "0-262-61127-9",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
size = "6 pages",
notes = "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'.",
}
@InProceedings{Eetal96,
author = "A. E. Eiben and T. J. Euverman and W. Kowalczyk and E.
Peelen and F. Slisser and J. A. M. Wesseling",
title = "Comparing Adaptive and Traditional Techniques for
Direct Marketing",
booktitle = "Proceedings of the 4th European Congress on
Intelligent Techniq ues and Soft Computing",
year = "1996",
editor = "H.-J. Zimmermann",
pages = "434--437",
publisher_address = "Aachen, Germany",
publisher = "Verlag Mainz",
keywords = "genetic algorithms, genetic programming",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/802/http:zSzzSzwww.wi.leidenuniv.nlzSz~guszzSzeufit96.pdf/eiben96comparing.pdf",
URL = "http://citeseer.ist.psu.edu/eiben96comparing.html",
size = "4 pages",
abstract = "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.",
notes = "
",
}
@Unpublished{eiben:email:10-Nov-1997,
author = "Gusz Eiben",
title = "{GP} in Leiden",
note = "electronic communication",
month = "10 " # nov,
year = "1997",
keywords = "genetic algorithms, genetic programming",
notes = "
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.
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 \cite{EEKS98}
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 \cite{veennan: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
",
}
@InCollection{EEKS98,
author = "A. E. Eiben and T. J. Euverman and W. Kowalczyk and F.
Slisser",
title = "Modelling Customer Retention with Statistical
Techniques, Rough Data Models and Genetic Programming",
booktitle = "Rough-Fuzzy Hybridization: A New Trend in Decision
Making Fuzzy Sets, Rough Sets and Decision Making
Processes",
publisher = "Springer-Verlag",
year = "1998",
editor = "Sankar K. Pal and Andrzej Skowron",
pages = "330--345",
address = "Berlin",
keywords = "genetic algorithms, genetic programming",
ISBN = "9814021008",
URL = "http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=DF680800F7770919CB85C7A704F50DC9?doi=10.1.1.55.7177&rep=rep1&type=pdf",
size = "16 pages",
abstract = "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.",
notes = "
http://www.amazon.com/Rough-Fuzzy-Hybridization-Decision-Making/dp/9814021008",
}
@InProceedings{eiben:1998:gmcr,
author = "A. E. Eiben and A. E. Koudijs and F. Slisser",
title = "Genetic Modelling of Customer Retention",
booktitle = "Proceedings of the First European Workshop on Genetic
Programming",
year = "1998",
editor = "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer
and Terence C. Fogarty",
volume = "1391",
series = "LNCS",
pages = "178--186",
address = "Paris",
publisher_address = "Berlin",
month = "14-15 " # apr,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-64360-5",
doi = "doi:10.1007/BFb0055937",
abstract = "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.",
notes = "EuroGP'98",
}
@InProceedings{eiben:1999:PA,
author = "A. E. Eiben and D. Elia and J. I. van Hemert",
title = "Population dynamics and emerging mental features in
{AEGIS}",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1257--1264",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "artificial life, adaptive behavior and agents",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-038.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-038.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@Article{eiben:1999:pcea,
author = "Agoston Endre Eiben and Robert Hinterding and Zbigniew
Michalewicz",
title = "Parameter Control in Evolutionary Algorithms",
journal = "IEEE Transations on Evolutionary Computation",
year = "1999",
volume = "3",
number = "2",
pages = "124--141",
month = jul,
keywords = "evolutionary strategies, genetic algorithms,
evolutionary computation, self-adjusting systems,
control mechanisms, evolutionary algorithms, parameter
control, self-adaptation",
ISSN = "1089-778X",
size = "18 pages",
abstract = "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",
notes = "Some mention of GP, particularly Peter Angeline's
work. Reference Cited: 144 CODEN: ITEVF5 Inspec
Accession Number: 6290502",
}
@Article{Eiben:2002:IPL,
author = "A. E. Eiben and M. Schoenauer",
title = "Evolutionary computing",
journal = "Information Processing Letters",
year = "2002",
volume = "82",
pages = "1--6",
number = "1",
abstract = "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.",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6V0F-44YWS0J-1/2/a93e1d8b3c96d1cb1a32da104588a569",
keywords = "genetic algorithms, genetic programming, Evolutionary
computing, Evolution strategies, Evolutionary
programming",
}
@Book{eiben:2003:book,
author = "A. E. Eiben and J. E. Smith",
title = "Introduction to Evolutionary Computing",
publisher = "Springer",
year = "2003",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-40184-9",
URL = "http://www.cs.vu.nl/~gusz/ecbook/ecbook.html",
notes = "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",
size = "pages",
}
@InProceedings{1277023,
author = "Gusz Eiben and Joeri Bekker and Robert Griffioen and
Evert Haasdijk",
title = "Balancing quality and quantity in evolving agent
systems",
booktitle = "GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation",
year = "2007",
editor = "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",
volume = "1",
isbn13 = "978-1-59593-697-4",
pages = "335--335",
address = "London",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p335.pdf",
doi = "doi:10.1145/1276958.1277023",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
month = "7-11 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, Artificial
Life, Evolutionary Robotics, Adaptive Behaviour,
Evolvable Hardware: Poster, multiagent system, NEW
TIES, quality bias, quantity bias, varying population
size",
notes = "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",
}
@InCollection{Eisenstein:1997:GAil,
author = "Jacob Eisenstein",
title = "Genetic Algorithms and Incremental Learning",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1997",
publisher = "Stanford Bookstore",
year = "1997",
editor = "John R. Koza",
pages = "47--56",
address = "Stanford, California, 94305-3079 USA",
month = "17 " # mar,
keywords = "genetic algorithms, genetic programming, seeding",
ISBN = "0-18-205981-2",
notes = "part of \cite{koza:1997:GAGPs}",
}
@TechReport{Eisenstein:2003-023,
author = "Jacob Eisenstein",
title = "Evolving Robocode Tank Fighters",
institution = "Computer Science and Artificial Intelligence
Laboratory, MIT",
year = "2003",
type = "AI Memo",
number = "2003-023",
address = "Cambridge, MA 02139, USA",
month = "28 " # oct,
keywords = "genetic algorithms, genetic programming",
URL = "ftp://publications.ai.mit.edu/ai-publications/2003/AIM-2003-023.pdf",
URL = "ftp://publications.ai.mit.edu/ai-publications/2003/AIM-2003-023.ps",
abstract = "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.",
size = "24 pages",
}
@InProceedings{ekart:1998:gcd4bl,
author = "Aniko Ekart",
title = "Generating Class Descriptions of Four Bar Linkages",
booktitle = "Late Breaking Papers at the Genetic Programming 1998
Conference",
year = "1998",
editor = "John R. Koza",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "Stanford, CA, USA",
month = "22-25 " # jul,
publisher = "Stanford University Bookstore",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.sztaki.hu/~ekart/asi.ps",
URL = "http://citeseer.ist.psu.edu/465578.html",
abstract = "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.",
notes = "GP-98LB See also \cite{ekart:1999:ASI}",
}
@InProceedings{ekart:1999:ccgGPm,
author = "Aniko Ekart",
title = "Controlling Code Growth in Genetic Programming by
Mutation",
booktitle = "Late-Breaking Papers of EuroGP-99",
year = "1999",
editor = "W. B. Langdon and Riccardo Poli and Peter Nordin and
Terry Fogarty",
pages = "3--12",
address = "Goteborg, Sweden",
month = "26-27 " # may,
organisation = "EvoGP",
keywords = "genetic algorithms, genetic programming",
URL = "ftp://ftp.cwi.nl/pub/CWIreports/SEN/SEN-R9913.pdf",
URL = "ftp://ftp.cwi.nl/pub/CWIreports/SEN/SEN-R9913.ps.Z",
abstract = "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",
notes = "EuroGP'99LB part of \cite{langdon:1999:egplb}",
}
@InProceedings{ekart:1999:ASI,
author = "Aniko Ekart and Andras Markus",
title = "Decision Trees and Genetic Programming in Synthesis of
Four Bar Mechanisms",
booktitle = "Life Cycle Approaches to Production Systems,
Proceedings of the Advanced Summer Institute-ASI'99",
year = "1999",
pages = "210--208",
address = "Leuven",
month = "22-24 " # sep,
keywords = "genetic algorithms, genetic programming",
ISBN = "960-530-040-0",
notes = "http://www.lar.ee.upatras.gr/icims/asi/asi99/asi99.htm
See also \cite{ekart:1998:gcd4bl} Nice fusion of C4.5
and GP.",
}
@InProceedings{ekart:1999:EA,
author = "Aniko Ekart",
title = "Shorter Fitness Preserving Genetic Programs",
booktitle = "Artificial Evolution. 4th European Conference, AE'99,
Selected Papers",
year = "2000",
editor = "C. Fonlupt and J.-K. Hao and E. Lutton and E. Ronald
and M. Schoenauer",
volume = "1829",
series = "LNCS",
pages = "73--83",
address = "Dunkerque, France",
month = "3-5 " # nov,
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-67846-8",
URL = "http://www.sztaki.hu/~ekart/ea.ps",
URL = "http://citeseer.ist.psu.edu/496596.html",
notes = "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",
}
@InProceedings{ekart:2000:mGPfs,
author = "Aniko Ekart and S. Z. Nemeth",
title = "A metric for genetic programs and fitness sharing",
booktitle = "Genetic Programming, Proceedings of EuroGP'2000",
year = "2000",
editor = "Riccardo Poli and Wolfgang Banzhaf and William B.
Langdon and Julian F. Miller and Peter Nordin and
Terence C. Fogarty",
volume = "1802",
series = "LNCS",
pages = "259--270",
address = "Edinburgh",
publisher_address = "Berlin",
month = "15-16 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-67339-3",
URL = "http://www.sztaki.hu/~ekart/new_metric.ps",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=259",
abstract = "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.",
notes = "EuroGP'2000, part of \cite{poli:2000:GP}",
}
@Article{ekart:2001:genp,
author = "Aniko Ekart and S. Z. Nemeth",
title = "Selection Based on the Pareto Nondomination Criterion
for Controlling Code Growth in Genetic Programming",
journal = "Genetic Programming and Evolvable Machines",
year = "2001",
volume = "2",
number = "1",
pages = "61--73",
month = mar,
keywords = "genetic algorithms, genetic programming, code growth,
selection scheme, multiobjective optimization",
ISSN = "1389-2576",
doi = "doi:10.1023/A:1010070616149",
abstract = "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.",
notes = "Article ID: 319813",
}
@InProceedings{ekart:2001:ESI,
author = "Aniko Ekart and S. Z. Nemeth",
title = "Stability of Tree Based Decision Principles",
booktitle = "EURO Summer Institute (ESI) XIX, Decision Analysis and
Artificial Intellience",
year = "2001",
address = "Toulouse, France",
month = "9-22 " # sep,
keywords = "genetic algorithms, genetic programming",
}
@PhdThesis{ekart:thesis,
author = "Aniko Ekart",
title = "Genetic programming: new performance improving methods
and applications",
school = "E{\"{o}}tv{\"{o}}s Lorand University",
year = "2001",
email = "ekart@sztaki.hu",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
}
@InProceedings{ekart:2002:EuroGP,
title = "Maintaining the Diversity of Genetic Programs",
author = "Anik\'o Ek\'art and Sandor Zoltan N\'emeth",
editor = "James A. Foster and Evelyne Lutton and Julian Miller
and Conor Ryan and Andrea G. B. Tettamanzi",
booktitle = "Genetic Programming, Proceedings of the 5th European
Conference, EuroGP 2002",
volume = "2278",
series = "LNCS",
pages = "162--171",
publisher = "Springer-Verlag",
address = "Kinsale, Ireland",
publisher_address = "Berlin",
month = "3-5 " # apr,
URL = "http://www.sztaki.hu/~ekart/eurgp2.ps",
year = "2002",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-43378-3",
abstract = "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.",
notes = "EuroGP'2002, part of \cite{lutton:2002:GP}",
}
@Article{ekart:2002:EJOP,
author = "Anik\'o Ek\'art and S. Z. N\'emeth",
title = "Stability analysis of tree structured decision
functions",
journal = "European Journal of Operational Research",
year = "2005",
volume = "160",
number = "3",
pages = "676--695",
month = "1 " # feb,
keywords = "genetic algorithms, genetic programming, Decision
support systems, Evolutionary computation, Stability
analysis, Decision functions",
ISSN = "0377-2217",
URL = "http://www.sciencedirect.com/science/article/B6VCT-4B6CR54-4/2/8de1437b694f9e2060da541ad1b175be",
doi = "doi:10.1016/j.ejor.2003.10.007",
abstract = "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.",
}
@Article{ekart:2003:AIEDAM,
author = "Aniko Ekart and Andras Markus",
title = "Using Genetic Programming and Decision Trees for
Generating Structural Descriptions of Four Bar
Mechanisms",
journal = "Artificial Intelligence for Engineering Design,
Analysis and Manufacturing",
year = "2003",
volume = "17",
number = "3",
pages = "205--220",
keywords = "genetic algorithms, genetic programming, decision
trees, four bar mechanism synthesis, machine learning",
ISSN = "0890-0604",
abstract = "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.",
notes = "http://journals.cambridge.org/action/displayJournal?jid=AIE",
}
@InProceedings{ekart:2004:eurogp,
author = "Aniko Ekart and Steven Gustafson",
title = "A Data Structure for Improved {GP} Analysis via
Efficient Computation and Visualisation of Population
Measures",
booktitle = "Genetic Programming 7th European Conference, EuroGP
2004, Proceedings",
year = "2004",
editor = "Maarten Keijzer and Una-May O'Reilly and Simon M.
Lucas and Ernesto Costa and Terence Soule",
volume = "3003",
series = "LNCS",
pages = "35--46",
address = "Coimbra, Portugal",
publisher_address = "Berlin",
month = "5-7 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-21346-5",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=35",
URL = "http://www.sztaki.hu/~ekart/eurgp4.ps",
URL = "http://www.cs.nott.ac.uk/~smg/research/publications/eurogp-itree-2004.pdf",
abstract = "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.",
notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in
conjunction with EvoCOP2004 and EvoWorkshops2004",
}
@InProceedings{Ekart:2004:IWES,
author = "Aniko Ekart",
title = "Analysing the Emerging Properties of Genetic Programs
through the iTrees of Populations",
booktitle = "Proceedings of the 5th International Workshop on
Emergent Synthesis IWES'04",
year = "2004",
pages = "61--66",
address = "Budapest, Hungary",
month = may # " 24-25",
organisation = "Computer and Automation Research Institute. Hungarian
Academy of Sciences",
keywords = "genetic algorithms, genetic programming",
notes = "http://www.sztaki.hu/IWES04/",
}
@InProceedings{1274010,
author = "Aniko Ekart",
title = "Evolution of lace knitting stitch patterns by genetic
programming",
booktitle = "Late breaking paper at Genetic and Evolutionary
Computation Conference {(GECCO'2007)}",
year = "2007",
month = "7-11 " # jul,
editor = "Peter A. N. Bosman",
isbn13 = "978-1-59593-698-1",
pages = "2457--2461",
address = "London, United Kingdom",
keywords = "genetic algorithms, genetic programming, creativity,
evaluation, representation",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2457.pdf",
doi = "doi:10.1145/1274000.1274010",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Distributed on CD-ROM at GECCO-2007 ACM Order No.
910071",
}
@InProceedings{Eklund:2001:AMPGA,
author = "E. Eklund",
title = "A Massively Parallel {GP} Architecture",
booktitle = "Evolutionary Methods for Design Optimization and
Control with Applications to Industrial Problems",
editor = "K. C. Giannakoglou and D. T. Tsahalis and J.
P\'{e}riaux and K. D. Papailiou and T. Fogarty",
pages = "103--108",
year = "2001",
publisher = "International Center for Numerical Methods in
Engineering (Cmine)",
publisher_address = "Gran Capitan s/n, 08034 Barcelona, Spain",
ISBN = "84-89925-97-6",
address = "Athens, Greece",
month = "19-21 " # sep,
keywords = "genetic algorithms, genetic programming",
notes = "Proceedings of the EUROGEN2001 Conference",
}
@InProceedings{Eklund:2001:MPA,
author = "Sven E. Eklund",
title = "A Massively Parallel Architecture for Linear Machine
Code Genetic Programming",
booktitle = "Evolvable Systems: From Biology to Hardware:
Proceedings of 4th International Conference, ICES
2001",
year = "2001",
editor = "Yong Liu and Kiyoshi Tanaka and Masaya Iwata and
Tetsuya Higuchi and Moritoshi Yasunaga",
volume = "2210",
series = "Lecture Notes in Computer Science",
pages = "216--224",
address = "Tokyo, Japan",
publisher_address = "Heidelberg",
month = oct # " 3-5",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
CODEN = "LNCSD9",
ISSN = "0302-9743",
bibdate = "Sat Feb 2 13:06:57 MST 2002",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2210&spage=216",
acknowledgement = ack-nhfb,
abstract = "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).",
notes = "ICES-2001
A1 Dalarna University, Sweden sven.eklund@ieee.org",
}
@InProceedings{eklund:2002:ampgeiv,
author = "Sven E. Eklund",
title = "A Massively Parallel {GP} Engine in {VLSI}",
booktitle = "Proceedings of the 2002 Congress on Evolutionary
Computation CEC2002",
editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
and Garry Greenwood and Hitoshi Iba and Paul Marrow and
Mark Shackleton",
pages = "629--633",
year = "2002",
publisher = "IEEE Press",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
organisation = "IEEE Neural Network Council (NNC), Institution of
Electrical Engineers (IEE), Evolutionary Programming
Society (EPS)",
ISBN = "0-7803-7278-6",
month = "12-17 " # may,
notes = "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)",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
}
@InProceedings{eklund:2003:PDPS,
author = "Sven E Eklund",
title = "Time series forecasting using massively parallel
genetic programming",
booktitle = "Proceedings of Parallel and Distributed Processing
International Symposium",
year = "2003",
pages = "143--147",
month = "22-26 " # apr,
organisation = "IEEE",
keywords = "genetic algorithms, genetic programming, EHW, FPGA,
Virtex XC2V10000, wolfe sunspot",
doi = "doi:10.1109/IPDPS.2003.1213272",
URL = "http://dalea.du.se/research/?itemId=147",
abstract = "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.",
notes = "outperforms SETAR but not best ANN",
}
@InProceedings{Eklund:2003:ICONS,
author = "Sven E. Eklund",
title = "Handwritten Character Recognition using a massively
parallel {GP} engine in {VLSI}",
booktitle = "IFAC International Conference on Intelligent Control
Systems and Signal Processing",
year = "2003",
editor = "Peter J. Fleming",
address = "Faro, Portugal",
month = apr # " 08-11",
organisation = "IFAC",
keywords = "genetic algorithms, genetic programming",
notes = "http://www.sciference.com/icons03/main.py/pre_programme",
}
@Article{Eklund:2004:PC,
author = "Sven E. Eklund",
title = "A massively parallel architecture for distributed
genetic algorithms",
journal = "Parallel Computing",
year = "2004",
volume = "30",
pages = "647--676",
number = "5-6",
keywords = "genetic algorithms, genetic programming, Parallel
architecture, Diffusion model, FPGA, Classification,
Time series forecasting, Regression",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6V12-4CDS49V-1/2/5ba1531eae2c9d8b336f1e90cc0ba5e9",
ISSN = "0167-8191",
doi = "doi:10.1016/j.parco.2003.12.009",
abstract = "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.",
}
@InProceedings{ekman:2001:ehs,
author = "Magnus Ekman and Peter Nordin",
title = "Evolvable Hardware using State-machines",
booktitle = "Graduate Student Workshop",
year = "2001",
editor = "Conor Ryan",
pages = "409--412",
address = "San Francisco, California, USA",
month = "7 " # jul,
keywords = "genetic algorithms, genetic programming",
notes = "GECCO-2001WKS Part of heckendorn:2001:GECCOWKS",
}
@Article{El-Bakry:2006:IJMPB,
author = "Salah Yaseen El-Bakry and Amr Radi",
title = "Genetic Programming approach for electron-alkali-metal
atom collisions",
journal = "International Journal of Modern Physics B",
year = "2006",
volume = "20",
number = "32",
pages = "5463--5471",
month = dec,
keywords = "genetic algorithms, genetic programming, Condensed
Matter Physics, Statistical Physics, Applied Physics,
electron scattering, alkali atoms, total cross
sections, dipole polarizability",
URL = "http://www.genetic-programming.org/hc2007/09-Radi/Radi-Paper-A.pdf",
doi = "doi:10.1142/S0217979206035825",
size = "5 pages",
abstract = "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.",
notes = "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",
}
@Article{El-Bakry:2007:AR,
author = "Mostafa Y. El-Bakry and Amr Radi",
title = "Genetic programming approach for flow of steady state
fluid between two eccentric spheres",
journal = "Applied Rheology",
year = "2007",
volume = "17",
number = "6",
pages = "68210",
keywords = "genetic algorithms, genetic programming",
ISSN = "1430-6395",
publisher = "Kerschensteiner Verlag, Germany",
URL = "http://www.genetic-programming.org/hc2007/09-Radi/Radi-Paper-C.pdf",
doi = "doi:10.3933/ApplRheol-17-68210",
size = "13 pages",
abstract = "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.",
notes = "Mostafa Elbakry http://www.appliedrheology.org/ 2007
HUMIES GECCO-2007",
}
@Article{El-Bakry:2007:IJMPC,
author = "Mostafa Y. El-Bakry and Amr Radi",
title = "Genetic Programming for Hadronic Interactions at High
Energies",
journal = "International Journal of Modern Physics C,
Computational Physics and Physical Computation",
year = "2007",
volume = "18",
number = "3",
pages = "329--334",
keywords = "genetic algorithms, genetic programming, hadron-hadron
interactions, Pseudo-rapidity distribution,
proton2proton interaction at high energies",
doi = "doi:10.1142/S0129183107010371",
abstract = "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.",
notes = "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",
}
@Article{El-Bakry:2007:IJMPC2,
author = "Salah Yaseen El-Bakry and Amr Radi",
title = "Discovered Function for Positron Collisions with
Alkali-Metal Atoms using Genetic Programming",
journal = "International Journal of Modern Physics C,
Computational Physics and Physical Computation",
year = "2007",
volume = "18",
number = "3",
pages = "351--358",
keywords = "genetic algorithms, genetic programming, positron
collisions, alkali-metal atoms, total collisional cross
sections",
doi = "doi:10.1142/S0129183107009480",
abstract = "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.",
notes = "IJMPC
Physics Department, Taibah University, Madinah
Munawwarah, P. O. Box 344, Saudi Arabia
Physics Department, Ain Shams University, Abbassia,
Cairo, Egypt",
}
@Article{El-Baroudy:2010:JH,
author = "I. El-Baroudy and A. Elshorbagy and S. K. Carey and O.
Giustolisi and D. Savic",
title = "Comparison of three data-driven techniques in
modelling the evapotranspiration process",
journal = "Journal of Hydroinformatics",
year = "2010",
volume = "12",
number = "4",
pages = "365--379",
keywords = "genetic algorithms, genetic programming, EPR, actual
evapotranspiration, data driven techniques, eddy
covariance, evolutionary polynomial regression, neural
networks",
ISSN = "1464-7141",
URL = "http://www.iwaponline.com/jh/012/0365/0120365.pdf",
doi = "doi:10.2166/hydro.2010.029",
size = "15 pages",
abstract = "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.",
}
@InProceedings{el-beltagy:1999:MTFEOCEPPL,
author = "Mohammed A. El-Beltagy and Prasanth B. Nair and Andy
J. Keane",
title = "Metamodeling Techniques For Evolutionary Optimization
of Computationally Expensive Problems: Promises and
Limitations",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "196--203",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-854.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-854.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{conf/crv/El-SawahJGP07,
author = "Ayman El-Sawah and Chris Joslin and Nicolas D.
Georganas and Emil M. Petriu",
title = "A Framework for {3D} Hand Tracking and Gesture
Recognition using Elements of Genetic Programming",
booktitle = "Fourth Canadian Conference on Computer and Robot
Vision, CRV '07",
year = "2007",
pages = "495--502",
address = "Montreal",
month = "28-30 " # may,
publisher = "IEEE Computer Society",
keywords = "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",
doi = "doi:10.1109/CRV.2007.3",
size = "8 pages",
abstract = "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.",
notes = "Univ. of Ottawa, Ottawa Almost no description of GP
used",
bibdate = "2007-06-01",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/crv/crv2007.html#El-SawahJGP07",
}
@InProceedings{eldershaw:1999:RMG,
author = "Craig Eldershaw and Stephen Cameron",
title = "Real-world applications: Motion planning using {GA}s",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1776",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "real world applications, poster papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-768.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-768.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@Article{Eldrandaly:2008:IAJIT,
author = "Khalid Eldrandaly and Abdel-Azim Negm",
title = "Performance Evaluation of Gene Expression Programming
for Hydraulic Data Mining",
journal = "The International Arab Journal of Information
Technology",
year = "2008",
volume = "5",
number = "2",
pages = "126--131",
month = apr,
email = "khalid_eldrandaly@yahoo.com",
keywords = "genetic algorithms, genetic programming, gene
expression programming, GEP, Data mining, multiple
linear regression, MLR, hydraulic jump.",
URL = "http://www.ccis2k.org/iajit/PDF/vol.5,no.2/4-103.pdf",
size = "6 pages",
abstract = "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.",
notes = "Information Systems Department, College of Computers,
Zagazig University, Egypt http://www.iajit.org/",
}
@Article{Eldrandaly:2009:AJAS,
title = "Integrating Gene Expression Programming and Geographic
Information Systems for Solving a Multi Site Land Use
Allocation Problem",
author = "Khalid A. Eldrandaly",
journal = "American Journal of Applied Sciences",
publisher = "Science Publications",
year = "2009",
ISSN = "15469239",
keywords = "genetic algorithms, genetic programming, gene
expression programming, Multi site land use allocation,
GIS, SDSS",
URL = "http://www.scipub.org/fulltext/ajas/ajas651021-1027.pdf",
URL = "http://www.doaj.org/doaj?func=openurl\&genre=article\&issn=15469239\&date=2009\&volume=6\&issue=5\&spage=1021",
bibsource = "OAI-PMH server at www.doaj.org",
oai = "oai:doaj-articles:374b808b659956eb2527109ade485337",
abstract = "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.",
notes = "Faculty of Computers and Informatics, Zagazig
University, Egypt",
}
@Article{Eldrandaly2009,
author = "Khalid Eldrandaly",
title = "A {GEP}-based spatial decision support system for
multisite land use allocation",
journal = "Applied Soft Computing",
year = "2009",
volume = "10",
number = "3",
pages = "694--702",
month = jun,
ISSN = "1568-4946",
doi = "doi:10.1016/j.asoc.2009.07.014",
URL = "http://www.sciencedirect.com/science/article/B6W86-4X2DCVV-2/2/c8addfbfae7f3e5035dc45213f378416",
keywords = "genetic algorithms, genetic programming, Spatial
decision support systems, Multisite land use
allocation, GIS, Gene expression programming",
abstract = "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.",
notes = "King Abdulaziz University, P.O. Box 80105, Jeddah
21589, Saudi Arabia",
}
@PhdThesis{Elfwing:thesis,
author = "Stefan Elfwing",
title = "Embodied Evolution of Learning Ability",
school = "KTH School of Computer Science and Communication",
year = "2007",
type = "Doctoral Thesis",
address = "SE-100 44 Stockholm, Sweden",
month = nov,
keywords = "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",
URL = "http://www.irp.oist.jp/nc/elfwing/Elfwing_thesis_final_electronic.pdf",
size = "162 pages",
isbn13 = "978-91-7178-787-3",
abstract = "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.",
notes = "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",
}
@Article{Elfwing:2007:tec,
author = "Stefan Elfwing and Eiji Uchibe and Kenji Doya and
Henrik I. Christensen",
title = "Evolutionary Development of Hierarchical Learning
Structures",
journal = "IEEE Transactions on Evolutionary Computation",
year = "2007",
volume = "11",
number = "2",
pages = "249--264",
month = apr,
keywords = "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",
doi = "doi:10.1109/TEVC.2006.890270",
ISSN = "1089-778X",
abstract = "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",
}
@InProceedings{elhaggaz:1999:E,
author = "Salah Elhaggaz and Brian Turton and John Brown",
title = "Evolutionary algorithm for phased network topology
design",
booktitle = "Late Breaking Papers at the 1999 Genetic and
Evolutionary Computation Conference",
year = "1999",
editor = "Scott Brave and Annie S. Wu",
pages = "80--87",
address = "Orlando, Florida, USA",
month = "13 " # jul,
notes = "GECCO-99LB",
}
@Article{ellis:2002:AEM,
author = "David I. Ellis and David Broadhurst and Douglas B.
Kell and Jem J. Rowland and Royston Goodacre",
title = "Rapid and Quantitative Detection of the Microbial
Spoilage of Meat by Fourier Transform Infrared
Spectroscopy and Machine Learning",
journal = "Applied and Environmental Microbiology",
year = "2002",
volume = "68",
number = "6",
pages = "2822--2828",
month = jun,
keywords = "genetic algorithms, genetic programming",
URL = "http://dbkgroup.org/Papers/app_%20env_microbiol_68_(2822).pdf",
doi = "doi:10.1128/AEM.68.6.2822?2828.2002",
abstract = "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.",
notes = "American Society for Microbiology PMID: 12039738",
}
@Article{Ellis:2004:ACA,
author = "David I. Ellis and David Broadhurst and Royston
Goodacre",
title = "Rapid and quantitative detection of the microbial
spoilage of beef by Fourier transform infrared
spectroscopy and machine learning",
journal = "Analytica Chimica Acta",
year = "2004",
volume = "514",
pages = "193--201",
number = "2",
abstract = "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.",
owner = "wlangdon",
URL = "http://dbkgroup.org/dave_files/ACAbeef04.pdf",
URL = "http://www.sciencedirect.com/science/article/B6TF4-4CDJJ78-5/2/63df147cb89407ac7ac8bf9d093580f7",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1016/j.aca.2004.03.060",
}
@Article{ElNofely1989437,
author = "A. El-Nofely and L. Sadek and N. Soliman",
title = "Spacing in the human deciduous dentition in relation
to tooth size and dental arch size",
journal = "Archives of Oral Biology",
volume = "34",
number = "6",
pages = "437--441",
year = "1989",
ISSN = "0003-9969",
doi = "doi:10.1016/0003-9969(89)90122-2",
URL = "http://www.sciencedirect.com/science/article/B6T4J-4BWHJWH-10R/2/d3ad580204c24fb1b0297899cd63dc6d",
notes = "Not on GP",
}
@InProceedings{Elsey:1996:Chemeca,
author = "Justin Elsey and Jorg Riepenhausen and Ben McKay and
Geoffrey W. Barton",
title = "Dynamic Modelling of a Cooking Extruder",
booktitle = "Chemeca 96: Excellence in Chemical Engineering; 24th
Australian and New Zealand Chemical Engineering
Conference and Exhibition; Proceedings",
year = "1996",
editor = "Gordon Weiss",
volume = "2",
pages = "43--48",
address = "Barton, ACT, Australia",
organisation = "Institution of Engineers, Australia",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-85825-658-4",
URL = "http://search.informit.com.au/documentSummary;dn=893841670974616;res=IELENG",
abstract = "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.",
notes = "National conference publication (Institution of
Engineers, Australia) ; no. 96/13. cited 20 Dec 11
",
}
@PhdThesis{Elsey:thesis,
author = "Justin Rae Elsey",
title = "Dynamic Modelling, Measurement and Control of
Co-rotating Twin-Screw Extruders",
school = "Department of Chemical Engineering, University of
Sydney",
year = "2002",
address = "Australia",
month = "25 " # aug,
keywords = "genetic algorithms, genetic programming, twin-screw
extrusion, extruder geometry, dynamic modelling,
process control, acoustic sensors, image analysis,
bubble growth",
URL = "http://ses.library.usyd.edu.au/bitstream/2123/687/2/adt-NU20050131.14060102whole.pdf",
URL = "http://hdl.handle.net/2123/687",
size = "242 pages",
abstract = "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.",
notes = "Uses GP, eg in chapter 6. See also his publications
pages iv-v",
}
@Article{Elshorbagy:2009:JH,
author = "Amin Elshorbagy and Ibrahim El-Baroudy",
title = "Investigating the capabilities of evolutionary
data-driven techniques using the challenging estimation
of soil moisture content",
journal = "Journal of Hydroinformatics",
year = "2009",
volume = "11",
number = "3-4",
pages = "237--251",
keywords = "genetic algorithms, genetic programming, evolutionary
polynomial regression, EPR, prediction, soil moisture,
tool uncertainty",
ISSN = "1464-7141",
URL = "http://www.iwaponline.com/jh/011/0237/0110237.pdf",
doi = "doi:10.2166/hydro.2009.032",
size = "15 pages",
abstract = "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.",
notes = "Laucelli EPR toolbox, South Bison Hill, oil sands
reclamation, 1 foot or more peat layer, AB Canada,
Discipulus \cite{francone:manual}
p242 'Discipulus produced better models than EPR'. p246
EPR provides insight. p258 GPLAB always evolved
constants (not formulae).",
}
@Article{Elshorbagy:2010:HESS,
author = "A. Elshorbagy and G. Corzo and S. Srinivasulu and D.
P. Solomatine",
title = "Experimental investigation of the predictive
capabilities of data driven modeling techniques in
hydrology - Part 1: Concepts and methodology",
journal = "Hydrology and Earth System Sciences",
year = "2010",
volume = "14",
number = "10",
pages = "1931--1941",
month = "14 " # oct,
keywords = "genetic algorithms, genetic programming",
ISSN = "1471-2164",
URL = "http://www.hydrol-earth-syst-sci.net/14/1931/2010/hess-14-1931-2010.pdf",
URL = "www.hydrol-earth-syst-sci.net/14/1931/2010/",
doi = "doi:10.5194/hess-14-1931-2010",
publisher = "Copernicus GmbH",
abstract = "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{\"i}ve 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.",
notes = "See also \cite{Elshorbagy:2010a:HESS} Published in
Hydrol. Earth Syst. Sci. Discuss.: 19 November 2009
\cite{oai:doaj-articles:09c2f3076a15547532440e3ac274c044}
and
\cite{oai:doaj-articles:90e50b27744c40b3f9d0243d0896b665}
http://www.hydrol-earth-syst-sci-discuss.net/6/7055/2009/hessd-6-7055-2009.pdf
cite{hessd-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",
}
@Article{Elshorbagy:2010a:HESS,
title = "Experimental investigation of the predictive
capabilities of data driven modeling techniques in
hydrology - Part 2: Application",
author = "A. Elshorbagy and G. Corzo and S. Srinivasulu and D.
P. Solomatine",
journal = "Hydrology and Earth System Sciences",
year = "2010",
volume = "14",
number = "10",
pages = "1943--1961",
month = "14 " # oct,
keywords = "genetic algorithms, genetic programming",
ISSN = "10275606",
bibsource = "OAI-PMH server at www.doaj.org",
language = "eng",
oai = "oai:doaj-articles:0b5621edb6cf47d7aee8cedce805592b",
source = "Hydrology and Earth System Sciences",
URL = "http://www.hydrol-earth-syst-sci.net/14/1943/2010/hess-14-1943-2010.pdf",
size = "19 pages",
abstract = "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.",
notes = "See also \cite{Elshorbagy:2010:HESS}",
}
@InProceedings{El-Telbany:2004:ICEEC,
author = "M. E. El-Telbany",
title = "The egyptian stock market return prediction: a genetic
programming approach",
booktitle = "International Conference on Electrical, Electronic and
Computer Engineering, ICEEC-04",
year = "2004",
editor = "Abdel-Moniem Wahdan and Ahmed Amer and Hani Fikry and
Ashraf Salem",
pages = "161--164",
address = "Ain Shams University, Cairo, Egypt",
month = "5-7 " # sep,
keywords = "genetic algorithms, genetic programming",
notes = "details from ieee",
}
@InProceedings{figueiredopereiraemer:2002:gecco,
author = "Maria Cl{\'a}udia Figueiredo Pereira Emer and Silvia
Regina Vergilio",
title = "{GPTesT}: {A} Testing Tool Based On Genetic
Programming",
booktitle = "GECCO 2002: Proceedings of the Genetic and
Evolutionary Computation Conference",
editor = "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",
year = "2002",
pages = "1343--1350",
address = "New York",
publisher_address = "San Francisco, CA 94104, USA",
month = "9-13 " # jul,
publisher = "Morgan Kaufmann Publishers",
keywords = "genetic algorithms, genetic programming, search-based
software engineering, fault-based testing, induction of
programs, mutation analysis, software test criteria",
ISBN = "1-55860-878-8",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/sbse017.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/sbse017.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-25.pdf",
abstract = "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.",
notes = "GECCO-2002. A joint meeting of the eleventh
International Conference on Genetic Algorithms
(ICGA-2002) and the seventh Annual Genetic Programming
Conference (GP-2002)",
}
@InProceedings{sbes2002meta006,
title = "Selection and evaluation of test data sets based on
genetic programming",
author = "Maria Claudia Emer and Silvia Regina Vergilio",
year = "2002",
identifier = "sbes2002article006",
language = "por",
rights = "Sociedade Brasileira de Computa{\c c}{\~a}o",
source = "sbes2002",
booktitle = "XVI Simposio Brasileiro de Engenharia de Software",
address = "Gramado, Rio Grande do Sul, Brasil",
keywords = "genetic algorithms, genetic programming",
}
@Article{emer:2003:SQJ,
author = "Maria Claudia F. P. Emer and Silvia Regina Vergilio",
title = "Selection and Evaluation of Test Data Based on Genetic
Programming",
journal = "Software Quality Journal",
year = "2003",
volume = "11",
number = "2",
pages = "167--186",
month = jun,
keywords = "genetic algorithms, genetic programming, evolutionary
computation, testing criteria, mutation analysis SBSE,
software engineering",
doi = "doi:10.1023/A:1023772729494",
size = "20 pages",
abstract = "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.",
notes = "Article ID: 5122058
Interactive tool incorporating GP. GPTesT (C++
UML).
Chameleon \cite{Spinosa: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
",
}
@InProceedings{Endo:2002:GOB,
author = "Ken Endo and Funinori Yamasaki and Takashi Maeno and
Hiroaki Kitano",
title = "Co-evolution of Morphology and Controller for Biped
Humanoid Robot",
booktitle = "{RoboCup} 2002: Robot Soccer World Cup {VI}",
editor = "Gal A. Kaminka and Pedro U. Lima and Raul Rojas",
volume = "2752",
year = "2002",
series = "Lecture Notes in Artificial Intelligence",
pages = "327--341",
publisher_address = "Berlin and Heidelberg",
publisher = "Springer-Verlag",
keywords = "genetic algorithms",
CODEN = "LNCSD9",
ISSN = "0302-9743",
ISBN = "3-540-40666-2",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2752&spage=327",
doi = "doi:10.1007/b11927",
size = "15 pages",
abstract = "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.",
notes = "GA, not a GP approach",
}
@InCollection{engel:1995:EESEAT,
author = "David Engel",
title = "Evolving Effective Solutions in Effective Amounts of
Time",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1995",
year = "1995",
editor = "John R. Koza",
pages = "76--85",
address = "Stanford, California, 94305-3079 USA",
month = "11 " # dec,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-195720-5",
notes = "part of \cite{koza:1995:gagp}",
}
@InCollection{Engelbrecht:2002:DMaHA,
author = "A. P. Engelbrecht and L. Schoeman and Sonja
Rouwhorst",
title = "A Building Block Approach to Genetic Programming for
Rule Discovery",
booktitle = "Data Mining: A Heuristic Approach",
publisher = "IGI-global",
year = "2002",
editor = "Hussein A. Abbass and Charles S. Newton and Ruhul
Sarker",
chapter = "9",
pages = "174--190",
address = "701 E Chocolate Avenue, Hershey PA 17033, USA",
keywords = "genetic algorithms, genetic programming",
isbn13 = "9781930708259",
URL = "http://www.igi-global.com/chapter/building-block-approach-genetic-programming/7589",
doi = "doi:10.4018/978-1-930708-25-9.ch009",
abstract = "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.",
notes = "A. P. Engelbrecht (University of Pretoria, South
Africa), L. Schoeman (University of Pretoria, South
Africa) and Sonja Rouwhorst (Vrije Universiteit
Amsterdam, The Netherlands)",
}
@InCollection{engelhardt:1998:LBNDSUGP,
author = "Barbara Engelhardt",
title = "Learning a {Bayesian} Network from Data Samples Using
Genetic Programming",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1998",
year = "1998",
editor = "John R. Koza",
pages = "1--10",
address = "Stanford, California, 94305-3079 USA",
month = "17 " # mar,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-212568-8",
notes = "part of \cite{koza:1998:GAGPs}",
}
@InProceedings{Engler:2009:ieeeAUTOTESTCON,
author = "Joseph Engler",
title = "Optimization of test engineering utilizing
evolutionary computation",
booktitle = "IEEE AUTOTESTCON, 2009",
year = "2009",
month = sep,
pages = "447--452",
keywords = "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",
doi = "doi:10.1109/AUTEST.2009.5314025",
ISSN = "1088-7725",
abstract = "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.",
notes = "Also known as \cite{5314025}",
}
@Article{Engoren:2008:GPEM,
author = "Milo Engoren and Jeffrey A. Kline",
title = "Use of genetic programming to diagnose venous
thromboembolism in the emergency department",
journal = "Genetic Programming and Evolvable Machines",
year = "2008",
volume = "9",
number = "1",
pages = "39--51",
month = mar,
keywords = "genetic algorithms, genetic programming, Pulmonary
embolism, Venous thromboembolic disease, Capnometry,
Oximetry",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-007-9050-x",
size = "13 pages",
abstract = "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.",
notes = "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",
}
@InProceedings{Eppstein:gecco06lbp,
author = "Margaret J. Eppstein and Joshua L. Payne and Bill C.
White and Jason H. Moore",
title = "Hill-climbing through {"}random chemistry{"} for
detecting epistasis",
booktitle = "Late breaking paper at Genetic and Evolutionary
Computation Conference {(GECCO'2006)}",
year = "2006",
month = "8-12 " # jul,
editor = "J{\"{o}}rn Grahl",
address = "Seattle, WA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006etc/papers/lbp111.pdf",
notes = "Distributed on CD-ROM at GECCO-2006",
keywords = "genetic algorithms, genetic programming, Population
based optimisation, epistasis, SNPs, data mining.",
abstract = "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.",
}
@Article{Eppstein:2007:GPEM,
author = "Margaret J. Eppstein and Joshua L. Payne and Bill C.
White and Jason H. Moore",
title = "Genomic mining for complex disease traits with
``random chemistry''",
journal = "Genetic Programming and Evolvable Machines",
year = "2007",
volume = "8",
number = "4",
pages = "395--411",
month = dec,
note = "special issue on medical applications of Genetic and
Evolutionary Computation",
keywords = "Evolutionary algorithms, Epistasis, Single nucleotide
polymorphisms, Data mining, Genome-wide association
studies, Complex traits, Feature selection",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-007-9039-5",
abstract = "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.",
notes = "SNP, ROC, AUC",
}
@InProceedings{erba:2001:EuroGP,
author = "Massimiliano Erba and Roberto Rossi and Valentino
Liberali and Andrea Tettamanzi",
title = "An Evolutionary Approach to Automatic Generation of
{VHDL} Code for Low-Power Digital Filters",
booktitle = "Genetic Programming, Proceedings of EuroGP'2001",
year = "2001",
editor = "Julian F. Miller and Marco Tomassini and Pier Luca
Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
William B. Langdon",
volume = "2038",
series = "LNCS",
pages = "36--50",
address = "Lake Como, Italy",
publisher_address = "Berlin",
month = "18-20 " # apr,
organisation = "EvoNET",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, Evolvable
Hardware, Evolutionary Algorithms, Electronic Design,
Digital Filters, VHDL",
ISBN = "3-540-41899-7",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=36",
size = "15 pages",
abstract = "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.",
notes = "EuroGP'2001, part of \cite{miller:2001:gp}",
}
@InProceedings{eriksson97,
author = "Roger Eriksson and Bj{\"{o}}rn Olsson",
title = "Cooperative Coevolution in Inventory Control
Optimisation",
year = "1997",
booktitle = "Artificial Neural Nets and Genetic Algorithms:
Proceedings of the International Conference,
ICANNGA97",
editor = "George D. Smith and Nigel C. Steele and Rudolf F.
Albrecht",
publisher = "Springer-Verlag",
address = "University of East Anglia, Norwich, UK",
note = "published in 1998",
keywords = "genetic algorithms",
ISBN = "3-211-83087-1",
notes = "ICANNGA97",
}
@Article{Eriksson:2004:BS,
author = "R. Eriksson and B. Olsson",
title = "Adapting genetic regulatory models by genetic
programming",
journal = "Biosystems",
year = "2004",
volume = "76",
pages = "217--227",
number = "1-3",
abstract = "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.",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6T2K-4D09KY2-7/2/1abfe196bb4afc60afc3311cadb75d66",
keywords = "genetic algorithms, genetic programming, Gene
networks, Evolutionary algorithms, Machine learning",
doi = "doi:10.1016/j.biosystems.2004.05.014",
notes = "Papers presented at the Fifth International Workshop
on Information Processing in Cells and Tissues
PMID: 15351145 [PubMed - indexed for MEDLINE]",
}
@InProceedings{Escazut:1997:ccscts,
author = "Cathy Escazut and Terence C. Fogarty",
title = "Coevolving Classifier Systems to Control Traffic
Signals",
booktitle = "Late Breaking Papers at the 1997 Genetic Programming
Conference",
year = "1997",
editor = "John R. Koza",
address = "Stanford University, CA, USA",
publisher_address = "Stanford University, Stanford, California,
94305-3079, USA",
month = "13--16 " # jul,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-206995-8",
notes = "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",
}
@InProceedings{eurogp:EscuelaOK05,
author = "Gabi Escuela and Gabriela Ochoa and Natalio
Krasnogor",
editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre
Collet and Jano I. {van Hemert} and Marco Tomassini",
title = "Evolving {L}-Systems to Capture Protein Structure
Native Conformations",
booktitle = "Proceedings of the 8th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3447",
year = "2005",
address = "Lausanne, Switzerland",
month = "30 " # mar # " - 1 " # apr,
organisation = "EvoNet",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-25436-6",
pages = "74--84",
URL = "http://www.cs.nott.ac.uk/~nxk/PAPERS/LsysPSP05.pdf",
URL = "http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=74",
doi = "doi:10.1007/b107383",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in
conjunction with EvoCOP2005 and EvoWorkshops2005",
}
@Article{Esfahanipour:2010:IJRIS,
author = "Akbar Esfahanipour and Somaye Mousavi",
title = "Genetic programming application to generate technical
trading rules in stock markets",
journal = "International Journal of Reasoning-based Intelligent
Systems",
year = "2010",
volume = "2",
number = "3/4",
pages = "244--250",
keywords = "genetic algorithms, genetic programming, technical
trading rules, stock markets, tehran stock exchange,
TSE, Iran, decision making, stock trading",
ISSN = "1755-0564",
bibsource = "OAI-PMH server at www.inderscience.com",
language = "eng",
URL = "http://www.inderscience.com/link.php?id=36870",
abstract = "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.",
}
@Article{Esfahanipour20118438,
author = "Akbar Esfahanipour and Somayeh Mousavi",
title = "A genetic programming model to generate risk-adjusted
technical trading rules in stock markets",
journal = "Expert Systems with Applications",
volume = "38",
number = "7",
pages = "8438--8445",
year = "2011",
ISSN = "0957-4174",
doi = "doi:10.1016/j.eswa.2011.01.039",
URL = "http://www.sciencedirect.com/science/article/B6V03-52178YW-J/2/5208571320b6e5c08daf35597b9f81f4",
keywords = "genetic algorithms, genetic programming, Technical
trading rules, Risk-adjusted measures, Conditional
Sharpe ratio, Tehran Stock Exchange (TSE)",
abstract = "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.",
}
@Article{Eskil2008774,
author = "Murat Eskil and Erdogan Kanca",
title = "A new formulation for martensite start temperature of
Fe-Mn-Si shape memory alloys using genetic
programming",
journal = "Computational Materials Science",
volume = "43",
number = "4",
pages = "774--784",
year = "2008",
keywords = "genetic algorithms, genetic programming, Martensite
start temperature, Fe-Mn-Si alloys, Shape memory
effect, Formulation and modelling",
ISSN = "0927-0256",
URL = "http://www.sciencedirect.com/science/article/B6TWM-4S1BT8K-1/2/8c255199aba8337ed54aa30bf0ec4ab4",
doi = "doi:10.1016/j.commatsci.2008.01.042",
abstract = "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.",
}
@InProceedings{eskin:1999:Othello,
author = "E. Eskin and Eric V. Siegel",
title = "Genetic Programming Applied to Othello: Introducing
Students to Machine Learning Research",
booktitle = "30th Technical Symposium of the ACM Special Interest
Group in Computer Science Education",
year = "1999",
editor = "Daniel Joyce",
volume = "31.1",
series = "SIGCSE Bulletin",
pages = "242--246",
address = "New Orleans, LA, USA",
month = "24-28 " # mar,
publisher = "ACM Press",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.columbia.edu/~evs/papers/sigcse-paper.ps",
abstract = "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 .",
}
@InProceedings{eskridge:2004:isamcofgp,
title = "Imitating Success: {A} Memetic Crossover Operator for
Genetic Programming",
author = "Brent Eskridge and Dean Hougen",
pages = "809--815",
booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary
Computation",
year = "2004",
publisher = "IEEE Press",
month = "20-23 " # jun,
address = "Portland, Oregon",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming, Theory of
evolutionary algorithms, Poster Session",
abstract = "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.",
notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and
the IEE.",
}
@InProceedings{eskridge:mcf:gecco2004,
author = "Brent E. Eskridge and Dean F. Hougen",
title = "Memetic Crossover for Genetic Programming: Evolution
Through Imitation",
booktitle = "Genetic and Evolutionary Computation -- GECCO-2004,
Part II",
year = "2004",
editor = "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",
series = "Lecture Notes in Computer Science",
pages = "459--470",
address = "Seattle, WA, USA",
publisher_address = "Heidelberg",
month = "26-30 " # jun,
organisation = "ISGEC",
publisher = "Springer-Verlag",
volume = "3103",
ISBN = "3-540-22343-6",
ISSN = "0302-9743",
URL = "http://link.springer.de/link/service/series/0558/bibs/3103/31030459.htm",
size = "12",
keywords = "genetic algorithms, genetic programming",
notes = "GECCO-2004 A joint meeting of the thirteenth
international conference on genetic algorithms
(ICGA-2004) and the ninth annual genetic programming
conference (GP-2004)",
}
@InProceedings{Eskridge:2006:CEC,
author = "Brent E. Eskridge and Dean F. Hougen",
title = "An Analysis of Memetic Crossover's Impact on a
Population",
booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary
Computation",
year = "2006",
editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and
Simon M. Lucas",
pages = "6844--6850",
address = "Vancouver",
month = "6-21 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-9487-9",
size = "7 pages",
abstract = "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",
notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
the IEE.
IEEE Catalog Number: 06TH8846D",
}
@InProceedings{Esmeraldo:2010:SPL,
author = "Guilherme Esmeraldo and Edna Barros",
title = "A Genetic Programming based approach for efficiently
exploring architectural communication design space of
{MPSoCs}",
booktitle = "VI Southern Programmable Logic Conference (SPL 2010)",
year = "2010",
month = "24-26 " # mar,
pages = "29--34",
address = "Ipojuca, Brazil",
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/SPL.2010.5483006",
notes = "Also known as \cite{5483006}",
}
@TechReport{esparcia:1995:95012,
author = "K. C. Sharman and A. I. Esparcia-Alcazar and Y. Li",
title = "Evolving Digital Signal Processing Algorithms by
Genetic Programming",
institution = "Faculty of Engineering",
year = "1995",
type = "Technical Report",
number = "CSC-95012",
address = "Glasgow G12 8QQ, Scotland",
month = "31 " # mar,
keywords = "genetic algorithms, genetic programming, simulated
annealing, digital signal processing, neural networks",
URL = "http://www.mech.gla.ac.uk/Research/Control/Publications/Reports/csc95012.ps",
URL = "http://www.mech.gla.ac.uk/Research/Control/Publications/Rabstracts/abs95012.html",
abstract = "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.",
notes = "Also submitted to: Proc. First IEE/IEEE Int. Conf. on
GA in Eng. Syst.: Innovations and Appl., Sheffield,
Sept. 1995, pp.473-480.",
size = "8 pages",
}
@TechReport{esparcia:1996:96009,
author = "Anna I. Esparcia-Alcazar and Ken C. Sharman",
title = "Evolving Recurrent Neural Network Architectures by
Genetic Programming",
institution = "Faculty of Engineering",
year = "1996",
type = "Technical Report",
number = "CSC-96009",
address = "Glasgow G12 8QQ, Scotland",
keywords = "genetic algorithms, genetic programming, Recurrent
Neural Networks, Simulated annealing, Digital Signal
Processing",
URL = "http://www.mech.gla.ac.uk/Research/Control/Publications/Reports/csc96009.ps",
URL = "http://www.mech.gla.ac.uk/Research/Control/Publications/Rabstracts/abs96009.html",
abstract = "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.",
size = "pages",
}
@TechReport{esparcia:1996:96010,
author = "Anna I. Esparcia-Alcazar and Ken C. Sharman",
title = "Application of Genetic Programming to Signal
Processing Problems",
institution = "Faculty of Engineering",
year = "1996",
type = "Technical Report",
number = "CSC-96010",
address = "Glasgow G12 8QQ, Scotland",
keywords = "genetic algorithms, genetic programming, Digital
Signal Processing Simulated Annealing, Adaptive
Filtering",
URL = "http://www.mech.gla.ac.uk/Research/Control/Publications/Reports/csc96010.ps",
URL = "http://www.mech.gla.ac.uk/Research/Control/Publications/Rabstracts/abs96010.html",
abstract = "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.",
notes = "Also submitted to: Late-breaking papers at the Genetic
Programming 96 Conference, Stanford, USA, July 1996
\cite{esparcia:1996:GPdsp}",
size = "pages",
}
@InProceedings{esparcia:1996:GPdsp,
author = "Anna I. {Esparcia Alcazar} and Ken C. Sharman",
title = "Some Applications of Genetic Programming in Digital
Signal Processing",
booktitle = "Late Breaking Papers at the Genetic Programming 1996
Conference Stanford University July 28-31, 1996",
year = "1996",
editor = "John R. Koza",
pages = "24--31",
address = "Stanford University, CA, USA",
publisher_address = "Stanford University, Stanford, California
94305-3079, USA",
month = "28--31 " # jul,
publisher = "Stanford Bookstore",
ISBN = "0-18-201031-7",
keywords = "genetic algorithms, genetic programming, DSP",
URL = "http://www.iti.upv.es/~anna/papers/someappsgp96.ps",
notes = "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 \cite{esparcia:1996:96010}",
}
@InProceedings{esparcia:1996:GPerNNasp,
author = "Anna I. Esparcia-Alcazar and Kenneth C. Sharman",
title = "Genetic Programming Techniques that Evolve Recurrent
Neural Networks Architectures for Signal Processing",
booktitle = "IEEE Workshop on Neural Networks for Signal
Processing",
year = "1996",
month = "4-6 " # sep,
pages = "139--148",
address = "Seiko, Kyoto, Japan",
publisher = "IEEE",
keywords = "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",
doi = "doi:10.1109/NNSP.1996.548344",
size = "10 pages",
abstract = "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",
}
@InProceedings{esparcia:1997:GPdsp,
author = "Anna I. Esparcia-Alcazar and Ken Sharman",
title = "Evolving Recurrent Neural Network Architectures by
Genetic Programming",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "89--94",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
URL = "http://www.iti.upv.es/~anna/papers/gp-rnn97.ps",
notes = "GP-97",
}
@InProceedings{Esparcia-Alcazar:1997:lsGP,
author = "Anna I. Esparcia-Alcazar and Ken Sharman",
title = "Learning Schemes for Genetic Programming",
booktitle = "Late Breaking Papers at the 1997 Genetic Programming
Conference",
year = "1997",
editor = "John R. Koza",
pages = "57--65",
address = "Stanford University, CA, USA",
publisher_address = "Stanford University, Stanford, California,
94305-3079, USA",
month = "13--16 " # jul,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-206995-8",
URL = "http://www.iti.upv.es/~anna/papers/learningGP97.ps",
notes = "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",
}
@InProceedings{Esparcia-Alcazar:1997:iGPtasp,
author = "Anna I Esparcia-Alcazar",
title = "An investigation into a Genetic Programming Technique
for Adaptive Signal Processing",
booktitle = "Late Breaking Papers at the 1997 Genetic Programming
Conference",
year = "1997",
editor = "John R. Koza",
pages = "290",
address = "Stanford University, CA, USA",
publisher_address = "Stanford University, Stanford, California,
94305-3079, USA",
month = "13--16 " # jul,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-206995-8",
notes = "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",
}
@PhdThesis{Esparcia-Alcazar:1998:thesis,
author = "Anna I. Esparcia-Alcazar",
title = "Genetic Programming for Adaptive Signal Processing",
school = "Electronics and Electrical Engineering, University of
Glasgow",
year = "1998",
month = jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/esparcia-alcazar/thesis.ps.gz",
URL = "http://www.iti.upv.es/~anna/papers/Thesis.zip",
size = "142 pages",
abstract = "This thesis is devoted to presenting the application
of the Genetic Programming (GP) paradigm to a class of
Digital Signal Processing (DSP) problems. Its main
contributions are
a new methodology for representing Discrete-Time
Dynamic Systems (DDS) as expression trees. The
objective is the state space specification of DDSs: the
behaviour of a system for a time instant t_0 is
completely accounted for given the inputs to the system
and also a set of quantities which specify the state of
the system. This means that the proposed method must
incorporate a form of memory that will handle this
information.
For this purpose a number of node types and associated
data structures are defined. These will allow for the
implementation of local and time recursion and also
other specific functions, such as the sigmoid commonly
encountered in neural networks. An example is given by
representing a recurrent NN as an expression tree.
a new approach to the channel equalisation problem. A
survey of existing methods for channel equalisation
reveals that the main shortcoming of these techniques
is that they rely on the assumption of a particular
structure or model for the system addressed. This
implies that knowledge about the system is available;
otherwise the solution obtained will have a poor
performance because it was not well matched to the
problem.
This gives a main motivation for applying GP to channel
equalisation, which is done in this work for the first
time. Firstly, to provide a unified technique for a
wide class of problems, including those which are
poorly understood; and secondly, to find alternative
solutions to those problems which have been
successfully addressed by existing techniques.
In particular, in the equalisation of nonlinear
channels, which have been mainly addressed with Neural
Networks and various adaptation algorithms, the
proposed GP approach presents itself as an interesting
alternative.",
abstract = "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.",
}
@InProceedings{esparcia-alcazar:1999:ppGPcdlis,
author = "Anna Esparcia-Alcazar and Ken Sharman",
title = "Phenotype Plasticity in Genetic Programming: {A}
Comparison of {Darwinian} and {Lamarckian} Inheritance
Schemes",
booktitle = "Genetic Programming, Proceedings of EuroGP'99",
year = "1999",
editor = "Riccardo Poli and Peter Nordin and William B. Langdon
and Terence C. Fogarty",
volume = "1598",
series = "LNCS",
pages = "49--64",
address = "Goteborg, Sweden",
publisher_address = "Berlin",
month = "26-27 " # may,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-65899-8",
URL = "http://www.iti.upv.es/~anna/papers/eurogp99.ps",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1598&spage=49",
notes = "EuroGP'99, part of \cite{poli: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).",
}
@InProceedings{esparcia-alcazar:1999:GPce,
author = "Anna Esparcia-Alcazar and Ken Sharman",
title = "Genetic Programming for Channel Equalisation",
booktitle = "Evolutionary Image Analysis, Signal Processing and
Telecommunications: First European Workshop, EvoIASP'99
and EuroEcTel'99",
year = "1999",
editor = "Riccardo Poli and Hans-Michael Voigt and Stefano
Cagnoni and Dave Corne and George D. Smith and Terence
C. Fogarty",
volume = "1596",
series = "LNCS",
pages = "126--137",
address = "Goteborg, Sweden",
publisher_address = "Berlin",
month = "28-29 " # may,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-65837-8",
URL = "http://www.iti.upv.es/~anna/papers/evoiasp99.ps",
doi = "doi:10.1007/10704703_10",
URL = "http://citeseer.ist.psu.edu/286482.html",
abstract = "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.",
notes = "EvoIASP99'99",
}
@Proceedings{Esparcia-Alcazar:2009:gecco,
title = "{GECCO} '09: Proceedings of the 11th annual conference
companion on Genetic and evolutionary computation
conference",
year = "2009",
editor = "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",
month = "8-12 " # jul,
address = "Montreal, Qu\'{e}bec, Canada",
publisher = "ACM",
publisher_address = "New York, NY, USA",
organisation = "SigEVO",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-60558-505-5",
notes = "GECCO 2009 workshops",
}
@Proceedings{Esparcia-Alcazar:2010:GP,
title = "Proceedings of the 13th European Conference on Genetic
Programming, Euro{GP} 2010",
year = "2010",
editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara
Silva and Stephen Dignum and A. Sima Uyar",
volume = "6021",
series = "LNCS",
address = "Istanbul",
month = "7-9 " # apr,
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-12147-0",
doi = "doi:10.1007/978-3-642-12148-7",
size = "310 pages",
notes = "EuroGP'2010 held in conjunction with EvoCOP2010
EvoBIO2010 and EvoApplications2010",
}
@Article{espejo:2006:AEPIA,
author = "Pedro G. Espejo and Cesar Hervas and Sebastian Ventura
and Cristobal Romero",
title = "Eleccion de Operadores Logicos para la Induccion de
Conocimiento Comprensible",
journal = "Inteligencia Artificial, Revista Iberoamericana de
Inteligencia Artificial",
year = "2006",
volume = "29",
pages = "19--30",
note = "Ejemplar dedicado a: Mineria de Datos",
keywords = "genetic algorithms, genetic programming, Grammatical
Evolution, Mineria de datos, Clasificacion,
Comprensibilidad, Programacion genetica gramatical",
ISSN = "1137-3601",
URL = "http://sci2s.ugr.es/keel/pdf/keel/articulo/1cr-1r-2r.pdf",
size = "12 pages",
resumen = "Son varias las caracteristicas que determinan la
calidad del conocimiento obtenido en el proceso de
mineria de datos. De estas caracteristicas, a la que
mas atencion se ha dedicado tradicionalmente ha sido la
precision, relegandose a un segundo plano la
comprensibilidad. En este trabajo desarrollamos un
sistema de mineria de datos orientado a la tarea de
clasificacion, utilizando reglas como formalismo de
representacion. El objetivo principal es analizar el
balance entre precision y comprensibilidad,
centrandonos en un aspecto de la comprensibilidad poco
tratado hasta la fecha: el que viene determinado por la
eleccion de los operadores logicos que pueden aparecer
en el antecedente de las reglas. El sistema de mineria
desarrollado se basa en la programacion genetica
gramatical, ya que otro objetivo de nuestro trabajo es
estudiar la utilidad de esta tecnica evolutiva para
llevar a cabo tareas de mineria.",
abstract = "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",
notes = "c AEPIA (http://www.aepia.dsic.upv.es/)
In Spanish",
}
@Article{Espejo:2010:ieeetSMC,
author = "Pedro G. Espejo and Sebastian Ventura and Francisco
Herrera",
title = "A Survey on the Application of Genetic Programming to
Classification",
journal = "IEEE Transactions on Systems, Man, and Cybernetics,
Part C: Applications and Reviews",
year = "2010",
month = mar,
volume = "40",
number = "2",
pages = "121--144",
keywords = "genetic algorithms, genetic programming,
Classification, decision trees, ensemble classifiers,
feature construction, feature selection, rule-based
systems",
abstract = "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.",
doi = "doi:10.1109/TSMCC.2009.2033566",
ISSN = "1094-6977",
notes = "Also known as \cite{5340522}",
}
@InProceedings{Esposito:2007:PriCKL,
author = "Floriana Esposito and Nicola Fanizzi and Claudia
d'Amato",
title = "Conceptual Clustering Applied to Ontologies by means
of Semantic Discernability",
booktitle = "ECML/PKDD Workshop on Prior Conceptual Knowledge in
Machine Learning and Knowledge Discovery, PriCKL'07",
year = "2007",
address = "Warsaw, Poland",
month = sep # " 21",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.ecmlpkdd2007.org/CD/workshops/PRICKLWM2/P_Fan/PriCKL07/PriCkl2007-final.pdf",
abstract = "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",
notes = "Says based on GP and Simulated Annealing
Dipartimento di Informatica, Universit`a degli Studi di
Bari Campus Universitario, Via Orabona 4, 70125 Bari,
Italy",
}
@InProceedings{essam:2001:acpfgp,
author = "Daryl Essam and R. I. Bob McKay",
title = "Adaptive Control of Partial Functions in Genetic
Programming",
booktitle = "Proceedings of the 2001 Congress on Evolutionary
Computation CEC2001",
year = "2001",
pages = "895--901",
address = "COEX, World Trade Center, 159 Samseong-dong,
Gangnam-gu, Seoul, Korea",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "27-30 " # may,
organisation = "IEEE Neural Network Council (NNC), Evolutionary
Programming Society (EPS), Institution of Electrical
Engineers (IEE)",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, Partial
Functions, Fitness Evaluation",
ISBN = "0-7803-6658-1",
URL = "http://www.cs.adfa.edu.au/~rim/PAPERS/CEC01final.pdf",
doi = "doi:10.1109/CEC.2001.934285",
size = "7 pages",
abstract = "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",
notes = "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. \cite{ross: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.",
}
@Article{essam:2002:GPEM,
author = "Daryl Essam",
title = "Book Review: {Blondie24}: Playing at the Edge of
{AI}",
journal = "Genetic Programming and Evolvable Machines",
year = "2002",
volume = "3",
number = "4",
pages = "389--390",
month = dec,
ISSN = "1389-2576",
doi = "doi:10.1023/A:1020941026832",
notes = "Article ID: 5103876",
}
@InProceedings{Essam:2004:SEAL,
author = "Daryl Essam and R I (Bob) McKay",
title = "Heritage Diversity in Genetic Programming",
booktitle = "The 5th International Conference on Simulated
Evolution And Learning (SEAL'04)",
year = "2004",
address = "Busan, Korea",
month = oct # " 26-29",
keywords = "genetic algorithms, genetic programming, diversity",
}
@InProceedings{DBLP:conf/icann/EstebanezVAG05,
author = "C{\'e}sar Est{\'e}banez and Jos{\'e} Mar\'{\i}a Valls
and Ricardo Aler and In{\'e}s Mar\'{\i}a Galv{\'a}n",
title = "A First Attempt at Constructing Genetic Programming
Expressions for {EEG} Classification",
year = "2005",
pages = "665--670",
keywords = "genetic algorithms, genetic programming, EEG, BCI,
brain computer interface, projection",
bibsource = "DBLP, http://dblp.uni-trier.de",
editor = "Wlodzislaw Duch and Janusz Kacprzyk and Erkki Oja and
Slawomir Zadrozny",
booktitle = "Artificial Neural Networks: Biological Inspirations -
ICANN 2005, 15th International Conference, 2005,
Proceedings, Part I",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3696",
ISBN = "3-540-28752-3",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3696&spage=665",
doi = "doi:10.1007/11550822_103",
address = "Warsaw, Poland",
month = "11-15 " # sep,
abstract = "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.",
}
@InProceedings{conf/wec/EstebanezAV05,
title = "Genetic Programming Based Data Projections for
Classification Tasks",
author = "Cesar Estebanez and Ricardo Aler and Jose Maria
Valls",
year = "2005",
pages = "56--61",
editor = "Cemal Ardil",
publisher = "Enformatika, \c{C}anakkale, Turkey",
booktitle = "International Enformatika Conference, IEC'05",
volume = "7",
address = "Prague, Czech Republic",
month = aug # " 26-28",
organisation = "World Enformatika Society",
note = "CDROM",
bibdate = "2005-10-13",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/wec/iec2005prague.html#EstebanezAV05",
keywords = "genetic algorithms, genetic programming",
ISBN = "975-98458-6-5",
notes = "http://www.enformatika.org/proceedings.html",
}
@InProceedings{eurogp06:EstebanezVallsAler,
author = "C\'esar Est\'ebanez and Jos\'e M. Valls and Ricardo
Aler",
title = "Projecting Financial Data using Genetic Programming in
Classification and Regression Tasks",
editor = "Pierre Collet and Marco Tomassini and Marc Ebner and
Steven Gustafson and Anik\'o Ek\'art",
booktitle = "Proceedings of the 9th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3905",
year = "2006",
address = "Budapest, Hungary",
month = "10 - 12 " # apr,
organisation = "EvoNet",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-33143-3",
pages = "202--212",
URL = "http://link.springer.de/link/service/series/0558/papers/3905/39050202.pdf",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in
conjunction with EvoCOP2006 and EvoWorkshops2006",
}
@InProceedings{1144300,
author = "Cesar Estebanez and Julio Cesar Hernandez-Castro and
Arturo Ribagorda and Pedro Isasi",
title = "Evolving hash functions by means of genetic
programming",
booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference
on Genetic and evolutionary computation",
year = "2006",
editor = "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",
volume = "2",
ISBN = "1-59593-186-4",
pages = "1861--1862",
address = "Seattle, Washington, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p1861.pdf",
doi = "doi:10.1145/1143997.1144300",
publisher = "ACM Press",
publisher_address = "New York, NY, 10286-1405, USA",
month = "8-12 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, Real-World
Applications: Poster, avalanche effect, hash
functions",
size = "2 pages",
abstract = "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.",
notes = "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",
}
@InProceedings{Estebanez:PPSN:2006,
author = "Cesar Estebanez and Julio Cesar Hernandez-Castro and
Arturo Ribagorda and Pedro Isasi",
title = "Finding State-of-the-Art Non-cryptographic Hashes with
Genetic Programming",
booktitle = "Parallel Problem Solving from Nature - PPSN IX",
year = "2006",
editor = "Thomas Philip Runarsson and Hans-Georg Beyer and
Edmund Burke and Juan J. Merelo-Guervos and L. Darrell
Whitley and Xin Yao",
volume = "4193",
pages = "818--827",
series = "LNCS",
address = "Reykjavik, Iceland",
publisher_address = "Berlin",
month = "9-13 " # sep,
publisher = "Springer-Verlag",
ISBN = "3-540-38990-3",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1007/11844297_83",
size = "10 pages",
abstract = "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.",
notes = "PPSN-IX",
}
@Article{journals/ijcsa/EstebanezAV07,
author = "Cesar Estebanez and Ricardo Aler and Jose Maria
Valls",
title = "A Method Based on Genetic Programming for Improving
the Quality of Datasets in Classification Problems",
journal = "International Journal of Computer Science and
Applications",
year = "2007",
volume = "4",
number = "1",
pages = "69--80",
keywords = "genetic algorithms, genetic programming,
Classification, projections",
ISSN = "0972-9038",
URL = "http://www.tmrfindia.org/ijcsa/V4I17.pdf",
size = "12 pages",
abstract = "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.",
notes = "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",
bibdate = "2007-10-30",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/ijcsa/ijcsa4.html#EstebanezAV07",
}
@InProceedings{Estebanez:2009:eurogp,
author = "Cesar Estebanez and Ricardo Aler and Jose M. Valls and
Pablo Alonso",
title = "An experimental study on fitness distributions of tree
shapes in {GP} with One-Point Crossover",
booktitle = "Proceedings of the 12th European Conference on Genetic
Programming, EuroGP 2009",
year = "2009",
editor = "Leonardo Vanneschi and Steven Gustafson and Alberto
Moraglio and Ivanoe {De Falco} and Marc Ebner",
volume = "5481",
series = "LNCS",
pages = "244--255",
address = "Tuebingen",
month = apr # " 15-17",
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-01180-1",
doi = "doi:10.1007/978-3-642-01181-8_21",
notes = "Part of \cite{conf/eurogp/2009} EuroGP'2009 held in
conjunction with EvoCOP2009, EvoBIO2009 and
EvoWorkshops2009",
}
@Article{Estevez:2005:EL,
author = "P. A. Estevez and N. Becerra-Yoma and N. Boric and J.
A. Ramirez",
title = "Genetic programming-based voice activity detection",
journal = "Electronics Letters",
year = "2005",
volume = "41",
pages = "1141--1143",
month = "29 " # sep,
keywords = "genetic algorithms, genetic programming",
ISSN = "0013-5194",
doi = "doi:10.1049/el:20052475",
abstract = "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.",
notes = "Dept. of Electr. Eng., Univ. de Chile, Santiago,
Chile",
}
@Article{Estevez:2008:GPEM,
author = "Pablo A. Estevez",
title = "Russel {C}. Eberhart, Yuhui Shi: Computational
Intelligence: Concepts to Implementation",
journal = "Genetic Programming and Evolvable Machines",
year = "2008",
volume = "9",
number = "4",
pages = "367--369",
month = dec,
keywords = "genetic algorithms, genetic programming",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-008-9064-z",
size = "3 pages",
abstract = "Book review",
}
@Article{Estrada-Gil:2007:BI,
author = "Jesus K. Estrada-Gil and Juan C. Fernandez-Lopez and
Enrique Hernandez-Lemus and Irma Silva-Zolezzi and
Alfredo Hidalgo-Miranda and Gerardo Jimenez-Sanchez and
Edgar E. Vallejo-Clemente",
title = "{GPDTI}: {A} Genetic Programming Decision Tree
Induction method to find epistatic effects in common
complex diseases",
journal = "Bioinformatics",
year = "2007",
volume = "13",
number = "13",
pages = "i167--i174",
keywords = "genetic algorithms, genetic programming",
ISSN = "1460-2059",
doi = "doi:10.1093/bioinformatics/btm205",
abstract = "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.",
notes = "PMID: 17646293 [PubMed - in process]",
}
@Article{Etemadi20093199,
title = "A genetic programming model for bankruptcy prediction:
Empirical evidence from Iran",
author = "Hossein Etemadi and Ali Asghar Anvary Rostamy and
Hassan Farajzadeh Dehkordi",
journal = "Expert Systems with Applications",
volume = "36",
number = "2, Part 2",
pages = "3199--3207",
year = "2009",
ISSN = "0957-4174",
doi = "DOI:10.1016/j.eswa.2008.01.012",
URL = "http://www.sciencedirect.com/science/article/B6V03-4RSRDDN-4/2/acecffea7c551388162fae4dfbe2a6e2",
keywords = "genetic algorithms, genetic programming, Bankruptcy
prediction, Financial ratios, Multiple discriminant
analysis, Iranian companies",
abstract = "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.",
}
@InProceedings{Eto:2004:FLoGNPaiAtaPP,
title = "Functional Localization of Genetic Network Programming
and its Application to a Pursuit Problem",
author = "Shinji Eto and Kotaro Hirasawa and Jinglu Hu",
pages = "683--690",
booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary
Computation",
year = "2004",
publisher = "IEEE Press",
month = "20-23 " # jun,
address = "Portland, Oregon",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming, Evolutionary
intelligent agents, Poster Session",
abstract = "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.",
notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and
the IEE.",
}
@InProceedings{Eto:gecco06lbp,
author = "Shinji Eto and Shingo Mabu and Kotaro Hirasawa and
Jinglu Hu",
title = "Evolutionary method of Genetic Network Programing
Considering Breadth and Depth",
booktitle = "Late breaking paper at Genetic and Evolutionary
Computation Conference {(GECCO'2006)}",
year = "2006",
month = "8-12 " # jul,
editor = "J{\"{o}}rn Grahl",
address = "Seattle, WA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006etc/papers/lbp119.pdf",
notes = "Distributed on CD-ROM at GECCO-2006",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
}
@InProceedings{Eto:2007:cec,
author = "Shinji Eto and Shingo Mabu and Kotaro Hirasawa and
Takayuki Huruzuki",
title = "Genetic Network Programming with Control Nodes",
booktitle = "2007 IEEE Congress on Evolutionary Computation",
year = "2007",
editor = "Dipti Srinivasan and Lipo Wang",
pages = "1023--1028",
address = "Singapore",
month = "25-28 " # sep,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "1-4244-1340-0",
file = "1128.pdf",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and
the IET.
IEEE Catalog Number: 07TH8963C",
}
@Article{Evans:2001:CEP,
author = "C. Evans and P. J. Fleming and D. C. Hill and J. P.
Norton and I. Pratt and D. Rees and K.
Rodriguez-Vazquez",
title = "Application of system identification techniques to
aircraft gas turbine engines",
journal = "Control Engineering Practice",
volume = "9",
pages = "135--148",
year = "2001",
number = "2",
month = feb,
keywords = "genetic algorithms, genetic programming, Gas turbines,
System identification, Frequency domain, Multisine
signals, Least-squares estimation, Time-varying
systems, Structure selection",
ISSN = "0967-0661",
URL = "http://www.sciencedirect.com/science/article/B6V2H-4280YP2-3/1/24d44180070f91dea854032d98f9187a",
doi = "doi:10.1016/S0967-0661(00)00091-5",
abstract = "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.",
}
@InProceedings{Evans:2008:IVCNZ,
author = "H. Evans and Mengjie Zhang",
title = "Particle swarm optimisation for object
classification",
booktitle = "23rd International Conference Image and Vision
Computing New Zealand, IVCNZ 2008",
year = "2008",
month = nov,
pages = "1--6",
keywords = "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",
doi = "doi:10.1109/IVCNZ.2008.4762143",
abstract = "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.",
notes = "Refers to \cite{zhang:2004:eurogp} Also known as
\cite{4762143}",
}
@InProceedings{evett:1987:rifs,
author = "Ian W. Evett and E. J. Spiehler",
title = "Rule Induction in Forensic Science",
booktitle = "KBS in Goverment",
year = "1987",
pages = "107--118",
publisher_address = "Pinner, UK",
publisher = "Online Publications",
keywords = "genetic algorithms, genetic programming, BEAGLE",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/evett_1987_rifs.pdf",
notes = "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?",
}
@InProceedings{evett:1999:MG,
author = "Matthew Evett and Taghi Khoshgoftaar and Pei-der Chien
and Edward Allen",
title = "Modelling software quality with {GP}",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1232",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, poster
papers, SBSE",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-462.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-462.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@TechReport{Evett97-agps-tr,
author = "M. Evett and T. Fernandez",
title = "A Distributed System for Genetic Programming that
Dynamically Allocates Processors",
institution = "Dept. Computer Science and Engineering, Florida
Atlantic University",
year = "1997",
address = "Boca Raton, FL, USA",
annote = "AGPS",
keywords = "genetic algorithms, genetic programming",
notes = "See \cite{Evett:1997:aaaiMAL}. parallel GP system,
AGPS, is based on MPI, not PVM",
}
@InProceedings{Evett:1997:aaaiMAL,
author = "Matthew Evett and Thomas Fernandez",
title = "A Distributed System for Genetic Programming that
Dynamically Allocates Processors",
booktitle = "Papers from the AAAI Workshop on Building
Resource-Bounded Reasoning Systems",
year = "1997",
editor = "Shlomo Zilberstein and Louis Hoebel",
pages = "43--48",
organisation = "AAAI",
note = "Published in AAAI Technical Report WS-97-06",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.aaai.org/Papers/Workshops/1997/WS-97-06/WS97-06-008.pdf",
size = "6 pages",
abstract = "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.",
notes = "http://www.aaai.org/Library/Workshops/ws97-06.php",
}
@InProceedings{evett:1998:GPsqp,
author = "Matthew Evett and Taghi Khoshgoftar and Pei-der Chien
and Edward Allen",
title = "{GP}-based software quality prediction",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "60--65",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, SBSE",
ISBN = "1-55860-548-7",
URL = "http://www.emunix.emich.edu/~evett/Publications/gp98-se.pdf",
size = "6 pages",
abstract = "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.",
notes = "GP-98",
}
@InProceedings{evett:1998:nmidncGP,
author = "Matthew Evett and Thomas Fernandez",
title = "Numeric Mutation Improves the Discovery of Numeric
Constants in Genetic Programming",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "66--71",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-548-7",
URL = "http://www.emunix.emich.edu/~evett/Publications/gp98-nm.pdf",
size = "6 pages",
abstract = "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.",
notes = "GP-98",
}
@InProceedings{Evett:1999:FLAIRS,
author = "Matthew Evett and Taghi Khoshgoftaar and Pei-der Chien
and Ed Allen",
title = "Using genetic programming to determine software
quality",
booktitle = "Proceedings of the Twelfth International FLAIRS
Conference",
year = "1999",
pages = "113--117",
publisher = "AAAI",
keywords = "genetic algorithms, genetic programming, SBSE",
URL = "http://www.aaai.org/Papers/FLAIRS/1999/FLAIRS99-020.pdf",
abstract = "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.",
notes = "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",
}
@Article{evonews:1999:mole,
key = "evonews",
title = "{MOLE} at City University",
journal = "EvoNEWS",
year = "1999",
volume = "11",
pages = "2--3",
month = "summer",
keywords = "genetic algorithms, genetic programming",
URL = "http://evonet.lri.fr/evoweb/files/evonews/evonews11.pdf",
abstract = "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)",
}
@Article{evonews:1999:art,
key = "evonews",
title = "Evol-artists - a new breed entirely",
journal = "EvoNEWS",
year = "1999",
volume = "11",
pages = "7--10",
month = "summer",
keywords = "genetic algorithms, genetic programming",
URL = "http://evonet.lri.fr/evoweb/files/evonews/evonews11.pdf",
abstract = "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.",
notes = "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",
}
@InCollection{Evstigneev2009507,
author = "Igor V. Evstigneev and Thorsten Hens and Klaus Reiner
Schenk-Hoppe",
title = "Evolutionary Finance",
editor = "Thorsten Hens and Klaus Reiner Schenk-Hoppe",
booktitle = "Handbook of Financial Markets: Dynamics and
Evolution",
publisher = "North-Holland",
address = "San Diego",
year = "2009",
pages = "507--566",
isbn13 = "978-0-12-374258-2",
doi = "doi:10.1016/B978-012374258-2.50013-0",
URL = "http://www.sciencedirect.com/science/article/B8N8N-4W6Y2CK-9/2/d140c798e01e01356572d883e6694255",
abstract = "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.",
}
@InProceedings{fabrega:1999:GANNCSG,
author = "Francesc Xavier Llora i Fabrega and Josep Maria
Garrell i Guiu",
title = "{GENIFER}: {A} Nearest Neighbour based Classifier
System using {GA}",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "797",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems, poster
papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-321.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-321.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{Fagan:2010:EuroGP,
author = "David Fagan and Michael O'Neill and Edgar Galvan-Lopez
and Anthony Brabazon and Sean McGarraghy",
title = "An Analysis of Genotype-Phenotype Maps in Grammatical
Evolution",
booktitle = "Proceedings of the 13th European Conference on Genetic
Programming, EuroGP 2010",
year = "2010",
editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara
Silva and Stephen Dignum and A. Sima Uyar",
volume = "6021",
series = "LNCS",
pages = "62--73",
address = "Istanbul",
month = "7-9 " # apr,
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-12147-0",
doi = "doi:10.1007/978-3-642-12148-7_6",
abstract = "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.",
notes = "Typed GP, GEVA, pi-GE, 5-parity, x+x^2+x^3+x^4, Santa
Fe trail, Max \cite{langdon:1997:MAX}.
Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010
held in conjunction with EvoCOP2010 EvoBIO2010 and
EvoApplications2010",
}
@InProceedings{fagan_etal:cec2010,
author = "David Fagan and Miguel Nicolau and Michael O'Neill and
Edgar Galvan-Lopez and Anthony Brabazon and Sean
McGarraghy",
title = "Investigating Mapping Order in pi{GE}",
booktitle = "2010 IEEE World Congress on Computational
Intelligence",
pages = "3058--3064",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
isbn13 = "978-1-4244-6910-9",
doi = "doi:10.1109/CEC.2010.5586204",
abstract = "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.",
notes = "WCCI 2010. Also known as \cite{5586204}",
}
@InProceedings{fagan:2011:EuroGP,
author = "David Fagan and Miguel Nicolau and Erik Hemberg and
Michael O'Neill and Anthony Brabazon and Sean
McGarraghy",
title = "Investigation of the Performance of Different Mapping
Orders for {GE} on the Max Problem",
booktitle = "Proceedings of the 14th European Conference on Genetic
Programming, EuroGP 2011",
year = "2011",
month = "27-29 " # apr,
editor = "Sara Silva and James A. Foster and Miguel Nicolau and
Mario Giacobini and Penousal Machado",
series = "LNCS",
volume = "6621",
publisher = "Springer Verlag",
address = "Turin, Italy",
pages = "286--297",
organisation = "EvoStar",
keywords = "genetic algorithms, genetic programming, Grammatical
Evolution: poster",
isbn13 = "978-3-642-20406-7",
doi = "doi:10.1007/978-3-642-20407-4_25",
abstract = "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.",
notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in
conjunction with EvoCOP2011 EvoBIO2011 and
EvoApplications2011",
}
@InProceedings{Fagan:2011:GECCOposter,
author = "David Fagan and Miguel Nicolau and Erik Hemberg and
Michael O'Neill and Anthony Brabazon",
title = "Dynamic ant: introducing a new benchmark for genetic
programming in dynamic environments",
booktitle = "GECCO '11: Proceedings of the 13th annual conference
companion on Genetic and evolutionary computation",
year = "2011",
editor = "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",
isbn13 = "978-1-4503-0690-4",
keywords = "genetic algorithms, genetic programming, grammatical
evolution: Poster",
pages = "183--184",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001858.2001961",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{2001961} Distributed on CD-ROM at
GECCO-2011.
ACM Order Number 910112.",
}
@InProceedings{Fagan:2011:GECCOcomp,
author = "David Fagan",
title = "Genotype-phenotype mapping in dynamic environments
with grammatical evolution",
booktitle = "GECCO 2011 Graduate students workshop",
year = "2011",
editor = "Miguel Nicolau",
isbn13 = "978-1-4503-0690-4",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
pages = "783--786",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001858.2002091",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{2002091} Distributed on CD-ROM at
GECCO-2011.
ACM Order Number 910112.",
}
@InProceedings{faglia:1996:mpdCAMGA,
author = "Rodolfo Faglia and David Vetturi",
title = "Motion Planning and Design of {CAM} Mechanisms by
Means of a Genetic Algorithm",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "Genetic Algorithms",
pages = "479--484",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96 GA paper",
}
@Article{fairley:2003:gbc,
author = "Peter Fairley",
title = "Germs that build Circuits",
journal = "IEEE Spectrim",
year = "2003",
pages = "36--41",
month = nov,
keywords = "nanotechnology",
URL = "http://ieeexplore.ieee.org/iel5/6/27854/01242955.pdf",
size = "5 pages",
abstract = "Circuits With viruses serving as construction crews
and DNA as the blueprint, biotechnology may hold the
key to postlithography integrated circuits",
notes = "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
",
}
@InProceedings{Falbo:2002:IFORS,
author = "Paolo Falbo and Nicola Doninelli",
title = "{"}Reverse engineering{"} of managed fund market
timing strategies",
booktitle = "The Sixteenth Triennial Conference of the
International Federation of Operational Research
Societies",
year = "2002",
address = "University of Edinburgh",
month = "8-12 " # jul,
organisation = "UK Operational Research Society",
note = "Conference theme: OR in a globalised, networked world
economy, Invited session",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "University of Brescia, Italy
http://meetings.informs.org/IFORS2002/working_files/program.pdf",
}
@InProceedings{Faler:2011:28C3,
author = "Wes Faler",
title = "Automatic Algorithm Invention with {GPU}",
booktitle = "28th Chaos Communication Congress",
year = "2011",
pages = "ID 4764",
address = "Berlin",
month = "27-30 " # dec,
keywords = "genetic algorithms, genetic programming, GPU,
Cartesian Genetic Programming",
URL = "http://events.ccc.de/congress/2011/Fahrplan/events/4764.en.html",
URL = "http://events.ccc.de/congress/2011/Fahrplan/attachments/2029_AutomaticAlgorithmInvention.pdf",
abstract = "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!",
notes = "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/",
}
@InProceedings{Faler:2011:28C3c,
author = "Wes Faler",
title = "Evolving custom communication protocols",
booktitle = "28th Chaos Communication Congress",
year = "2011",
pages = "ID 4818",
address = "Berlin",
month = "27-30 " # dec,
keywords = "genetic algorithms, genetic programming, GPU,
Cartesian Genetic Programming",
URL = "http://events.ccc.de/congress/2011/Fahrplan/events/4818.en.html",
URL = "http://events.ccc.de/congress/2011/Fahrplan/attachments/2054_EvolvingCustomCommunicationProtocols.pdf",
abstract = "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!",
notes = "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",
}
@InCollection{fan:1998:DADDCGP,
author = "John L. Fan",
title = "Design of an Adaptive Detector for Digital
Communications using Genetic Programming",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1998",
year = "1998",
editor = "John R. Koza",
pages = "11--19",
address = "Stanford, California, 94305-3079 USA",
month = "17 " # mar,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-212568-8",
notes = "part of \cite{koza:1998:GAGPs}",
}
@InProceedings{WeigueFan:1999:agmfGPeir,
author = "Weiguo Fan and Michael D. Gordon and Praveen Pathak",
title = "Automatic generation of matching functions by genetic
programming for effective information retrieval",
booktitle = "Proceedings of the 1999 Americas Conference on
Information Systems",
year = "1999",
editor = "W. David Haseman and Derek L. Nazareth",
pages = "49--51",
address = "Milwaukee, WI, USA",
month = "13-15 " # aug,
organisation = "Association for Information Systems",
keywords = "genetic algorithms, genetic programming",
URL = "http://filebox.vt.edu/users/wfan/paper/Amcis_final.pdf",
size = "3 pages",
abstract = "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.",
notes = "AMCIS99
https://commerce.mindspring.com/www.icisnet.org/proc.html
Prototype implemented in C. Fitness based on user
feedback
Duplicate entry \cite{Fan:1999:AMCIS} removed 21 Oct
2006",
}
@InProceedings{WeiguoFan:2000:icis,
author = "Weiguo Fan and Michael D. Gordon and Praveen Pathak",
title = "Personalization of Search Engine Services for
Effective Retrieval and Knowledge Management",
booktitle = "The Proceedings of the International Conference on
Information Systems 2000",
year = "2000",
pages = "20--34",
keywords = "genetic algorithms, genetic programming, information
retrieval",
URL = "http://filebox.vt.edu/users/wfan/paper/icis_final.pdf",
abstract = "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.",
}
@Article{Fan2003a,
author = "Weiguo Fan and Michael D. Gordon and Praveen Pathak",
title = "Discovery of context-specific ranking functions for
effective information retrieval using genetic
programming",
journal = "IEEE Transactions on Knowledge and Data Engineering",
year = "2004",
volume = "16",
number = "4",
pages = "523--527",
month = apr,
keywords = "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",
ISSN = "1041-4347",
doi = "doi:10.1109/TKDE.2004.1269663",
size = "5 pages",
abstract = "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.",
notes = "http://filebox.vt.edu/users/wfan/pub_area.html",
}
@Article{Fan2003b,
author = "Weiguo Fan and Michael D. Gordon and Praveen Pathak",
title = "A generic ranking function discovery framework by
genetic programming for information retrieval",
journal = "Information Processing and Management",
year = "2003",
volume = "40",
number = "4",
pages = "587--602",
keywords = "genetic algorithms, genetic programming, Information
retrieval; Ranking function, Text mining",
doi = "doi:10.1016/j.ipm.2003.08.001",
URL = "http://filebox.vt.edu/users/wfan/paper/ARRANGER/ip&m2003.pdf",
URL = "http://www.sciencedirect.com/science/article/B6VC8-49J8S58-2/2/158a3713b59ef9defad7d00e81707f66",
size = "16 pages",
abstract = "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.",
}
@InProceedings{Fan2004,
author = "Weiguo Fan and Michael D. Gordon and Praveen Pathak
and Wensi Xi and Edward A. Fox",
title = "Ranking Function Optimization For Effective Web Search
By Genetic Programming: An Empirical Study",
booktitle = "Proceedings of 37th Hawaii International Conference on
System Sciences",
year = "2004",
pages = "105--112",
address = "Hawaii",
month = "5-8 " # jan,
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1109/HICSS.2004.1265279",
size = "8 pages",
abstract = "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",
notes = "http://filebox.vt.edu/users/wfan/pub_area.html",
}
@Article{Fan2004dsstwostage,
author = "Weiguo Fan and Michael D. Gordon and Praveen Pathak",
title = "A two stage integrated model for intelligent
information routing",
journal = "Decision Support Systems",
year = "2006",
volume = "42",
number = "1",
pages = "362--374",
month = oct,
keywords = "genetic algorithms, genetic programming, Information
Routing, Information Retrieval, Personalization, Text
Mining",
abstract = "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.",
URL = "http://filebox.vt.edu/users/wfan/pub_area.html",
doi = "doi:10.1016/j.dss.2005.01.007",
}
@Article{Fan2004jasist,
author = "Weiguo Fan and Edward A. Fox and Praveen Pathak and
Harris Wu",
title = "The effects of fitness functions on genetic
programming-based ranking discovery for web search",
journal = "Journal of the American Society for Information
Science and Technology",
year = "2004",
volume = "55",
number = "7",
pages = "628--636",
keywords = "genetic algorithms, genetic programming, ranking
function, text mining, web search, information
retrieval",
URL = "http://filebox.vt.edu/users/wfan/paper/ARRANGER/JASIST2004.pdf",
doi = "doi:10.1002/asi.20009",
abstract = "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.",
}
@InProceedings{Fan2004sigir,
author = "Weiguo Fan and Ming Luo and Li Wang and Wensi Xi and
Edward A. Fox",
title = "Tuning before feedback: combining ranking function
discovery and blind feedback for robust retrieval",
booktitle = "the Proceedings of the 27th Annual International ACM
SIGIR Conference",
year = "2004",
address = "U.K.",
publisher = "ACM",
keywords = "genetic algorithms, genetic programming, intelligent
information retrieval, search engine, ranking function
discovery, information retrieval, blind feedback",
URL = "http://filebox.vt.edu/users/wfan/paper/ARRANGER/p52-Fan.pdf",
abstract = "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.",
}
@Article{journals/dss/FanPW06,
title = "Nonlinear ranking function representations in genetic
programming-based ranking discovery for personalized
search",
author = "Weiguo Fan and Praveen Pathak and Linda Wallace",
journal = "Decision Support Systems",
year = "2006",
number = "3",
volume = "42",
pages = "1338--1349",
month = dec,
bibdate = "2007-01-23",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/dss/dss42.html#FanPW06",
keywords = "genetic algorithms, genetic programming, Information
routing, Information retrieval, Ranking function",
doi = "doi:10.1016/j.dss.2005.11.002",
abstract = "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.",
}
@Article{Fan2009398,
author = "Weiguo Fan and Praveen Pathak and Mi Zhou",
title = "Genetic-based approaches in ranking function discovery
and optimization in information retrieval -- {A}
framework",
journal = "Decision Support Systems",
volume = "47",
number = "4",
pages = "398--407",
year = "2009",
note = "Smart Business Networks: Concepts and Empirical
Evidence",
ISSN = "0167-9236",
doi = "doi:10.1016/j.dss.2009.04.005",
URL = "http://www.sciencedirect.com/science/article/B6V8S-4W2W5G2-2/2/891e4aeaad9141e2bfe99d4477f96c1a",
keywords = "genetic algorithms, genetic programming, Information
retrieval, Artificial intelligence, Evolutionary
computations, Data fusion",
abstract = "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.",
}
@InProceedings{Fan:2010:ICNC,
author = "Xinqiao Fan and Yongli Zhu",
title = "The application of Empirical Mode Decomposition and
Gene Expression Programming to short-term load
forecasting",
booktitle = "Sixth International Conference on Natural Computation
(ICNC 2010)",
year = "2010",
month = "10-12 " # aug,
volume = "8",
pages = "4331--4334",
keywords = "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",
doi = "10.1109/ICNC.2010.5583605",
abstract = "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.",
notes = "also known as \cite{5583605}",
}
@InProceedings{fan:2001:bgrgaafd,
author = "Zhun Fan and Jianjun Hu and Kisung Seo and Erik D.
Goodman and Ronald C. Rosenberg and Baihai Zhang",
title = "Bond Graph Representation and {GP} for Automated
Analog Filter Design",
booktitle = "2001 Genetic and Evolutionary Computation Conference
Late Breaking Papers",
year = "2001",
editor = "Erik D. Goodman",
pages = "81--86",
address = "San Francisco, California, USA",
month = "9-11 " # jul,
email = "fanzhun@egr.msu.edu, hujianju@egr.msu.edu",
keywords = "genetic algorithms, genetic programming, STGP, bond
graphs, evolutionary synthesis",
URL = "http://www.egr.msu.edu/~ksseo/publication.htm",
URL = "http://citeseer.ist.psu.edu/448346.html",
abstract = "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.",
notes = "GECCO-2001LB, lilgp",
}
@InProceedings{fan:2002:gecco,
author = "Zhun Fan and Kisung Seo and Ronald C. Rosenberg and
Jianjun Hu and Erik D. Goodman",
title = "Exploring Multiple Design Topologies Using Genetic
Programming And Bond Graphs",
booktitle = "GECCO 2002: Proceedings of the Genetic and
Evolutionary Computation Conference",
editor = "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",
year = "2002",
pages = "1073--1080",
address = "New York",
publisher_address = "San Francisco, CA 94104, USA",
month = "9-13 " # jul,
publisher = "Morgan Kaufmann Publishers",
keywords = "genetic algorithms, genetic programming, real world
applications, bond graphs, design automation,
mechatronic system, topology",
ISBN = "1-55860-878-8",
URL = "http://garage.cse.msu.edu/papers/GARAGe02-07-03.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/RWA217.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-20.pdf",
abstract = "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.",
notes = "GECCO-2002. A joint meeting of the eleventh
International Conference on Genetic Algorithms
(ICGA-2002) and the seventh Annual Genetic Programming
Conference (GP-2002)",
}
@InProceedings{ZhunFan:2003:AAAI,
author = "Zhun Fan and Kisung Seo and Ronald C. Rosenberg and
Jianjun Hu and Erik D. Goodman",
title = "Computational Synthesis of Multi-Domain Systems",
booktitle = "Proceedings of the 2003 AAAI Spring Symposium -
Computational Synthesis: From Basic Building Blocks to
High Level Functionality",
year = "2003",
pages = "59--66",
address = "Stanford, California",
month = mar,
organisation = "AAAI",
email = "hujianju@msu.edu, goodman@egr.msu.edu",
keywords = "genetic algorithms, genetic programming, bond graphs,
evolutionary synthesis",
URL = "http://garage.cse.msu.edu/papers/GARAGe03-03-02.pdf",
URL = "http://www.egr.msu.edu/~ksseo/publication.htm",
abstract = "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.",
}
@InProceedings{fan:2003:gecco,
author = "Zhun Fan and Kisung Seo and Jianjun Hu and Ronald C.
Rosenberg and Erik D. Goodman",
title = "System-Level Synthesis of {MEMS} via Genetic
Programming and Bond Graphs",
booktitle = "Genetic and Evolutionary Computation -- GECCO-2003",
editor = "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",
year = "2003",
pages = "2058--2071",
address = "Chicago",
publisher_address = "Berlin",
month = "12-16 " # jul,
volume = "2724",
series = "LNCS",
ISBN = "3-540-40603-4",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, Real World
Applications",
abstract = "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.",
notes = "GECCO-2003. A joint meeting of the twelfth
International Conference on Genetic Algorithms
(ICGA-2003) and the eighth Annual Genetic Programming
Conference (GP-2003)",
}
@InProceedings{fan:2004:hesom,
title = "Hierarchical Evolutionary Synthesis of {MEMS}",
author = "Zhun Fan and Erik Goodman and Jiachuan Wang and Ronald
Rosenberg and Kisung Seo and Jianjun Hu",
pages = "2320--2327",
booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary
Computation",
year = "2004",
publisher = "IEEE Press",
month = "20-23 " # jun,
address = "Portland, Oregon",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming, Evolutionary
design \& evolvable hardware, Real-world applications",
abstract = "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.",
notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and
the IEE.",
}
@InCollection{Fan:2004:EMTP,
author = "Zhun Fan and Jiachuan Wang and Kisung Seo and Jianjun
Hu and Ronald Rosenberg and Janis Terpenny and Erik
Goodman",
title = "Automating the Hierarchical Synthesis of {MEMS} Using
Evolutionary Approaches",
year = "2004",
booktitle = "Evolvable Machines: Theory \& Practice",
pages = "129--149",
volume = "161",
series = "Studies in Fuzziness and Soft Computing",
chapter = "6",
editor = "Nadia Nedjah and Luiza {de Macedo Mourelle}",
publisher = "Springer",
address = "Berlin",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-22905-1",
URL = "http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980449-0,00.html",
notes = "Springer says published in 2005 but available Nov
2004",
}
@PhdThesis{ZhunFan:thesis,
author = "Zhun Fan",
title = "Design Automation of Mechatronic Systems",
school = "Electrical and Computer Engineering, Michigan State
University",
year = "2004",
address = "USA",
keywords = "genetic algorithms, genetic programming",
URL = "https://www.msu.edu/~fanzhun/Zhun%27s%20Dissertation%20Research.htm",
abstract = "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",
}
@Article{Fan2008579,
author = "Zhun Fan and Jiachuan Wang and Sofiane Achiche and
Erik Goodman and Ronald Rosenberg",
title = "Structured synthesis of {MEMS} using evolutionary
approaches",
journal = "Applied Soft Computing",
volume = "8",
number = "1",
pages = "579--589",
year = "2008",
ISSN = "1568-4946",
doi = "doi:10.1016/j.asoc.2007.04.001",
URL = "http://www.sciencedirect.com/science/article/B6W86-4NWCGRR-6/2/6d147c9eb8cc9af8eec68e592dfd22f",
keywords = "genetic algorithms, genetic programming, MEMS
synthesis, Genetic programming, Bond graphs, Genetic
algorithm",
abstract = "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.",
}
@Book{Mechatronic_Design_Automation_Emerging_Research_and_Recent_Advances,
author = "Zhun Fan",
title = "Mechatronic Design Automation: Emerging Research and
Recent Advances",
publisher = "Nova publishers",
year = "2010",
month = apr,
keywords = "genetic algorithms, genetic programming, bond graph",
isbn13 = "978-1616689568",
URL = "https://www.novapublishers.com/catalog/product_info.php?products_id=27671",
URL = "https://www.novapublishers.com/catalog/downloadOA.php?order=1&access=true",
URL = "http://www.amazon.com/Mechatronic-Design-Automation-Engineering-Applications/dp/1616689560",
abstract = "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.",
notes = "Technical University of Denmark, Denmark",
size = "161 pages",
}
@InProceedings{conf/isica/FangL10,
title = "A Review of Tournament Selection in Genetic
Programming",
author = "Yongsheng Fang and Jun Li",
booktitle = "ISICA 2010",
year = "2010",
editor = "Zhihua Cai and Chengyu Hu and Zhuo Kang and Yong Liu",
volume = "6382",
series = "Lecture Notes in Computer Science",
pages = "181--192",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-16492-7",
doi = "doi:10.1007/978-3-642-16493-4_19",
size = "12 pages",
bibdate = "2010-10-19",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/isica/isica2010.html#FangL10",
abstract = "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.",
affiliation = "Department of Finance, Anhui Polytechnic University,
Wuhu City, Anhui P.R. China",
}
@InProceedings{Fanizzi:2007:MCD,
author = "Nicola Fanizzi and Claudia d'Amato and Floriana
Esposito",
title = "Clustering Individuals in Ontologies: a Distance-based
Evolutionary Approach",
booktitle = "Proceedings of the third ECML/PKDD international
workshop on Mining Complex Data",
year = "2007",
editor = "Zbigniew W. Ras and Djamel Zighed and Shusaku
Tsumoto",
pages = "197--208",
address = "Warsaw",
month = "17 and 21 " # sep,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.ecmlpkdd2007.org/CD/workshops/MCDM/18_Fanizzi/mcdws2007-final.pdf",
size = "12 pages",
abstract = "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.",
notes = "LACAM Dipartimento di Informatica, Universit`a degli
Studi di Bari Campus Universitario, Via Orabona 4 70125
Bari, Italy",
}
@Article{Fanizzi:2009:IS,
author = "Nicola Fanizzi and Claudia d'Amato and Floriana
Esposito",
title = "Metric-based stochastic conceptual clustering for
ontologies",
journal = "Information Systems",
year = "2009",
volume = "34",
pages = "792--806",
number = "8",
note = "Sixteenth ACM Conference on Information Knowledge and
Management (CIKM 2007)",
keywords = "genetic algorithms, genetic programming, Conceptual
clustering",
doi = "doi:10.1016/j.is.2009.03.008",
ISSN = "0306-4379",
URL = "http://www.sciencedirect.com/science/article/B6V0G-4W3HXC0-1/2/95a1535c9097d816c4ec5ad804772c4b",
abstract = "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.",
}
@Article{Faraoun:2006:IJCIA,
author = "K. M. Faraoun and A. Boukelif",
title = "Genetic Programming Approach for Multi-Category
Pattern Classification Applied to Network Intrusions
Detection",
journal = "International Journal of Computational Intelligence
and Applications (IJCIA)",
year = "2006",
volume = "6",
number = "1",
pages = "77--100",
month = mar,
keywords = "genetic algorithms, genetic programming",
ISSN = "1469-0268",
URL = "http://direct.bl.uk/bld/PlaceOrder.do?UIN=193825360&ETOC=RN&from=searchengine",
URL = "http://www.worldscinet.com/cgi-bin/jform.cgi?/ijcia/mkt/free/S1469026806001812.html",
}
@InProceedings{Farinaccio:2010:gecco,
author = "Antonella Farinaccio and Leonardo Vanneschi and Mario
Giacobini and Giancarlo Mauri and Paolo Provero",
title = "On the use of genetic programming for the prediction
of survival in cancer",
booktitle = "GECCO '10: Proceedings of the 12th annual conference
on Genetic and evolutionary computation",
year = "2010",
editor = "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",
isbn13 = "978-1-4503-0072-8",
pages = "163--170",
keywords = "genetic algorithms, genetic programming,
Bioinformatics, computational, systems and synthetic
biology, SVM, ANN, MLP, voted percenptron, RBF",
month = "7-11 " # jul,
organisation = "SIGEVO",
address = "Portland, Oregon, USA",
doi = "doi:10.1145/1830483.1830514",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "NKI 70-gene breast cancer. p168 Implicit feature
selection. AF257175, NM_001809.
Also known as \cite{1830514} GECCO-2010 A joint meeting
of the nineteenth international conference on genetic
algorithms (ICGA-2010) and the fifteenth annual genetic
programming conference (GP-2010)",
}
@TechReport{farringdon:1996:in05,
author = "J Farringdon",
title = "Random Effects in Genetic Algorithms and Programming
(\& Other Genetic Algorithm Issues)",
institution = "University College London",
year = "1996",
type = "Internal Note",
number = "IN/96/05",
address = "Computer Science, Gower Street, London WC1E 6BT, UK",
month = jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/staff/j.farringdon/GP/in-1996-05.html",
abstract = "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.",
}
@Misc{farrow:1958:Leo,
author = "Steve Farrow",
title = "{GP} in 1958!",
howpublished = "Peter Bentely, GP mailing list, EC-digest",
year = "2004",
month = "8 " # mar,
keywords = "genetic algorithms, genetic programming",
URL = "http://groups.yahoo.com/group/genetic_programming/message/2492",
size = "1 page",
abstract = "First four members of a series are a, b, c, d. What is
the fifth?",
notes = "LEO II/4",
}
@InProceedings{Farry:LPSC98,
author = "K. A. Farry and J. S. Graham and F. Vilas and K. S.
Jarvis",
title = "Automating Asteroid Surface Composition Identification
from Reflectance Spectra",
booktitle = "The 29th Lunar and Planetary Science Conference",
year = "1998",
pages = "1661",
address = "Houston, Texas, USA",
month = "16-20 " # mar,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.lpi.usra.edu/meetings/LPSC98/pdf/1661.pdf",
size = "2 pages",
abstract = "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.",
notes = "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 \cite{1997DPS....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",
}
@Article{Fasli2011,
author = "Maria Fasli and Yevgeniya Kovalchuk",
title = "Learning approaches for developing successful seller
strategies in dynamic supply chain management",
journal = "Information Sciences",
note = "In Press, Corrected Proof",
year = "2011",
ISSN = "0020-0255",
doi = "doi:10.1016/j.ins.2011.04.014",
URL = "http://www.sciencedirect.com/science/article/B6V0C-52M4V3W-4/2/e88e5f17659c1d3f021a4e6052e7b965",
abstract = "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.",
}
@InProceedings{Fast:2010:GECCO,
author = "Ethan Fast and Claire {Le Goues} and Stephanie Forrest
and Westley Weimer",
title = "Designing better fitness functions for automated
program repair",
year = "2010",
booktitle = "GECCO '10: Proceedings of the 12th annual conference
on Genetic and evolutionary computation",
editor = "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",
isbn13 = "978-1-4503-0072-8",
pages = "965--972",
month = "7-11 " # jul,
organisation = "SIGEVO",
address = "Portland, Oregon, USA",
publisher = "ACM",
publisher_address = "New York, NY, USA",
keywords = "genetic algorithms, genetic programming, SBSE,
Software repair, software engineering",
URL = "http://www.cs.virginia.edu/~weimer/p/weimer-gecco2010-preprint.pdf",
doi = "doi:10.1145/1830483.1830654",
size = "8 pages",
abstract = "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.",
notes = "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 \cite{1830654} GECCO-2010 A joint meeting
of the nineteenth international conference on genetic
algorithms (ICGA-2010) and the fifteenth annual genetic
programming conference (GP-2010)",
}
@InProceedings{Fatima:2010:cec,
author = "Shaheen Fatima and Mohamed Bader-El-Den",
title = "Co-evolutionary hyper-heuristic method for auction
based scheduling",
booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-6910-9",
abstract = "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.",
doi = "doi:10.1109/CEC.2010.5586319",
notes = "WCCI 2010. Also known as \cite{5586319}",
}
@InProceedings{Fatima:2011:GECCO,
author = "Shaheen Fatima and Ahmed Kattan",
title = "Evolving optimal agendas for package deal
negotiation",
booktitle = "GECCO '11: Proceedings of the 13th annual conference
on Genetic and evolutionary computation",
year = "2011",
editor = "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",
isbn13 = "978-1-4503-0557-0",
pages = "505--512",
keywords = "genetic algorithms, genetic programming, Evolutionary
combinatorial optimization and metaheuristics",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001576.2001646",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{2001646} GECCO-2011 A joint
meeting of the twentieth international conference on
genetic algorithms (ICGA-2011) and the sixteenth annual
genetic programming conference (GP-2011)",
}
@InProceedings{fazenda:evoapps12,
author = "Pedro Fazenda and James McDermott and Una-May
O'Reilly",
title = "A Library to Run Evolutionary Algorithms in the Cloud
using {MapReduce}",
booktitle = "Applications of Evolutionary Computing,
EvoApplications2012: {EvoCOMNET}, {EvoCOMPLEX},
{EvoFIN}, {EvoGAMES}, {EvoHOT}, {EvoIASP}, {EvoNUM},
{EvoPAR}, {EvoRISK}, {EvoSTIM}, {EvoSTOC}",
year = "2012",
month = "11-13 " # apr,
editor = "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",
series = "LNCS",
volume = "7248",
publisher = "Springer Verlag",
address = "Malaga, Spain",
pages = "416--425",
organisation = "EvoStar",
keywords = "genetic algorithms, genetic programming, MapReduce,
Hadoop, EC, Amazon EC2, FlexEA",
isbn13 = "978-3-642-29177-7",
doi = "doi:10.1007/978-3-642-29178-4_42",
size = "10 pages",
abstract = "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.",
notes = "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 \cite{DiChio:2012:EvoApps} EvoApplications2012
held in conjunction with EuroGP2012, EvoCOP2012,
EvoBio'2012 and EvoMusArt2012",
}
@InProceedings{federman:1998:clps,
author = "Francine Federman and Susan Fife Dorchak",
title = "A Study of Classifier Length and Population Size",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "629--634",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, classifiers",
ISBN = "1-55860-548-7",
notes = "GP-98",
}
@InProceedings{federman:1999:RMLCSUBC,
author = "Francine Federman and Gayle Sparkman and Stephanie
Watt",
title = "Representation of Music in a Learning Classifier
System Utilizing Bach Chorales",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "785",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems, poster
papers",
ISBN = "1-55860-611-4",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InCollection{fehr:1994:semo,
author = "Garry Fehr",
title = "Spontaneous Emergence of Multicellular Organisms From
Unicellular Ancestors",
booktitle = "Artificial Life at Stanford 1994",
year = "1994",
editor = "John R. Koza",
pages = "28--34",
address = "Stanford, California, 94305-3079 USA",
month = jun,
organisation = "Stanford University",
publisher = "Stanford Bookstore",
keywords = "genetic algorithms",
ISBN = "0-18-182105-2",
notes = "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",
}
@TechReport{Feiler:book,
author = "Peter Feiler and Richard P. Gabriel and John
Goodenough and Rick Linger and Tom Longstaff and Rick
Kazman and Mark Klein and Linda Northrop and Douglas
Schmidt and Kevin Sullivan and Kurt Wallnau",
title = "Ultra-Large-Scale Systems -- The Software Challenge of
the Future",
institution = "software engineering institute, Carnegie Mellon
University",
year = "2006",
address = "Pittsburgh, PA 15213-3890, USA",
month = jun,
keywords = "genetic algorithms, genetic programming",
URL = "www.sei.cmu.edu/library/assets/ULS_Book20062.pdf",
ISBN = "0-9786956-0-7",
abstract = "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.",
notes = "GP described as digital evolution. USA Federal
Government Contract Number FA8721-05-C-0003.",
size = "150 pages",
}
@Article{feldkamp:2003:GPEM,
author = "Udo Feldkamp and Hilmar Rauhe and Wolfgang Banzhaf",
title = "Software Tools for {DNA} Sequence Design",
journal = "Genetic Programming and Evolvable Machines",
year = "2003",
volume = "4",
number = "2",
pages = "153--171",
month = jun,
keywords = "DNA computing, DNA nanotechnology, molecular
self-assembly, sequence design, specific
hybridization",
ISSN = "1389-2576",
URL = "http://www.cs.mun.ca/~banzhaf/papers/softwaretools.pdf",
doi = "doi:10.1023/A:1023985029398",
abstract = "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.",
notes = "Special Issue on Biomolecular Machines and Artificial
Evolution Article ID: 5122743",
}
@TechReport{feldt:1998:eGPmsv,
author = "Robert Feldt",
title = "An experiment on using genetic programming to develop
multiple diverse software variants",
institution = "Department of Computer Engineering, Chalmers
University of Technology",
year = "1998",
type = "Technical Report",
number = "98-13",
address = "Gothenburg, Sweden",
month = sep,
keywords = "genetic algorithms, genetic programming",
notes = "Included also in \cite{feldt:1998:midthesis}",
size = "pages",
}
@TechReport{feldt:1998:scdGPsft,
author = "Robert Feldt",
title = "A survey of the concept of diversity in genetic
programming and software fault tolerance",
institution = "Department of Computer Engineering, Chalmers
University of Technology",
year = "1998",
type = "Technical Report",
number = "98-15",
address = "Gothenburg, Sweden",
month = oct,
keywords = "genetic algorithms, genetic programming",
notes = "Included also in \cite{feldt:1998:midthesis}",
size = "pages",
}
@InProceedings{feldt:1998:gmdsvGP,
author = "Robert Feldt",
title = "Generating Multiple Diverse Software Versions with
Genetic Programming",
booktitle = "Proceedings of the 24th EUROMICRO Conference, Workshop
on Dependable Computing Systems",
year = "1998",
pages = "387--396",
address = "Vaesteraas, Sweden",
month = "25-27th " # aug,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.amp.york.ac.uk/external/sweden/sweden.htm",
doi = "doi:10.1109/EURMIC.1998.711831",
abstract = "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.",
notes = "described in \cite{feldt:1998:midthesis}",
}
@Article{feldt:1998:gdsvGPes,
author = "Robert Feldt",
title = "Generating Diverse Software Versions with Genetic
Programming: an Experimental Study",
journal = "IEE Proceedings - Software Engineering",
year = "1998",
volume = "145",
number = "6",
pages = "228--236",
month = dec,
note = "Special issue on Dependable Computing Systems",
keywords = "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",
ISSN = "1462-5970",
URL = "http://www.iee.org.uk/publish/journals/profjrnl/cntnsen.html#SENDecember1998",
abstract = "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",
notes = "See Workshop: Managing and Optimising Multiplicity
Computing, 22-23 March 2012
http://crest.cs.ucl.ac.uk/cow/18/
described in \cite{feldt:1998:midthesis}. Also known as
\cite{765682} CODEN: IPSEFU INSPEC Accession
Number:6150266",
}
@TechReport{feldt:1998:midthesis,
author = "Robert Feldt",
title = "Using Genetic Programming to Systematically Force
Software Diversity",
institution = "Department of Computer Engineering, Chalmers
University of Technology",
year = "1998",
type = "Technical Report",
number = "296L",
address = "Goteborg, Sweden",
month = nov,
keywords = "genetic algorithms, genetic programming",
ISBN = "91-7197-740-6",
notes = "licentiate of Engineering thesis",
size = "133 pages",
}
@InProceedings{feldt:1999:GPxtxsdp,
author = "Robert Feldt",
title = "Genetic Programming as an Explorative Tool in Early
Software Development Phases",
booktitle = "Proceedings of the 1st International Workshop on Soft
Computing Applied to Software Engineering",
year = "1999",
editor = "Conor Ryan and Jim Buckley",
pages = "11--20",
address = "University of Limerick, Ireland",
month = "12-14 " # apr,
organisation = "SCARE",
publisher = "Limerick University Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-874653-52-6",
URL = "http://drfeldt.googlepages.com/feldt_1999_gp_as_explorative_tool.pdf",
size = "10 pages",
abstract = "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.",
notes = "http://scare.csis.ul.ie/scase99/ SCASE'99 USAF
aircraft arresting system (landing on carriers) used as
example. Java GPsys.",
}
@InProceedings{feldt:2000:feeeGP,
author = "Robert Feldt and Peter Nordin",
title = "Using Factorial Experiments to Evaluate the Effect of
Genetic Programming Parameters",
booktitle = "Genetic Programming, Proceedings of EuroGP'2000",
year = "2000",
editor = "Riccardo Poli and Wolfgang Banzhaf and William B.
Langdon and Julian F. Miller and Peter Nordin and
Terence C. Fogarty",
volume = "1802",
series = "LNCS",
pages = "271--282",
address = "Edinburgh",
publisher_address = "Berlin",
month = "15-16 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-67339-3",
URL = "http://citeseer.ist.psu.edu/325152.html",
URL = "http://drfeldt.googlepages.com/feldt_2000_factorial_exp_gp_params.pdf",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=271",
abstract = "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.",
notes = "EuroGP'2000, part of \cite{poli:2000:GP}",
}
@InProceedings{feldt:2000:gp-beagle,
author = "Robert Feldt and Michael O'Neill and Conor Ryan and
Peter Nordin and William B. Langdon",
title = "{GP-Beagle:} {A} Benchmarking Problem Repository for
the Genetic Programming Community",
pages = "90--97",
booktitle = "Late Breaking Papers at the 2000 Genetic and
Evolutionary Computation Conference",
year = "2000",
editor = "Darrell Whitley",
address = "Las Vegas, Nevada, USA",
month = "8 " # jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/feldt_et_al_gecco2000lb_gpbeagle.pdf",
URL = "http://drfeldt.googlepages.com/20_paper7_gpbeagle.ps",
URL = "http://citeseer.ist.psu.edu/302050.html",
size = "8 pages",
abstract = "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.",
notes = "Part of \cite{whitley:2000:GECCOlb}",
}
@TechReport{Feldt:2002:tr,
author = "Robert Feldt",
title = "An Interactive Software Development Workbench based on
Biomimetic Algorithms",
institution = "Department of Computer Engineering, Chalmers
University of Technology",
year = "2002",
address = "Gothenburg, SWEDEN",
month = nov,
keywords = "genetic algorithms, genetic programming, simulated
annealing, multi-agent, SBSE, Ruby",
URL = "http://drfeldt.googlepages.com/feldt_2002_wise_tech_report.pdf",
abstract = "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.",
size = "42 pages",
}
@PhdThesis{Feldt:thesis,
author = "Robert Feldt",
title = "Biomimetic Software Engineering Techniques for
Dependability",
school = "Department of Computer Engineering, Chalmers
University of Technology",
year = "2002",
address = "Gothenburg, Sweden",
month = dec,
keywords = "genetic algorithms, genetic programming, SBSE",
}
@Article{Felton:2000:MDD,
author = "Michael J. Felton",
title = "Survival of the Fittest in Drug Design",
journal = "Modern Drug Discovery",
year = "2000",
volume = "3",
number = "9",
pages = "49--50",
month = nov # "/" # dec,
publisher = "American Chemical Society",
keywords = "genetic algorithms, genetic programming",
ISSN = "1532-4486",
URL = "http://pubs.acs.org/subscribe/journals/mdd/v03/i09/html/felton.html",
size = "2 pages",
notes = "magazine",
}
@Article{Feng:2006:IJRMMS,
author = "Xia-Ting Feng and Bing-Rui Chen and Chengxiang Yang
and Hui Zhou and Xiuli Ding",
title = "Identification of visco-elastic models for rocks using
genetic programming coupled with the modified particle
swarm optimization algorithm",
journal = "International Journal of Rock Mechanics and Mining
Sciences",
year = "2006",
volume = "43",
number = "5",
pages = "789--801",
month = jul,
keywords = "genetic algorithms, genetic programming, Visco-elastic
models, Rock, Evolutionary algorithm, Particle swarm
optimisation",
doi = "doi:10.1016/j.ijrmms.2005.12.010",
abstract = "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.",
notes = "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",
}
@InProceedings{Feng:2010:IEEC,
author = "Yanghe Feng and Chaofan Dai and Jianmai Shi and Liang
Mu",
title = "An Automatic Model Selection Algorithm Based Genetic
Programming",
booktitle = "2nd International Symposium on Information Engineering
and Electronic Commerce (IEEC 2010)",
year = "2010",
month = jul,
abstract = "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.",
keywords = "genetic algorithms, genetic programming, automatic
model selection algorithm, genetic operations,
metamodels, model-aided decision usability,
metacomputing",
doi = "doi:10.1109/IEEC.2010.5533243",
notes = "Also known as \cite{5533243}",
}
@InProceedings{Ferariu:2009:ICANNGA,
author = "Lavinia Ferariu and Alina Patelli",
title = "Multiobjective Genetic Programming for Nonlinear
System Identification",
year = "2009",
booktitle = "9th International Conference on Adaptive and Natural
Computing Algorithms, ICANNGA 2009",
editor = "Mikko Kolehmainen and Pekka Toivanen and Bartlomiej
Beliczynski",
series = "Lecture Notes in Computer Science",
volume = "5495",
pages = "233--242",
address = "Kuopio, Finland",
month = "23-25 " # apr,
publisher = "Springer",
note = "Revised selected papers",
keywords = "genetic algorithms, genetic programming,
multiobjective optimisation, nonlinear system
identification",
isbn13 = "978-3-642-04920-0",
doi = "doi:10.1007/978-3-642-04921-7_24",
abstract = "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.",
notes = "ICANNGA 2009",
}
@InProceedings{Ferariu:2009:SACI,
author = "L. Ferariu and A. Patelli",
title = "Migration-based multiobjective genetic programming for
nonlinear system identification",
booktitle = "5th International Symposium on Applied Computational
Intelligence and Informatics, SACI '09",
year = "2009",
month = may,
pages = "475--480",
keywords = "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)",
doi = "doi:10.1109/SACI.2009.5136295",
abstract = "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.",
notes = "Also known as \cite{5136295}",
}
@InProceedings{Ferariu:2010:ICCC-CONTI,
author = "L. Ferariu and B. Burlacu",
title = "Graph genetic programming for hybrid neural networks
design",
booktitle = "International Joint Conference on Computational
Cybernetics and Technical Informatics (ICCC-CONTI)",
year = "2010",
month = may,
pages = "547--552",
abstract = "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.",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1109/ICCCYB.2010.5491213",
notes = "Also known as \cite{5491213}",
}
@InProceedings{fernandes:1999:EAST,
author = "Carlos Fernandes and Joao Paulo Caldeira and Fernando
Melicio and Agostinho Rosa",
title = "Evolutionary Algorithm for School Timetabling",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1777",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "real world applications, poster papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-743.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-743.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{conf/eusflat/FernandezBJH09,
title = "Genetic Cooperative-Competitive Fuzzy Rule Based
Learning Method using Genetic Programming for Highly
Imbalanced Data-Sets",
author = "Alberto Fernandez and Francisco Jose Berlanga and
Maria Jose {del Jesus} and Francisco Herrera",
booktitle = "Proceedings of the Joint 2009 International Fuzzy
Systems Association World Congress and 2009 European
Society of Fuzzy Logic and Technology Conference",
year = "2009",
editor = "Jo{\~a}o Paulo Carvalho and Didier Dubois and Uzay
Kaymak and Jo{\~a}o Miguel da Costa Sousa",
pages = "42--47",
address = "Lisbon, Portugal",
month = jul # " 20-24",
keywords = "genetic algorithms, genetic programming, Fuzzy
Rule-Based Classification Systems, Genetic Fuzzy
Systems, imbalanced Data-Sets, Interpretability",
isbn13 = "978-989-95079-6-8",
URL = "http://www.eusflat.org/publications/proceedings/IFSA-EUSFLAT_2009/pdf/tema_0042.pdf",
bibdate = "2009-12-16",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/eusflat/eusflat2009.html#FernandezBJH09",
abstract = "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.",
notes = "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",
}
@InProceedings{FSTG99,
author = "F. Fernandez and J. M. Sanchez and M. Tomassini and J.
A. Gomez",
title = "A Parallel Genetic Programming Tool based on {PVM}",
booktitle = "Recent Advances in Parallel Virtual Machine and
Message Passing Interface, Proceedings of the 6th
European PVM/MPI Users' Group Meeting",
series = "Lecture Notes in Computer Science",
editor = "J. Dongarra and E. Luque and T. Margalef",
volume = "1697",
pages = "241--248",
publisher = "Springer-Verlag",
ISBN = "3-540-66549-8",
year = "1999",
month = sep,
address = "Barcelona, Spain",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{FTVB00,
author = "F. Fernandez and M. Tomassini and L. Vanneschi and L.
Bucher",
title = "A Distributed Computing Environment for Genetic
Programming using {MPI}",
editor = "J. J. Dongarra and Peter Kacsuk and Norbert
Podhorszki",
booktitle = "Recent advances in parallel virtual machine and
message passing interface: 7th European {PVM\slash MPI}
Users' Group Meeting",
volume = "1908",
publisher = "Springer-Verlag",
address = "Balatonfured, Hungary",
pages = "322--329",
year = "2000",
ISBN = "3-540-41010-4 (softcover)",
ISSN = "0302-9743",
bibdate = "Mon Oct 16 18:31:56 MDT 2000",
series = "Lecture Notes in Computer Science",
month = "10-13 " # sep,
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{fernandez:1999:SAEP,
author = "Francisco Fernandez and Marco Tomassini and J. M.
Sanchez",
title = "Solving the Ant and the Even Parity-5 problems by
means of parallel genetic programming",
booktitle = "Late Breaking Papers at the 1999 Genetic and
Evolutionary Computation Conference",
year = "1999",
editor = "Scott Brave and Annie S. Wu",
pages = "88--92",
address = "Orlando, Florida, USA",
month = "13 " # jul,
keywords = "genetic algorithms, genetic programming",
notes = "GECCO-99LB",
}
@InProceedings{fernandez:2000:esmpGP,
author = "F. Fernandez and M. Tomassini and W. F. {Punch III}
and J. M. Sanchez",
title = "Experimental Study of Multipopulation Parallel Genetic
Programming",
booktitle = "Genetic Programming, Proceedings of EuroGP'2000",
year = "2000",
editor = "Riccardo Poli and Wolfgang Banzhaf and William B.
Langdon and Julian F. Miller and Peter Nordin and
Terence C. Fogarty",
volume = "1802",
series = "LNCS",
pages = "283--293",
address = "Edinburgh",
publisher_address = "Berlin",
month = "15-16 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-67339-3",
URL = "http://garage.cse.msu.edu/papers/GARAGe00-03-01.pdf",
URL = "http://citeseer.ist.psu.edu/445504.html",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=283",
doi = "doi:10.1007/b75085",
size = "11 pages",
abstract = "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.",
notes = "EuroGP'2000, part of \cite{poli:2000:GP}",
}
@InProceedings{vega:2000:mgpabd,
author = "F. {Fernandez de Vega} and Laura M. Roa and Marco
Tomassini and J. M. Sanchez",
title = "Multipopulation Genetic Programing Applied to Burn
Diagnosing",
booktitle = "Proceedings of the 2000 Congress on Evolutionary
Computation CEC00",
year = "2000",
pages = "1292--1296",
address = "La Jolla Marriott Hotel La Jolla, California, USA",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "6-9 " # jul,
organisation = "IEEE Neural Network Council (NNC), Evolutionary
Programming Society (EPS), Institution of Electrical
Engineers (IEE)",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, novel
applications i",
ISBN = "0-7803-6375-2",
notes = "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",
}
@InProceedings{fernandez:2000:GA,
author = "Francisco Fernandez and Marco Tomassini",
title = "Genetic programming and reconfigurable hardware: {A}
proposal for solving the problem of placement and
routing",
booktitle = "Graduate Student Workshop",
year = "2000",
editor = "Conor Ryan and Una-May O'Reilly and William B.
Langdon",
pages = "265--268",
address = "Las Vegas, Nevada, USA",
month = "8 " # jul,
keywords = "genetic algorithms, genetic programming",
notes = "GECCO-2000WKS Part of \cite{wu:2000:GECCOWKS}",
}
@InProceedings{Fernandez:2000:GECCO,
author = "Francisco Fernandez and Marco Tomassini and William
Punch and J. M. Sanchez",
title = "Experimental Study of Isolated Multipopulation Genetic
Programming",
pages = "536",
year = "2000",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2000)",
editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz
and Lee Spector and Ian Parmee and Hans-Georg Beyer",
address = "Las Vegas, Nevada, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "10-12 " # jul,
keywords = "genetic algorithms, genetic programming, Poster",
ISBN = "1-55860-708-0",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/GP159.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/GP159.ps",
notes = "A joint meeting of the ninth International Conference
on Genetic Algorithms (ICGA-2000) and the fifth Annual
Genetic Programming Conference (GP-2000) Part of
\cite{whitley:2000:GECCO}",
}
@InProceedings{fernandez:2000:esimgp,
author = "F. Fernandez and M. Tomassini and J. M. Sanchez",
title = "Experimental Study of Isolated Multipopulation Genetic
Programming",
booktitle = "Proceedings of the 26th Annual Conference of the IEEE
Industrial Electronics Society",
volume = "1697",
pages = "2672--2677",
publisher = "IEEE Press",
ISBN = "0-7803-6456-2",
year = "2000",
month = oct,
address = "Nagoya, Japan",
keywords = "genetic algorithms, genetic programming",
URL = "http://fp.ieeexplore.ieee.org/iel5/7662/20956/00972420.pdf?isNumber=20956&prod=CNF&arnumber=00972420",
abstract = "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.",
}
@InProceedings{fernandez:2001:EuroGP,
author = "Francisco Fernandez and Marco Tomassini and Leonardo
Vanneschi",
title = "Studying the Influence of Communication Topology and
Migration on Distributed Genetic Programming",
booktitle = "Genetic Programming, Proceedings of EuroGP'2001",
year = "2001",
editor = "Julian F. Miller and Marco Tomassini and Pier Luca
Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
William B. Langdon",
volume = "2038",
series = "LNCS",
pages = "51--63",
address = "Lake Como, Italy",
publisher_address = "Berlin",
month = "18-20 " # apr,
organisation = "EvoNET",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, Distributed
Genetic Programming, Parallelism, Multipopulation
structures, Parallel evolutionary algorithms",
ISBN = "3-540-41899-7",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=51",
size = "13 pages",
abstract = "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.",
notes = "EuroGP'2001, part of \cite{miller:2001:gp}",
}
@InProceedings{fernandez:2001:soprvpmrp,
author = "F. Fernandez and M. Tomassini",
title = "Studying the Optimal Parameter Range of Values in
{PADGP} by Means of Real-life Problems",
booktitle = "Proceedings of the 2001 Congress on Evolutionary
Computation CEC2001",
year = "2001",
pages = "436--441",
address = "COEX, World Trade Center, 159 Samseong-dong,
Gangnam-gu, Seoul, Korea",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "27-30 " # may,
organisation = "IEEE Neural Network Council (NNC), Evolutionary
Programming Society (EPS), Institution of Electrical
Engineers (IEE)",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, Parallel
Genetic Programming, FPGA",
ISBN = "0-7803-6658-1",
notes = "CEC-2001 - A joint meeting of the IEEE, Evolutionary
Programming Society, Galesia, and the IEE.
IEEE Catalog Number = 01TH8546C,
Library of Congress Number =",
}
@InProceedings{fernandez:2001:gecco,
title = "A new methodology for the Placement and Routing
problem based on {PADGP}",
author = "F. Fernandez and J. M. Sanchez and M. Tomassini",
pages = "175",
year = "2001",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2001)",
editor = "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",
address = "San Francisco, California, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "7-11 " # jul,
keywords = "genetic algorithms, genetic programming: Poster,
Parallel Evolutionary Algorithms, Evolvable Hardware",
ISBN = "1-55860-774-9",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d02.pdf",
notes = "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 \cite{spector:2001:GECCO}",
}
@InProceedings{fernandez:2001:,
author = "F. Fernandez and J. M. Sanchez and M. Tomassini",
title = "Placing and Routing Circuits on {FPGA}s by Means of
Parallel and Distributed Genetic Programming",
booktitle = "Evolvable Systems: From Biology to Hardware,
Proceedings of the 4th International Conference, ICES
2001",
series = "Lecture Notes in Computer Science",
editor = "Yong Liu and Kiyoshi Tanaka and Masaya Iwata and
Tetsuya Higuchi and Moritoshi Yasunaga",
volume = "2210",
pages = "204--214",
publisher = "Springer-Verlag",
ISBN = "3-540-42671-X",
ISSN = "0302-9743",
year = "2001",
month = "3-5 Octpber",
address = "Tokyo, Japan",
keywords = "genetic algorithms, genetic programming, evolvable
hardware, PADGP",
ISBN = "3-540-42671-X",
ISSN = "0302-9743",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2210&spage=204",
size = "12 pages",
abstract = "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.",
notes = "island-based FPGAs eg Xilinx. Digital circuits.
Connecting circuits given by syntax of evolved GP
tree.",
}
@InProceedings{fernandez:2002:EuroGP,
title = "Comparing Synchronous and Asynchronous Parallel and
Distributed {GP} Models",
author = "Francisco Fernandez and G. Galeano and J. A. Gomez",
editor = "James A. Foster and Evelyne Lutton and Julian Miller
and Conor Ryan and Andrea G. B. Tettamanzi",
booktitle = "Genetic Programming, Proceedings of the 5th European
Conference, EuroGP 2002",
volume = "2278",
series = "LNCS",
pages = "326--335",
publisher = "Springer-Verlag",
address = "Kinsale, Ireland",
publisher_address = "Berlin",
month = "3-5 " # apr,
year = "2002",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-43378-3",
URL = "http://link.springer-ny.com/link/service/series/0558/bibs/2278/22780326.htm",
URL = "http://link.springer-ny.com/link/service/series/0558/papers/2278/22780326.pdf",
abstract = "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.",
notes = "EuroGP'2002, part of lutton:2002:GP.
Santa Fe Ant, even-5-parity. padgp",
}
@Article{Fernandez:2003:GPEM,
author = "Francisco Fernandez and Marco Tomassini and Leonardo
Vanneschi",
title = "An Empirical Study of Multipopulation Genetic
Programming",
journal = "Genetic Programming and Evolvable Machines",
year = "2003",
volume = "4",
number = "1",
pages = "21--51",
month = mar,
keywords = "genetic algorithms, genetic programming, distributed
evolutionary algorithms, parallel algorithms,
structured populations",
ISSN = "1389-2576",
doi = "doi:10.1023/A:1021873026259",
abstract = "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.",
notes = "Article ID: 5113071",
}
@InProceedings{fernandez03,
author = "Francisco Fernandez and Leonardo Vanneschi and Marco
Tomassini",
title = "The Effect of Plagues in Genetic Programming: {A}
Study of Variable-Size Populations",
booktitle = "Genetic Programming, Proceedings of EuroGP'2003",
year = "2003",
editor = "Conor Ryan and Terence Soule and Maarten Keijzer and
Edward Tsang and Riccardo Poli and Ernesto Costa",
volume = "2610",
series = "LNCS",
pages = "317--326",
address = "Essex",
publisher_address = "Berlin",
month = "14-16 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-00971-X",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=317",
abstract = "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.",
notes = "EuroGP'2003 held in conjunction with EvoWorkshops
2003",
}
@PhdThesis{fernandez:thesis,
author = "Francisco {Fernandez de Vega}",
title = "Distributed Genetic Programming Models with
Application to Logic Synthesis on {FPGA}s",
school = "University of Extremadura",
year = "2001",
email = "fcofdez@unex.es",
keywords = "genetic algorithms, genetic programming,
reconfigurable hardware",
URL = "http://cum.unex.es/profes/profes/fcofdez/escritorio/investigacion/pgp/thesis/phd.html",
size = "156 pages",
notes = "For Spanish version see
\cite{fernandez: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).",
}
@PhdThesis{fernandez:thesis:espanol,
author = "Francisco {Fernandez de Vega}",
title = "Modelos de Programacion Genetica Paralela y
Distribuida con aplicaciones a la Sintesis Logica en
{FPGA}s",
school = "University of Extremadura",
year = "2001",
email = "fcofdez@unex.es",
keywords = "genetic algorithms, genetic programming,
reconfigurable hardware",
URL = "http://cum.unex.es/profes/profes/fcofdez/escritorio/investigacion/pgp/thesis/phd.html",
size = "162 pages",
notes = "version espa\~{n}ol. for english version see
\cite{fernandez:thesis}",
}
@InProceedings{devega:2003:CEMAEB,
author = "Francisco {Fernandez de Vega}",
title = "Estudio de Poblaciones de tama\~{n}o variable en
Programacion Genetica",
booktitle = "Actas del II Congreso Espa\~{n}ol sobre
Metaheuristicas, Algoritmos Evolutivos y
Bioinspirados",
year = "2003",
pages = "424--428",
month = feb,
keywords = "genetic algorithms, genetic programming, bloat",
URL = "http://cum.unex.es/profes/profes/fcofdez/escritorio/investigacion/pgp/papers/maeb04.pdf",
abstract = "En este trabajo presentamos un estudio sobre el efecto
de poblaciones de tama\~{n}o variable en Programacion
Genetica. Por medio de una serie de experimentos
mostramos que la supresion sistematica de un n\'{u}mero
fijo de individuos a lo largo de varias generaciones
puede ayudar a reducir el esfuerzo computacional
requerido en la b\'{u}squeda 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\'{u}mero peque\~{n}o de individuos
en cada generacion.",
size = "6 pages",
notes = "in spanish",
}
@InProceedings{fernandez:2003:sceigpbmop,
author = "F. Fernandez and M. Tomassini and L. Vanneschi",
title = "Saving computational effort in genetic programming by
means of plagues",
booktitle = "Proceedings of the 2003 Congress on Evolutionary
Computation CEC2003",
editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass
and Kay Chen Tan and Bob McKay and Daryl Essam and Tom
Gedeon",
pages = "2042--2049",
year = "2003",
publisher = "IEEE Press",
address = "Canberra",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "8-12 " # dec,
organisation = "IEEE Neural Network Council (NNC), Engineers Australia
(IEAust), Evolutionary Programming Society (EPS),
Institution of Electrical Engineers (IEE)",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-7804-0",
abstract = "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.",
notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust,
the EPS, and the IEE.",
}
@InProceedings{fernandez:2004:eurogp,
author = "Francisco Fernandez and Aida Martin",
title = "Saving Effort in Parallel {GP} by means of Plagues",
booktitle = "Genetic Programming 7th European Conference, EuroGP
2004, Proceedings",
year = "2004",
editor = "Maarten Keijzer and Una-May O'Reilly and Simon M.
Lucas and Ernesto Costa and Terence Soule",
volume = "3003",
series = "LNCS",
pages = "269--278",
address = "Coimbra, Portugal",
publisher_address = "Berlin",
month = "5-7 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-21346-5",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=269",
abstract = "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.",
notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in
conjunction with EvoCOP2004 and EvoWorkshops2004",
}
@Article{Fernandez:2004:MM,
author = "Francisco {Fernandez de Vega} and J. I. Hidalgo and J.
Lanchares and J. M. Sanchez",
title = "A methodology for reconfigurable hardware design based
upon evolutionary computation",
journal = "Microprocessors and Microsystems",
year = "2004",
volume = "28",
number = "7",
pages = "363--371",
month = sep,
email = "fcofdez@unex.es",
keywords = "genetic algorithms, genetic programming,
reconfigurable hardware, Field programmable gate
arrays, Compact genetic algorithm, Configurable logic
blocks",
ISSN = "0141-9331",
URL = "http://www.sciencedirect.com/science/article/B6V0X-4C4BWW7-1/2/815fe7c17a6207d7a31f8046e4e2a5d1",
doi = "doi:10.1016/j.micpro.2004.03.017",
size = "9 pages",
abstract = "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.",
}
@InProceedings{Fernandez:PPSN:2004,
author = "Francisco Fernandez-de-Vega and German Galeano Gil and
Juan Antonio Gomez Pulido and Jose Luis Guisado",
title = "Control of bloat in Genetic Programming by means of
the Island Model",
booktitle = "Parallel Problem Solving from Nature - PPSN VIII",
year = "2004",
editor = "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\v{n}o Ata
Kab\'an and Hans-Paul Schwefel",
volume = "3242",
pages = "263--271",
series = "LNCS",
address = "Birmingham, UK",
publisher_address = "Berlin",
month = "18-22 " # sep,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-23092-0",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3242&spage=263",
doi = "doi:10.1007/b100601",
abstract = "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.",
notes = "PPSN-VIII",
}
@InCollection{fernandez2004,
author = "F. Fernandez and J. I. Hidalgo and J. M. Sanchez and
J. Lanchares",
title = "An Evolutionary Approach to Multi-{FPGA}s System
Synthesis",
booktitle = "Evolvable Machines: Theory \& Practice",
publisher = "Springer",
year = "2004",
editor = "Nadia Nedjah and Luiza {de Macedo Mourelle}",
volume = "161",
series = "Studies in Fuzziness and Soft Computing",
chapter = "7",
pages = "151--177",
address = "Berlin Hidelberg Germany",
keywords = "genetic algorithms, genetic programming,
reconfigurable hardware",
ISBN = "3-540-22905-1",
URL = "http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-175-22-33980449-0,00.html",
notes = "Springer says published in 2005 but available Nov
2004",
}
@InCollection{Fernandez:2005:pm,
author = "Francisco Fernandez and Giandomenico Spezzano and
Marco Tomassini and Leonardo Vanneschi",
title = "Parallel Genetic Programming",
booktitle = "Parallel Metaheuristics",
publisher = "Wiley-Interscience",
year = "2005",
editor = "Enrique Alba",
series = "Parallel and Distributed Computing",
chapter = "6",
pages = "127--153",
address = "Hoboken, New Jersey, USA",
keywords = "genetic algorithms, genetic programming, island model,
grid cellular structure, placement FPGA, EHW, cellular
genetic programming, ensemble of classifiers, CGPC,
bagCGPC",
ISBN = "0-471-67806-6",
notes = "Last example uses UCI cens (299285 tuples), 16 linux
myrinet pentium III nodes",
size = "27 pages",
}
@InCollection{FernandezdeVega:2005:HBBAA,
author = "Francisco {Fernandez de Vega}",
title = "Parallel Genetic Programming: Methodology, History,
and Application to Real-Life Problems",
booktitle = "Handbook of Bioinspired Algorithms and Applications",
publisher = "Chapman and Hall/CRC",
year = "2005",
editor = "Stephan Olariu and Albert Y. Zomaya",
series = "Computer \& Information Science Series",
chapter = "5",
pages = "5--65--5--84",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-58488-475-0",
doi = "doi:10.1201/9781420035063.ch5",
notes = "Reviewed by Kushal Chakrabarti, The Book Review Column
40(4), 2009, William Gasarch,
http://www.cs.umd.edu/~gasarch/bookrev/",
}
@Article{FernandezdeVega:2007:JPDC,
author = "Francisco {Fernandez de Vega} and Erick Cantu-Paz",
title = "Introduction to Special Issue on Parallel Bioinspired
Algorithms",
journal = "Journal of Parallel and Distributed Computing",
year = "2006",
volume = "66",
number = "8",
pages = "989--990",
month = aug,
email = "fcofdez@unex.es",
keywords = "genetic algorithms, genetic programming, Parallel
EAs",
ISSN = "0743-7315",
URL = "http://portal.acm.org/citation.cfm?id=1161625.1161626&coll=&dl=ACM",
}
@Article{FernandezdeVega:2008:SC,
author = "Francisco {Fernandez de Vega} and Erick Cantu-Paz",
title = "Special Issue on Distributed Bioinspired Algorithms",
journal = "Soft Computing",
year = "2008",
volume = "12",
number = "12",
pages = "1143--1144",
month = oct,
email = "fcofdez@unex.es",
keywords = "genetic algorithms, genetic programming, Parallel
EAs",
ISSN = "1432-7643",
URL = "http://www.springerlink.com/content/h57604076205127u/",
doi = "doi:10.1007/s00500-008-0299-7",
}
@Book{FernandezdeVega:pdci,
editor = "Francisco {Fernandez de Vega} and Erick Cantu-Paz",
title = "Parallel and Distributed Computational Intelligence",
publisher = "Springer",
year = "2010",
volume = "269",
series = "Studies in Computational Intelligence",
edition = "1st",
keywords = "genetic algorithms, genetic programming, Parallel
Computing, Distributed Computing, Grid Computing",
isbn13 = "978-3-642-10674-3",
URL = "http://www.springer.com/engineering/mathematical/book/978-3-642-10674-3",
doi = "doi:10.1007/978-3-642-10675-0",
abstract = "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.",
size = "354 pages",
}
@InProceedings{fernandez:1996:wrGPmsrp,
author = "Jaime J. Fernandez and Kristin A. Farry and John B.
Cheatham",
title = "Waveform Recognition Using Genetic Programming: The
Myoelectric Signal Recognition Problem",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "63--71",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
broken = "ftp://hobbes.jsc.nasa.gov/pub/jjf/gp96.gz",
size = "9 pages",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96",
}
@InProceedings{Fernandez:1997:tpsets,
author = "Thomas Fernandez and Matthew Evett",
title = "Training Period Size and Evolved Trading Systems",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "95",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/Fernandez_1997_tpsets.pdf",
size = "1 page",
notes = "GP-97",
}
@InProceedings{fernandez:1998:nmisrGP,
author = "Thomas Fernandez and Matthew Evett",
title = "Numeric Mutation as an Improvement to Symbolic
Regression in Genetic Programming",
booktitle = "Evolutionary Programming VII: Proceedings of the
Seventh Annual Conference on Evolutionary Programming",
year = "1998",
editor = "V. William Porto and N. Saravanan and D. Waagen and A.
E. Eiben",
volume = "1447",
series = "LNCS",
pages = "251--260",
address = "Mission Valley Marriott, San Diego, California, USA",
publisher_address = "Berlin",
month = "25-27 " # mar,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-64891-7",
doi = "doi:10.1007/BFb0040753",
notes = "EP-98. Florida Atlantic University, Boca Raton, FL",
}
@InProceedings{fernandez:vro:gecco2004,
author = "Thomas Fernandez",
title = "Virtual Ramping of Genetic Programming Populations",
booktitle = "Genetic and Evolutionary Computation -- GECCO-2004,
Part II",
year = "2004",
editor = "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",
series = "Lecture Notes in Computer Science",
pages = "471--482",
address = "Seattle, WA, USA",
publisher_address = "Heidelberg",
month = "26-30 " # jun,
organisation = "ISGEC",
publisher = "Springer-Verlag",
volume = "3103",
ISBN = "3-540-22343-6",
ISSN = "0302-9743",
URL = "http://link.springer.de/link/service/series/0558/bibs/3103/31030471.htm",
size = "12",
keywords = "genetic algorithms, genetic programming",
notes = "GECCO-2004 A joint meeting of the thirteenth
international conference on genetic algorithms
(ICGA-2004) and the ninth annual genetic programming
conference (GP-2004)",
}
@InProceedings{fernandez:1999:ABIFFRG,
author = "J. Jaime {Fernandez Jr.} and Ian D. Walker",
title = "A Biologically Inspired Fitness Function for Robotic
Grasping",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1517--1522",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, real world
applications",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-744.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-744b.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{Fernandez-Leiva:2011:IWINAC,
author = "Antonio Jose {Fernandez Leiva} and Jorge L. {O'Valle
Barragan}",
title = "Decision Tree-Based Algorithms for Implementing Bot
{AI} in {UT2004}",
booktitle = "Proceedings of the 4th International Work-Conference
on the Interplay Between Natural and Artificial
Computation, IWINAC 2011, Part I",
year = "2011",
editor = "Jose Manuel Ferrandez and Jose Ramon {Alvarez Sanchez}
and Felix {de la Paz} and F. Javier Toledo",
series = "Lecture Notes in Computer Science",
pages = "383--392",
volume = "6686",
address = "La Palma, Canary Islands, Spain",
month = may # " 30-" # jun # " 3",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-21343-4",
doi = "doi:10.1007/978-3-642-21344-1_40",
abstract = "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.",
affiliation = "Dept. Lenguajes y Ciencias de la Computacion, ETSI
Informatica, Campus de Teatinos, Universidad de Malaga,
29071 Malaga, Spain",
}
@Article{Fernandez-VillacanasMartin:2003:FGCS,
author = "Jose-Luis {Fernandez-Villacanas Martin} and Mark
Shackleton",
title = "Investigation of the importance of the
genotype-phenotype mapping in information retrieval",
journal = "Future Generation Computer Systems",
year = "2003",
volume = "19",
pages = "55--68",
number = "1",
keywords = "genetic algorithms, genetic programming,
Genotype-phenotype mapping, Information retrieval",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6V06-478HYP6-1/2/4edc0c200ae393af0e1c9cb343c0cf5d",
ISSN = "0167-739X",
doi = "doi:10.1016/S0167-739X(02)00108-5",
abstract = "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.",
}
@PhdThesis{Fernlund:thesis,
author = "Hans Karl Gustav Fernlund",
title = "Evolving models from observed human performance",
school = "Electrical Engineering and Computer Science,
University of Central Florida",
year = "2004",
address = "Orlando, Fla., USA",
month = "Spring Term",
keywords = "genetic algorithms, genetic programming, Context based
reasoning, CxBR, Human behavioral modeling, Learning by
observation, Simulation",
URL = "http://purl.fcla.edu/fcla/etd/CFE0000013",
size = "234 pages",
abstract = "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.",
notes = "Adviser: Avelino J. Gonzalez
http://ucf.catalog.fcla.edu/cf.jsp?Ntt=CF001100798&Ntk=Number&Nty=1&N=29&I=0&V=D
OpenGP",
}
@InProceedings{fernlund:2004:lbp,
author = "Hans Fernlund and Avelino J. Gonzalez",
title = "Using {GP} to Model Contextual Human Behavior -
Competitive with Human Modeling Performance",
booktitle = "Late Breaking Papers at the 2004 Genetic and
Evolutionary Computation Conference",
year = "2004",
editor = "Maarten Keijzer",
address = "Seattle, Washington, USA",
month = "26 " # jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/LBP015.pdf",
abstract = "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.",
notes = "Part of \cite{keijzer:2004:GECCO:lbp}",
}
@InProceedings{fernlund:ugt:gecco2004,
author = "Hans Fernlund and Avelino J. Gonzalez",
title = "Using {GP} to Model Contextual Human Behavior",
booktitle = "Genetic and Evolutionary Computation -- GECCO-2004,
Part II",
year = "2004",
editor = "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",
series = "Lecture Notes in Computer Science",
pages = "704--705",
address = "Seattle, WA, USA",
publisher_address = "Heidelberg",
month = "26-30 " # jun,
organisation = "ISGEC",
publisher = "Springer-Verlag",
volume = "3103",
ISBN = "3-540-22343-6",
ISSN = "0302-9743",
URL = "http://link.springer.de/link/service/series/0558/bibs/3103/31030704.htm",
size = "2",
keywords = "genetic algorithms, genetic programming, Poster",
notes = "GECCO-2004 A joint meeting of the thirteenth
international conference on genetic algorithms
(ICGA-2004) and the ninth annual genetic programming
conference (GP-2004)",
}
@Article{FGGD06,
author = "Hans K. G. Fernlund and Avelino J. Gonzalez and
Michael Georgiopoulos and Ronald F. DeMara",
title = "Learning tactical human behavior through observation
of human performance",
journal = "IEEE Transactions on Systems, Man and Cybernetics,
Part B",
volume = "36",
number = "1",
month = feb,
year = "2006",
pages = "128--140",
keywords = "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",
ISSN = "1083-4419",
URL = "http://www.cal.ucf.edu/journal/j_fernlund_gonzalez_itsmc_04.pdf",
doi = "doi:10.1109/TSMCB.2005.855568",
size = "13 pages",
abstract = "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.",
notes = "INSPEC Accession Number:8736964
Dept. of Culture, Dalarna Univ., Borlange, Sweden",
}
@Unpublished{Ferreira:2000:GEP,
author = "Candida Ferreira",
title = "Gene Expression Programming: a New Adaptive Algorithm
for Solving Problems",
note = "rejected for publication",
year = "2000",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.gene-expression-programming.com/webpapers/GEP.pdf",
abstract = "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.",
notes = "Date: Tue, 14 Nov 2000 21:04:44 -0100 To:
genetic-programming
From: Candida Ferreira
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",
size = "pages",
}
@Misc{ferreira:2001:WSC6,
author = "Candida Ferreira",
title = "{GEP} tutorial",
howpublished = "WSC6 tutorial",
year = "2001",
month = sep,
email = "candidaf@gene-expression-programming.com",
keywords = "genetic algorithms, genetic programming, Gene
Expression Programming",
URL = "http://www.gene-expression-programming.com/webpapers/GEPtutorial.pdf",
notes = "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",
}
@InProceedings{ferreira:2001:wsc6Aa,
author = "Candida Ferreira",
title = "Gene Expression Programming in Problem Solving",
booktitle = "Soft Computing and Industry Recent Applications",
year = "2001",
editor = "Rajkumar Roy and Mario K{\"o}ppen and Seppo Ovaska and
Takeshi Furuhashi and Frank Hoffmann",
pages = "635--654",
month = "10--24 " # sep,
publisher = "Springer-Verlag",
note = "Published 2002",
keywords = "genetic algorithms, genetic programming, Gene
Expression Programming",
ISBN = "1-85233-539-4",
URL = "http://www.gene-expression-programming.com/webpapers/ferreira-WSC6.pdf",
notes = "WSC6 Out of print?
http://www.amazon.co.uk/Soft-Computing-Industry-Recent-Applications/dp/1852335394
",
}
@Article{ferreira:2001:CS,
author = "C\^andida Ferreira",
title = "Gene Expression Programming: {A} New Adaptive
Algorithm for Solving Problems",
journal = "Complex Systems",
year = "2001",
volume = "13",
number = "2",
pages = "87--129",
email = "candidaf@gene-expressionprogramming.com",
keywords = "genetic algorithms, genetic programming, GEP",
URL = "http://www.gene-expression-programming.com/webpapers/GEPfirst.pdf",
URL = "http://www.complex-systems.com/Archive/hierarchy/abstract.cgi?vol=13&iss=2&art=01",
URL = "http://arXiv.org/abs/cs/0102027",
abstract = "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.",
notes = "Portuguese translation
http://www.gene-expression-programming.com/webpapers/GEPPort.pdf",
}
@InProceedings{ferreira:2002:EuroGP,
title = "Discovery of the {Boolean} Functions to the Best
Density-Classification Rules Using Gene Expression
Programming",
author = "C\^andida Ferreira",
editor = "James A. Foster and Evelyne Lutton and Julian Miller
and Conor Ryan and Andrea G. B. Tettamanzi",
booktitle = "Genetic Programming, Proceedings of the 5th European
Conference, EuroGP 2002",
volume = "2278",
series = "LNCS",
pages = "50--59",
publisher = "Springer-Verlag",
address = "Kinsale, Ireland",
publisher_address = "Berlin",
month = "3-5 " # apr,
year = "2002",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-43378-3",
abstract = "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.",
notes = "EuroGP'2002, part of \cite{lutton:2002:GP}",
}
@InProceedings{ferreira:2002:FEA,
author = "Candida Ferreira",
title = "Mutation, Transposition, and Recombination: An
Analysis of the Evolutionary Dynamics",
booktitle = "4th International Workshop on Frontiers in
Evolutionary Algorithms",
year = "2002",
editor = "Manuel Grana Romay and Richard Duro",
address = "North Carolina, USA",
month = "8-14 " # mar,
keywords = "genetic algorithms, genetic programming, gene
expression programming",
ISBN = "0-9707890-1-7",
URL = "http://www.gene-expression-programming.com/webpapers/ferreira-FEA02.pdf",
abstract = "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.",
notes = "Sat, 23 Mar 2002 17:52:10 GMT
genetic_programming@yahoogroups.com
FEA2002 In conjunction with Sixth Joint Conference on
Information Sciences",
}
@InProceedings{ferreira:2002:ASIA,
author = "Candida Ferreira",
title = "Combinatorial Optimization by Gene Expression
Programming: Inversion Revisited",
booktitle = "Proceedings of the Argentine Symposium on Artificial
Intelligence",
year = "2002",
editor = "J. M. Santos and A. Zapico",
pages = "160--174",
address = "Santa Fe, Argentina",
keywords = "genetic algorithms, genetic programming, GEP",
URL = "http://www.gene-expression-programming.com/webpapers/ferreira-ASAI02.pdf",
size = "9 pages",
abstract = "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.",
notes = "ASAI02
http://www.dc.uba.ar/people/profesores/santos/asai2002.html",
}
@InProceedings{ferreira:2002:WSC,
author = "C\^andida Ferreira",
title = "Function Finding and the Creation of Numerical
Constants in Gene Expression Programming",
booktitle = "7th Online World Conference on Soft Computing in
Industrial Applications",
year = "2002",
month = sep # " 23 - " # oct # " 4",
note = "on line",
keywords = "genetic algorithms, genetic programming, gene
expression programming",
URL = "http://www.gene-expression-programming.com/webpapers/Ferreira-WSC7.pdf",
abstract = "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.",
notes = "WSC7 http://wsc7.ugr.es/",
}
@Article{ferreira:2002:ACS,
author = "C. Ferreira",
title = "Genetic Representation and Genetic Neutrality in Gene
Expression Programming",
journal = "Advances in Complex Systems",
year = "2002",
volume = "5",
number = "4",
pages = "389--408",
keywords = "genetic algorithms, genetic programming, GEP, Genetic
neutrality, gene expression programming, evolutionary
computation",
URL = "http://www.gene-expression-programming.com/webpapers/Ferreira-ACS2002.pdf",
URL = "http://www.worldscinet.com/169/05/0504/S0219525902000626.html",
URL = "http://www.gepsoft.com/gep/webpapers/abstracts.asp#09",
abstract = "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.",
notes = "Tue, 18 Mar 2003 20:01:57 GMT
Wed, 28 Apr 2004 16:00:48 BST",
}
@Book{Ferreira:book,
author = "Candida Ferreira",
title = "Gene Expression Programming: Mathematical Modeling by
an Artificial Intelligence",
year = "2002",
keywords = "genetic algorithms, genetic programming, Gene
Expression Programming",
ISBN = "972-95890-5-4",
URL = "http://www.gene-expression-programming.com/gep/Books/index.asp",
notes = "Tue, 30 May 2006 11:21:33 BST to GP list replaced by
\cite{Ferreira:book2}
cf email Sun, 06 Jul 2003 18:40:43 BST GP list",
size = "272 pages",
}
@InProceedings{HreFer02,
author = "Candida Ferreira",
title = "Analyzing the Founder Effect in Simulated Evolutionary
Processes Using Gene Expression Programming",
booktitle = "Soft Computing Systems: Design, Management and
Applications",
year = "2002",
editor = "A. Abraham and J. Ruiz-del-Solar and M. K{\"o}ppen",
pages = "153--162",
organisation = "Gepsoft",
publisher = "IOS Press",
keywords = "genetic algorithms, genetic programming, Gene
Expression Programming",
ISBN = "1-58603-297-6",
URL = "http://www.gene-expression-programming.com/webpapers/ferreira-his02.pdf",
size = "10 pages",
abstract = "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.",
}
@InCollection{ferreira:2004:rdbic,
author = "Candida Ferreira",
title = "Gene expression programming and the automatic
evolution of computer programs",
booktitle = "Recent Developments in Biologically Inspired
Computing",
publisher = "Idea Group Publishing",
year = "2004",
editor = "Leandro N. {de Castro} and Fernando J. {Von Zuben}",
chapter = "6",
pages = "82--103",
keywords = "genetic algorithms, genetic programming, Gene
Expression Programming",
ISBN = "1-59140-312-X",
URL = "http://www.gene-expression-programming.com/gep/webpapers/abstracts.asp#11",
abstract = "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.",
notes = "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",
}
@InProceedings{ferreira:2004:wsc9,
author = "Candida Ferreira",
title = "Designing Neural Networks Using Gene Expression
Programming",
booktitle = "9th Online World Conference on Soft Computing in
Industrial Applications",
year = "2004",
editor = "Ajith Abraham and Mario K{\"o}ppen",
pages = "Paper No. 14",
address = "On the World Wide Web",
month = "20 " # sep # " - 8 " # oct,
organisation = "World Federation on Soft Computing (WFSC)",
keywords = "genetic algorithms, genetic programming, Gene
Expression Programming",
URL = "http://www.gene-expression-programming.com/webpapers/Ferreira-WSC9.pdf",
abstract = "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.",
notes = "WSC9",
}
@InCollection{Ferreira:2006:GSP,
author = "C\^{a}ndida Ferreira",
title = "Automatically Defined Functions in Gene Expression
Programming",
year = "2006",
booktitle = "Genetic Systems Programming: Theory and Experiences",
pages = "21--56",
volume = "13",
series = "Studies in Computational Intelligence",
editor = "Nadia Nedjah and Ajith Abraham and Luiza {de Macedo
Mourelle}",
publisher = "Springer",
address = "Germany",
keywords = "genetic algorithms, genetic programming, gene
expression programming, ADF",
ISBN = "3-540-29849-5",
URL = "http://www.gene-expression-programming.com/webpapers/Ferreira-GSP2006.pdf",
abstract = "
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.",
notes = "http://www.springer.com/sgw/cda/frontpage/0,11855,5-146-22-92733168-0,00.html",
}
@Book{Ferreira:book2,
author = "Candida Ferreira",
title = "Gene Expression Programming: Mathematical Modeling by
an Artificial Intelligence",
publisher = "Springer",
year = "2006",
edition = "2nd",
month = may,
keywords = "genetic algorithms, genetic programming, gene
expression programming",
ISBN = "3-540-32796-7",
notes = "Tue, 30 May 2006 11:21:33 BST
Genetic_Programming@Yahoogroups.Com 'This second
edition' of \cite{Ferreira: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.'",
size = "478 pages",
}
@InProceedings{Ferreira:wsc9,
author = "C. Ferreira",
title = "Designing Neural Networks Using Gene Expression
Programming",
booktitle = "Applied Soft Computing Technologies: The Challenge of
Complexity",
year = "2006",
editor = "A. Abraham and B. {de Baets} and M. Koppen and B.
Nickolay",
pages = "517--536",
address = "WWW",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, gene
expression programming",
isbn13 = "978-3-540-31649-7",
URL = "http://www.gene-expression-programming.com/webpapers/abstracts.asp#14",
abstract = "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.",
notes = "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.",
}
@InProceedings{conf/sbbd/FerreiraTGF08,
title = "Image Retrieval with Relevance Feedback based on
Genetic Programming",
author = "Cristiano D. Ferreira and Ricardo {da Silva Torres}
and Marcos Andre Goncalves and Weiguo Fan",
bibdate = "2009-03-02",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/sbbd/sbbd2008.html#FerreiraTGF08",
booktitle = "{XXIII} Simp{\'o}sio Brasileiro de Banco de Dados",
publisher = "SBC",
year = "2008",
editor = "Sandra {de Amo}",
isbn13 = "978-85-7669-205-8",
pages = "120--134",
URL = "http://www.lbd.dcc.ufmg.br:8080/colecoes/sbbd/2008/009.pdf",
address = "Campinas, {S}{\~a}o Paulo, Brasil",
month = "13-15 " # oct,
keywords = "genetic algorithms, genetic programming",
size = "15 pages",
abstract = "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.",
notes = "SBBD 2008. CBIR, COREL",
}
@Article{Ferreira201127,
author = "C. D. Ferreira and J. A. Santos and R. {da S. Torres}
and M. A. Goncalves and R. C. Rezende and Weiguo Fan",
title = "Relevance feedback based on genetic programming for
image retrieval",
journal = "Pattern Recognition Letters",
volume = "32",
number = "1",
pages = "27--37",
year = "2011",
note = "Image Processing, Computer Vision and Pattern
Recognition in Latin America",
ISSN = "0167-8655",
doi = "doi:10.1016/j.patrec.2010.05.015",
URL = "http://www.sciencedirect.com/science/article/B6V15-504123K-4/2/d925135e9c62c6da92ea517f2451d3bf",
keywords = "genetic algorithms, genetic programming, Relevance
feedback, Content-based image retrieval",
abstract = "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.",
}
@InProceedings{ferrer:1995:bef,
author = "Gabriel J. Ferrer and Worthy N. Martin",
title = "Using Genetic Programming to Evolve Board Evaluation
Functions for a Boardgame",
booktitle = "1995 IEEE Conference on Evolutionary Computation",
year = "1995",
volume = "2",
pages = "747",
address = "Perth, Australia",
publisher_address = "Piscataway, NJ, USA",
month = "29 " # nov # " - 1 " # dec,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, Senet",
broken = "http://www.cs.virginia.edu/~gjf2a/work/papers/senet.ps",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/senet.ps.gz",
size = "6 pages",
abstract = "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.",
notes = "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.
",
}
@InProceedings{FerrucciGOS10,
author = "Filomena Ferrucci and Carmine Gravino and Rocco
Oliveto and Federica Sarro",
title = "Genetic Programming for Effort Estimation: An Analysis
of the Impact of Different Fitness Functions",
booktitle = "Proceedings of the 2nd International Symposium on
Search Based Software Engineering (SSBSE '10)",
year = "2010",
pages = "89--98",
address = "Benevento, Italy",
month = "7-9 " # sep,
publisher = "IEEE",
editor = "Massimiliano {Di Penta} and Simon Poulding and Lionel
Briand and John Clark",
keywords = "genetic algorithms, genetic programming, SBSE",
doi = "doi:10.1109/SSBSE.2010.20",
owner = "Yuanyuan",
timestamp = "2010.09.08",
size = "10 pages",
abstract = "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.",
notes = "http://www.ssbse.org/program.php",
}
@InProceedings{Ferrucci:2011:SSBSE,
author = "Filomena Ferrucci and Carmine Gravino and Federica
Sarro",
title = "How Multi-Objective Genetic Programming Is Effective
for Software Development Effort Estimation?",
year = "2011",
booktitle = "Search Based Software Engineering",
editor = "Myra Cohen and Mel O'Cinneid",
volume = "6956",
series = "Lecture Notes in Computer Science",
pages = "274--275",
address = "Szeged, Hungary",
month = "10-12 " # sep,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, SBSE:
Poster",
isbn13 = "978-3-642-23715-7",
doi = "doi:10.1007/978-3-642-23716-4_28",
size = "1.3 page",
abstract = "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",
}
@PhdThesis{ficici:thesis,
author = "Sevan Gregory Ficici",
title = "Solution Concepts in Coevolutionary Algorithms",
school = "Computer Science Department, Brandeis University",
year = "2004",
address = "USA",
month = May,
keywords = "genetic algorithms, Coevolutionary Algorithms,
Evolutionary Game Theory, Machine Learning",
URL = "http://www.demo.cs.brandeis.edu/papers/long.html#ficici_thesis_04",
size = "299 pages",
abstract = "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.",
notes = "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)",
}
@Article{Ficko:2004:AJME,
author = "Mirko Ficko and Miha Kovacic and Miran Brezocnik",
title = "Genetic algorithms : a useful optimization method for
manufacturing problems",
journal = "Academic Journal of Manufacturing Engineering",
year = "2004",
volume = "2",
number = "1",
pages = "21--26",
keywords = "genetic algorithms, genetic programming, optimisation,
facility layout, flexible manufacturing systems",
ISSN = "1583-7904",
abstract = "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.",
notes = "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",
}
@Article{ficko:2004:JMPT,
author = "Mirko Ficko and Miran Brezocnik and Joze Balic",
title = "Designing the layout of single- and multiple-rows
flexible manufacturing system by genetic algorithms",
journal = "Journal of Materials Processing Technology",
year = "2004",
volume = "157-158",
pages = "150--158",
month = "20 " # dec,
keywords = "genetic algorithms, genetic programming, Flexible
manufacturing systems (FMS), Optimisation, Facility
layout",
ISSN = "0924-0136",
doi = "doi:10.1016/j.jmatprotec.2004.09.012",
abstract = "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.",
notes = "Special issue {"}Achievements in Mechanical and
Materials Engineering Conference{"} Edited by L. A.
Dobranski",
}
@Article{Ficko:2005:JMPT,
author = "M. Ficko and I. Drstvensek and M. Brezocnik and J.
Balic and B. Vaupotic",
title = "Prediction of total manufacturing costs for stamping
tool on the basis of {CAD}-model of finished product",
journal = "Journal of Materials Processing Technology",
year = "2005",
volume = "164-165",
pages = "1327--1335",
abstract = "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.",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6TGJ-4FJKWTY-D/2/17df3b2567564f2d6c9d8fdcb041d0e9",
month = "15 " # may,
keywords = "genetic algorithms, genetic programming, Prediction of
costs, Tool-making, Stamping, CAD-model, Intelligent
systems",
ISSN = "0924-0136",
doi = "doi:10.1016/j.jmatprotec.2005.02.013",
notes = "AMPT/AMME05 Part 2",
}
@InProceedings{fidelis:2000:DCCRGA,
author = "M. V. Fidelis and H. S. Lopes and A. A. Freitas",
title = "Discovering Comprehensible Classification Rules a
Genetic Algorithm",
booktitle = "Proceedings of the 2000 Congress on Evolutionary
Computation CEC00",
year = "2000",
volume = "1",
pages = "805--810",
address = "La Jolla Marriott Hotel La Jolla, California, USA",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "6-9 " # jul,
organisation = "IEEE Neural Network Council (NNC), Evolutionary
Programming Society (EPS), Institution of Electrical
Engineers (IEE)",
publisher = "IEEE Press",
keywords = "genetic algorithms, data mining",
ISBN = "0-7803-6375-2",
URL = "http://www.cpgei.cefetpr.br/~hslopes/publicacoes/2000/cec2000a.zip",
notes = "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",
}
@InProceedings{eurogp06:FillonBartoli,
author = "Cyril Fillon and Alberto Bartoli",
title = "A Divide and Conquer strategy for improving efficiency
and probability of success in Genetic Programming",
editor = "Pierre Collet and Marco Tomassini and Marc Ebner and
Steven Gustafson and Anik\'o Ek\'art",
booktitle = "Proceedings of the 9th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3905",
year = "2006",
address = "Budapest, Hungary",
month = "10 - 12 " # apr,
organisation = "EvoNet",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-33143-3",
pages = "13--23",
email = "cfillon@units.it",
URL = "http://link.springer.de/link/service/series/0558/papers/3905/39050013.pdf",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in
conjunction with EvoCOP2006 and EvoWorkshops2006",
}
@InProceedings{eurogp07:fillon,
author = "Cyril Fillon and Alberto Bartoli",
title = "Multi-objective Genetic Programming for Improving the
Performance of {TCP}",
editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and
Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar",
booktitle = "Proceedings of the 10th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "4445",
year = "2007",
address = "Valencia, Spain",
month = "11-13 " # apr,
pages = "170--180",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-71602-5",
isbn13 = "978-3-540-71602-0",
doi = "doi:10.1007/978-3-540-71605-1_16",
abstract = "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.",
notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in
conjunction with EvoCOP2007, EvoBIO2007 and
EvoWorkshops2007",
}
@InProceedings{Fillon:2007:cec,
author = "Cyril Fillon and Alberto Bartoli",
title = "Symbolic Regression of Discontinuous and Multivariate
Functions by Hyper-Volume Error Separation ({HVES})",
booktitle = "2007 IEEE Congress on Evolutionary Computation",
year = "2007",
editor = "Dipti Srinivasan and Lipo Wang",
pages = "23--30",
address = "Singapore",
month = "25-28 " # sep,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "1-4244-1340-0",
file = "1757.pdf",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and
the IET.
IEEE Catalog Number: 07TH8963C",
}
@InCollection{finkel:2003:UGPEAFN,
author = "Jenny Rose Finkel",
title = "Using Genetic Programming to Evolve an Algorithm for
Factoring Numbers",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2003",
year = "2003",
editor = "John R. Koza",
pages = "52--60",
address = "Stanford, California, 94305-3079 USA",
month = "4 " # dec,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.genetic-programming.org/sp2003/Finkel.pdf",
notes = "part of \cite{koza:2003:gagp}",
}
@InProceedings{finley:1999:E,
author = "Marion R. {Finley Jr.} and Haruo Akimaru and Evelyne
B. Hausen-Tropper",
title = "Element of a theoretical model of tele-learning using
genetic algorithms",
booktitle = "Late Breaking Papers at the 1999 Genetic and
Evolutionary Computation Conference",
year = "1999",
editor = "Scott Brave and Annie S. Wu",
pages = "93--98",
address = "Orlando, Florida, USA",
month = "13 " # jul,
keywords = "Genetic Algorithms",
notes = "GECCO-99LB",
}
@InProceedings{eurogp:FirpiGE05,
author = "Hiram Firpi and Erik D. Goodman and Javier Echauz",
editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre
Collet and Jano I. {van Hemert} and Marco Tomassini",
title = "On Prediction of Epileptic Seizures by Computing
Multiple Genetic Programming Artificial Features",
booktitle = "Proceedings of the 8th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3447",
year = "2005",
address = "Lausanne, Switzerland",
month = "30 " # mar # " - 1 " # apr,
organisation = "EvoNet",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-25436-6",
pages = "321--330",
URL = "http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=321",
doi = "doi:10.1007/b107383",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in
conjunction with EvoCOP2005 and EvoWorkshops2005",
}
@InProceedings{1068082,
author = "Hiram Firpi and Erik Goodman and Javier Echauz",
title = "Epileptic seizure detection by means of genetically
programmed artificial features",
booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on
Genetic and evolutionary computation",
year = "2005",
editor = "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",
volume = "1",
ISBN = "1-59593-010-8",
pages = "461--466",
address = "Washington DC, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p461.pdf",
doi = "doi:10.1145/1068009.1068082",
publisher = "ACM Press",
publisher_address = "New York, NY, 10286-1405, USA",
month = "25-29 " # jun,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, Biological
Applications, design, epilepsy, feature extraction,
seizure detection, state-space reconstruction",
size = "6 pages",
abstract = "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.",
notes = "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",
}
@Article{FGE:OPE:06,
title = "On Prediction of Epileptic Seizures by Means of
Genetic Programming Artificial Features",
author = "Hiram Firpi and Erik Goodman and Javier Echauz",
journal = "Annals of Biomedical Engineering",
year = "2006",
pages = "515--529",
volume = "34",
number = "3",
month = mar,
keywords = "genetic algorithms, genetic programming, Epilepsy,
Seizure prediction, Artificial feature, Feature
extraction, State-space reconstruction",
doi = "doi:10.1007/s10439-005-9039-7",
abstract = "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.",
}
@Article{Firpi:2007:BE,
title = "Epileptic Seizure Detection Using Genetically
Programmed Artificial Features",
author = "Hiram Firpi and Erik D. Goodman and Javier Echauz",
journal = "IEEE Transactions on Biomedical Engineering",
year = "2007",
volume = "54",
number = "2",
pages = "212--224",
doi = "doi:10.1109/TBME.2006.886936",
ISSN = "0018-9294",
month = feb,
keywords = "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",
abstract = "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",
}
@Article{Firpi2008558,
author = "Hiram Firpi and George Vachtsevanos",
title = "Genetically programmed-based artificial features
extraction applied to fault detection",
journal = "Engineering Applications of Artificial Intelligence",
volume = "21",
number = "4",
pages = "558--568",
year = "2008",
ISSN = "0952-1976",
doi = "doi:10.1016/j.engappai.2007.06.004",
URL = "http://www.sciencedirect.com/science/article/B6V2M-4PG2RVD-1/2/83e1929229a124416738c8ec59137146",
keywords = "genetic algorithms, genetic programming, Fault
detection, Feature extraction, Artificial feature,
Conventional feature",
abstract = "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.",
}
@InCollection{fischer:1994:bmpm,
author = "Ronald F. Fischer",
title = "Applying Genetic Algorithms to Bitmap Pattern
Matching",
booktitle = "Genetic Algorithms at Stanford 1994",
year = "1994",
editor = "John R. Koza",
pages = "41--48",
address = "Stanford, California, 94305-3079 USA",
month = dec,
organisation = "Stanford University",
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, GENESIS",
ISBN = "0-18-187263-3",
notes = "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",
}
@InProceedings{Fiser:2010:DDECS,
author = "Petr Fiser and Jan Schmidt and Zdenek Vasicek and
Lukas Sekanina",
title = "On logic synthesis of conventionally hard to
synthesize circuits using genetic programming",
booktitle = "13th IEEE International Symposium on Design and
Diagnostics of Electronic Circuits and Systems (DDECS),
2010",
year = "2010",
month = apr,
pages = "346--351",
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/DDECS.2010.5491755",
notes = "Also known as \cite{5491755}",
}
@InProceedings{Fitzgerald:2011:GECCO,
author = "Jeannie Fitzgerald and Conor Ryan",
title = "Drawing boundaries: using individual evolved class
boundaries for binary classification problems",
booktitle = "GECCO '11: Proceedings of the 13th annual conference
on Genetic and evolutionary computation",
year = "2011",
editor = "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",
isbn13 = "978-1-4503-0557-0",
pages = "1347--1354",
keywords = "genetic algorithms, genetic programming",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001576.2001758",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{2001758} GECCO-2011 A joint
meeting of the twentieth international conference on
genetic algorithms (ICGA-2011) and the sixteenth annual
genetic programming conference (GP-2011)",
}
@InProceedings{DBLP:conf/ichit/FitzgeraldR11,
author = "Jeannie Fitzgerald and Conor Ryan",
title = "Validation Sets for Evolutionary Curtailment with
Improved Generalisation",
booktitle = "5th International Conference on Convergence and Hybrid
Information Technology, ICHIT 2011",
year = "2011",
editor = "Geuk Lee and Daniel Howard and Dominik Slezak",
volume = "6935",
series = "Lecture Notes in Computer Science",
pages = "282--289",
address = "Daejeon, Korea",
month = sep # " 22-24",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-24081-2",
doi = "doi:10.1007/978-3-642-24082-9_35",
size = "8 page",
abstract = "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.",
notes = "ICHIT (1)",
affiliation = "Jeannie Fitzgerald, BDS Group, CSIS Department,
University of Limerick, Ireland",
}
@InProceedings{Fitzgerald:2011:SGAI,
author = "Jeannie Fitzgerald and Conor Ryan",
title = "Validation Sets, Genetic Programming and
Generalisation",
booktitle = "Proceedings of the 31st SGAI International Conference
on Innovative Techniques and Applications of Artificial
Intelligence, AI-2011",
year = "2011",
editor = "Max Bramer and Miltos Petridis and Lars Nolle",
pages = "79--92",
address = "Cambridge, England",
publisher_address = "London",
month = dec,
organisation = "BCS special interest group on Artificial
Intelligence",
publisher = "Springer",
note = "Research and Development in Intelligent Systems
XXVIII, Incorporating Applications and Innovations in
Intelligent Systems XIX",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4471-2318-7",
doi = "doi:10.1007/978-1-4471-2318-7_6",
abstract = "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.",
affiliation = "BDS Group, CSIS Department, University of Limerick,
Limerick, Ireland",
}
@MastersThesis{Flack:mastersthesis,
author = "Robert Flack",
title = "Evolution of Architectural Floor Plans",
school = "Brock University",
year = "2011",
keywords = "genetic algorithms, genetic programming",
}
@InCollection{flannery:2000:TETBPML,
author = "Matthew Flannery",
title = "The Evolution of Traffic Behavior Patterns on a
Macroscopic Level",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2000",
year = "2000",
editor = "John R. Koza",
pages = "135--142",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "part of \cite{koza:2000:gagp}",
}
@InProceedings{Flasch:2010:geccocomp,
author = "Oliver Flasch and Olaf Mersmann and Thomas
Bartz-Beielstein",
title = "{RGP}: an open source genetic programming system for
the {R} environment",
booktitle = "GECCO 2010 Late breaking abstracts",
year = "2010",
editor = "Daniel Tauritz",
isbn13 = "978-1-4503-0073-5",
keywords = "genetic algorithms, genetic programming",
pages = "2071--2072",
month = "7-11 " # jul,
organisation = "SIGEVO",
address = "Portland, Oregon, USA",
doi = "doi:10.1145/1830761.1830867",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{1830867} Distributed on CD-ROM at
GECCO-2010.
ACM Order Number 910102.",
}
@InProceedings{Flasch:2010:cec,
author = "Oliver Flasch and Thomas Bartz-Beielstein and Artur
Davtyan and Patrick Koch and Wolfgang Konen and Tosin
Daniel Oyetoyan and Michael Tamutan",
title = "Comparing {SPO}-tuned {GP} and {NARX} prediction
models for stormwater tank fill level prediction",
booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-6910-9",
abstract = "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.",
doi = "doi:10.1109/CEC.2010.5586172",
notes = "WCCI 2010. Also known as \cite{5586172}",
}
@InCollection{flight:1997:psGPtmt,
author = "John Flight",
title = "The Use of Program State by a Genetic Program to Track
a Moving Target",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1997",
publisher = "Stanford Bookstore",
year = "1997",
editor = "John R. Koza",
pages = "57-",
address = "Stanford, California, 94305-3079 USA",
month = "17 " # mar,
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-205981-2",
abstract = "how a GP might use state variables and feedback from
the fitness measure",
notes = "part of \cite{koza:1997:GAGPs}",
}
@InCollection{Flister:1997:rational,
author = "Erik D. Flister",
title = "The Deceptive Problem of Rational Trading and
Negotiation Strategies in Artificial Economic
Communities",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1997",
publisher = "Stanford Bookstore",
year = "1997",
editor = "John R. Koza",
pages = "66--75",
address = "Stanford, California, 94305-3079 USA",
month = "17 " # mar,
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-205981-2",
notes = "part of \cite{koza:1997:GAGPs}",
}
@InProceedings{conf/wilf/Floares05,
title = "Genetic Programming and Neural Networks Feedback
Linearization for Modeling and Controlling Complex
Pharmacogenomic Systems",
author = "Alexandru Floares",
year = "2005",
editor = "Isabelle Bloch and Alfredo Petrosino and Andrea
Tettamanzi",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3849",
booktitle = "Fuzzy Logic and Applications, 6th International
Workshop, WILF 2005, Revised Selected Papers",
pages = "178--187",
address = "Crema, Italy",
month = sep # " 15-17",
bibdate = "2006-02-22",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/wilf/wilf2005.html#Floares05",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-32529-8",
doi = "doi:10.1007/11676935_22",
abstract = "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.",
notes = "Published 2006?",
}
@InProceedings{Floares:2006:CEC,
author = "Alexandru G. Floares",
title = "Computation Intelligence Tools for Modeling and
Controlling Pharmacogenomic Systems: Genetic
Programming and Neural Networks",
booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary
Computation",
year = "2006",
editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and
Simon M. Lucas",
pages = "7510--7517",
address = "Vancouver",
month = "16-21 " # jul,
publisher = "IEEE Press",
keywords = "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",
ISBN = "0-7803-9487-9",
size = "8 pages",
abstract = "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.",
notes = "May 2010
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1716624&tag=1
\cite{conf/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",
}
@InProceedings{Floares:2008:ijcnn,
author = "Alexandru George Floares",
title = "Automatic Inferring Drug Gene Regulatory Networks with
Missing Information Using Neural Networks and Genetic
Programming",
booktitle = "2008 IEEE World Congress on Computational
Intelligence",
year = "2008",
editor = "Jun Wang",
pages = "3078--3085",
address = "Hong Kong",
month = "1-6 " # jun,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-1821-3",
file = "NN0852.pdf",
doi = "doi:10.1109/IJCNN.2008.4634233",
ISSN = "1098-7576",
abstract = "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.",
keywords = "genetic algorithms, genetic programming",
notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
EPS and the IET. Also known as \cite{4634233}",
}
@Article{Floares2008379,
author = "Alexandru George Floares",
title = "A reverse engineering algorithm for neural networks,
applied to the subthalamopallidal network of basal
ganglia",
journal = "Neural Networks",
volume = "21",
number = "2-3",
pages = "379--386",
year = "2008",
note = "Advances in Neural Networks Research: IJCNN '07, 2007
International Joint Conference on Neural Networks IJCNN
'07",
ISSN = "0893-6080",
doi = "doi:10.1016/j.neunet.2007.12.017",
URL = "http://www.sciencedirect.com/science/article/B6T08-4RDR1B6-1/2/5aae1d094dbe3fd190fbb3fe9acebe63",
keywords = "genetic algorithms, genetic programming, Neural
networks, Reverse engineering algorithm, Linear genetic
programming, Systems of ordinary differential
equations, Basal ganglia, Discovery science approach",
abstract = "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.",
}
@InProceedings{Floares:2009:IJCNN,
author = "Alexandru George Floares",
title = "A neural networks algorithm for inferring drug gene
regulatory networks from microarray time-series with
missing transcription factors information",
booktitle = "International Joint Conference on Neural Networks,
IJCNN 2009",
year = "2009",
month = jun,
pages = "848--854",
keywords = "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",
doi = "doi:10.1109/IJCNN.2009.5179081",
ISSN = "1098-7576",
abstract = "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.",
notes = "Also known as \cite{5179081}",
}
@InProceedings{Floares:2010:SOFA,
author = "Alexandru Floares and Ovidiu Balacescu and Carmen
Floares and Loredana Balacescu and Tiberiu Popa and
Oana Vermesan",
title = "Mining knowledge and data to discover intelligent
molecular biomarkers: Prostate cancer i-Biomarkers",
booktitle = "4th International Workshop on Soft Computing
Applications (SOFA 2010)",
year = "2010",
month = "15-17 " # jul,
pages = "113--118",
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/SOFA.2010.5565613",
notes = "Discipulus Also known as \cite{5565613}",
}
@InProceedings{Floreano:1997:gsrq,
author = "Dario Floreano and Stefano Nolfi",
title = "God Save the Red Queen! Competition in Co-Evolutionary
Robotics",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "Artifical life and evolutionary robotics",
pages = "398--406",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
URL = "ftp://kant.irmkant.rm.cnr.it/pub/econets/floreano.co-evolution.ps.Z",
notes = "GP-97",
}
@InProceedings{viento,
author = "Juan J. Flores and Mario Graff and Erasmo Cadenas",
title = "Wind Prediction using Genetic Algorithms and Gene
Expression Programming",
booktitle = "Proceedings of the International Conference on
Modelling and Simulation in the Enterprises. AMSE
2005",
year = "2005",
address = "Morelia, Mexico",
month = apr,
keywords = "genetic algorithms, genetic programming, Gene
Expression Programming",
notes = "information from Juan Flores
Thu, 15 Jun 2006 10:14:13 PDT",
}
@InProceedings{conf/iscis/FloresG05,
title = "System Identification Using Genetic Programming and
Gene Expression Programming",
author = "Juan J. Flores and Mario Graff",
year = "2005",
pages = "503--511",
booktitle = "Proceedings of the 20th International Symposium
Computer and Information Sciences - ISCIS 2005",
editor = "Pinar Yolum and Tunga Gungor and Fikret Gurgen and Can
Ozturan",
volume = "3733",
series = "Lecture Notes in Computer Science",
address = "Istanbul, Turkey",
publisher_address = "Berlin / Heidelberg",
month = oct # " 26-28",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, Gene
Expression Programming",
bibdate = "2005-12-14",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/iscis/iscis2005.html#FloresG05",
ISSN = "0302-9743",
ISBN = "3-540-29414-7",
doi = "doi:10.1007/11569596",
abstract = "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.",
}
@Article{Fogel199582,
author = "David B. Fogel",
title = "Advances in genetic programming : Kenneth {E}.
Kinnear, Jr., (ed.), {MIT} Press, Cambridge, {MA},
1994, 518 pp., \$45.00",
journal = "Biosystems",
volume = "36",
number = "1",
pages = "82--85",
year = "1995",
keywords = "genetic algorithms, genetic programming",
ISSN = "0303-2647",
doi = "doi:10.1016/0303-2647(95)90007-1",
URL = "http://www.sciencedirect.com/science/article/B6T2K-4CHS0P6-5/2/2474f3669e7a25204939e72cbb4d7253",
notes = "review of \cite{kinnear:book}",
abstract = "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.",
}
@InProceedings{fogel:1996:pedcs,
author = "David B. Fogel and Lawrence J. Fogel",
title = "Preliminary Experiments on Discriminating between
Chaotic Signals and Noise Using Evolutionary
Programming",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "Evolutionary Programming",
pages = "512--520",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96 EP paper",
}
@Article{Fogel:2004:NAR,
author = "Gary B. Fogel and Dana G. Weekes and Gabor Varga and
Ernst R. Dow and Harry B. Harlow and Jude E. Onyia and
Chen Su",
title = "Discovery of sequence motifs related to coexpression
of genes using evolutionary computation",
journal = "Nucleic Acids Research",
year = "2004",
volume = "32",
number = "13",
pages = "3826--3835",
doi = "doi:10.1093/nar/gkh713",
abstract = "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.",
notes = "PMID:",
}
@TechReport{vuwlgp-report,
author = "Christopher Fogelberg and Mengjie Zhang",
title = "{VUWLGP} - An {ANSI} {C}++ Linear Genetic Programming
Package",
institution = "MSCS, Victoria University of Wellington",
year = "2005",
number = "CS-TR-05/8",
address = "New Zealand",
email = "christo.fogelberg@mcs.vuw.ac.nz",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.syntilect.com/cgf/body00040.php",
URL = "http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-05-08.abs.html",
abstract = "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.",
}
@InProceedings{conf/ausai/FogelbergZ05,
title = "Linear Genetic Programming for Multi-class Object
Classification",
author = "Christopher Fogelberg and Mengjie Zhang",
year = "2005",
bibdate = "2005-11-29",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/ausai/ausai2005.html#FogelbergZ05",
pages = "369--379",
booktitle = "AI 2005: Advances in Artificial Intelligence, 18th
Australian Joint Conference on Artificial Intelligence,
Proceedings",
editor = "Shichao Zhang and Ray Jarvis",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3809",
address = "Sydney, Australia",
month = dec # " 5-9",
bibsource = "DBLP, http://dblp.uni-trier.de",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-30462-2",
doi = "doi:10.1007/11589990_39",
size = "11 pages",
abstract = "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.",
}
@InProceedings{Fogelson:2008:gecco,
author = "Sergey V. Fogelson and Walter D. Potter",
title = "A formulation for the relative permittivity of water
and steam to high temperatures and pressures evolved
using genetic programming",
booktitle = "GECCO '08: Proceedings of the 10th annual conference
on Genetic and evolutionary computation",
year = "2008",
editor = "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",
isbn13 = "978-1-60558-130-9",
pages = "1335--1336",
address = "Atlanta, GA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1335.pdf",
doi = "doi:10.1145/1389095.1389351",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "12-16 " # jul,
keywords = "genetic algorithms, genetic programming, relative
permittivity: Poster",
notes = "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 \cite{1389351}",
}
@Article{Fok:2007:ieeeIS,
author = "Ka-Ling Fok and Tien-Tsin Wong and Man-Leung Wong",
title = "Evolutionary Computing on Consumer Graphics Hardware",
journal = "IEEE Intelligent Systems",
year = "2007",
volume = "22",
number = "2",
pages = "69--78",
month = mar # "-" # apr,
keywords = "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",
ISSN = "1541-1672",
URL = "http://ieeexplore.ieee.org/iel5/9670/4136845/04136862.pdf?tp=&isnumber=4136845&arnumber=4136862&punumber=9670",
doi = "doi:10.1109/MIS.2007.28",
size = "10 pages",
abstract = "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",
notes = "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",
}
@InProceedings{folino:1999:ACGPAC,
author = "Gianluigi Folino and Clara Pizzuti and Giandomenico
Spezzano",
title = "A Cellular Genetic Programming Approach to
Classification",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1015--1020",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-611-4",
URL = "http://citeseer.ist.psu.edu/328823.html",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-427.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-427.ps",
abstract = "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.",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{folino:2000:GPSAhmeDT,
author = "Gianluigi Folino and Clara Pizzuti and Giandomenico
Spezzano",
title = "Genetic Programming and Simulated Annealing: {A}
Hybrid Method to Evolve Decision Trees",
booktitle = "Genetic Programming, Proceedings of EuroGP'2000",
year = "2000",
editor = "Riccardo Poli and Wolfgang Banzhaf and William B.
Langdon and Julian F. Miller and Peter Nordin and
Terence C. Fogarty",
volume = "1802",
series = "LNCS",
pages = "294--303",
address = "Edinburgh",
publisher_address = "Berlin",
month = "15-16 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-67339-3",
URL = "http://www.icar.cnr.it/pizzuti/eurogp00.ps",
URL = "http://citeseer.ist.psu.edu/326715.html",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=294",
size = "11 pages",
abstract = "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.",
notes = "EuroGP'2000, part of \cite{poli:2000:GP}",
}
@InProceedings{folino:2001:EuroGP,
author = "Gianluigi Folino and Clara Pizzuti and Giandomenico
Spezzano",
title = "{CAGE}: {A} Tool for Parallel Genetic Programming
Applications",
booktitle = "Genetic Programming, Proceedings of EuroGP'2001",
year = "2001",
editor = "Julian F. Miller and Marco Tomassini and Pier Luca
Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
William B. Langdon",
volume = "2038",
series = "LNCS",
pages = "64--73",
address = "Lake Como, Italy",
publisher_address = "Berlin",
month = "18-20 " # apr,
organisation = "EvoNET",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, Parallel
programming, Cellular model",
ISBN = "3-540-41899-7",
URL = "http://www.icar.cnr.it/pizzuti/eurogp01.ps",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=64",
size = "10 pages",
abstract = "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.",
notes = "EuroGP'2001, part of \cite{miller:2001:gp}",
}
@InProceedings{folino:2001:TAI,
author = "Gianluigi Folino and Clara Pizzuti and Giandomenico
Spezzano",
title = "Parallel genetic programming for decision tree
induction",
booktitle = "Proceedings of the 13th International Conference on
Tools with Artificial Intelligence",
year = "2001",
pages = "129--135",
address = "Dallas, TX USA",
month = "7-9 " # nov,
publisher = "IEEE",
keywords = "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",
URL = "http://www.icar.cnr.it/pizzuti/ictai01.ps",
size = "7 pages",
abstract = "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",
notes = "Inspec Accession Number: 7139478",
}
@InProceedings{folino:2002:euromicro,
author = "Gianluigi Folino and Clara Pizzuti and Giandomenico
Spezzano",
title = "Improving induction decision trees with parallel
genetic programming",
booktitle = "Proceedings 10th Euromicro Workshop on Parallel,
Distributed and Network-based Processing",
year = "2002",
pages = "181--187",
address = "Canary Islands",
month = "9-11 " # jan,
publisher = "IEEE",
keywords = "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",
abstract = "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",
notes = "Inspec Accession Number: 7205091",
}
@Article{folino:2003:tec,
author = "Gianluigi Folino and Clara Pizzuti and Giandomenico
Spezzano",
title = "A Scalable Cellular Implementation of Parallel Genetic
Programming",
journal = "IEEE Transactions on Evolutionary Computation",
year = "2003",
volume = "7",
number = "1",
pages = "37--53",
month = feb,
keywords = "genetic algorithms, genetic programming, Cellular
genetic programming model, load balance, parallel
processing, scalability",
abstract = "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.",
notes = "CAGE",
}
@InProceedings{folino03,
author = "Gianluigi Folino and Clara Pizzuti and Giandomenico
Spezzano",
title = "Ensemble techniques for Parallel Genetic Programming
based Classifiers",
booktitle = "Genetic Programming, Proceedings of EuroGP'2003",
year = "2003",
editor = "Conor Ryan and Terence Soule and Maarten Keijzer and
Edward Tsang and Riccardo Poli and Ernesto Costa",
volume = "2610",
series = "LNCS",
pages = "59--69",
address = "Essex",
publisher_address = "Berlin",
month = "14-16 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-00971-X",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=59",
abstract = "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.",
notes = "EuroGP'2003 held in conjunction with EvoWorkshops
2003",
}
@InProceedings{folino:2003:daicamgp,
author = "G. Folino and C. Pizzuti and G. Spezzano and L.
Vanneschi and M. Tomassini",
title = "Diversity analysis in cellular and multipopulation
genetic programming",
booktitle = "Proceedings of the 2003 Congress on Evolutionary
Computation CEC2003",
editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass
and Kay Chen Tan and Bob McKay and Daryl Essam and Tom
Gedeon",
pages = "305--311",
year = "2003",
publisher = "IEEE Press",
address = "Canberra",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "8-12 " # dec,
organisation = "IEEE Neural Network Council (NNC), Engineers Australia
(IEAust), Evolutionary Programming Society (EPS),
Institution of Electrical Engineers (IEE)",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.icar.cnr.it/pizzuti/cec03.pdf",
ISBN = "0-7803-7804-0",
abstract = "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.",
notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust,
the EPS, and the IEE.",
}
@InProceedings{folino:2004:eurogp,
author = "Gianluigi Folino and Clara Pizzuti and Giandomenico
Spezzano",
title = "Boosting technique for Combining Cellular {GP}
Classifiers",
booktitle = "Genetic Programming 7th European Conference, EuroGP
2004, Proceedings",
year = "2004",
editor = "Maarten Keijzer and Una-May O'Reilly and Simon M.
Lucas and Ernesto Costa and Terence Soule",
volume = "3003",
series = "LNCS",
pages = "47--56",
address = "Coimbra, Portugal",
publisher_address = "Berlin",
month = "5-7 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-21346-5",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=47",
abstract = "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.",
notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in
conjunction with EvoCOP2004 and EvoWorkshops2004",
}
@InProceedings{folino:2005:gsice,
author = "Gianluigi Folino and Clara Pizzuti and Giandomenico
Spezzano",
title = "{GP} Ensembles for improving multi-class prediction
problems",
booktitle = "AI*IA Workshop on Evolutionary Computation,
Evoluzionistico GSICE05",
year = "2005",
editor = "Sara Manzoni and Matteo Palmonari and Fabio Sartori",
address = "University of Milan Bicocca, Italy",
month = "20 " # sep,
keywords = "genetic algorithms, genetic programming, data mining,
classification, boosting",
ISBN = "88-900910-0-2",
size = "10 pages",
abstract = "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.",
notes = "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",
}
@InProceedings{eurogp06:FolinoSpezzano,
author = "Gianluigi Folino and Giandomenico Spezzano",
title = "{P-CAGE:} An Environment for Evolutionary Computation
in Peer-to-Peer Systems",
editor = "Pierre Collet and Marco Tomassini and Marc Ebner and
Steven Gustafson and Anik\'o Ek\'art",
booktitle = "Proceedings of the 9th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3905",
year = "2006",
address = "Budapest, Hungary",
month = "10 - 12 " # apr,
organisation = "EvoNet",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-33143-3",
pages = "341--350",
URL = "http://link.springer.de/link/service/series/0558/papers/3905/39050341.pdf",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in
conjunction with EvoCOP2006 and EvoWorkshops2006",
}
@InProceedings{1144139,
author = "Gianluigi Folino and Clara Pizzuti and Giandomenico
Spezzano",
title = "Improving cooperative {GP} ensemble with clustering
and pruning for pattern classification",
booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference
on Genetic and evolutionary computation",
year = "2006",
editor = "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",
volume = "1",
ISBN = "1-59593-186-4",
pages = "791--798",
address = "Seattle, Washington, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p791.pdf",
doi = "doi:10.1145/1143997.1144139",
publisher = "ACM Press",
publisher_address = "New York, NY, 10286-1405, USA",
month = "8-12 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming,
classification, data mining, ensemble",
notes = "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",
}
@Article{journals/jsw/FolinoFS06,
author = "Gianluigi Folino and Agostino Forestiero and
Giandomenico Spezzano",
title = "A Jxta Based Asynchronous Peer-to-Peer Implementation
of Genetic Programming",
journal = "Journal of Software",
year = "2006",
volume = "1",
number = "2",
pages = "12--23",
month = aug,
keywords = "genetic algorithms, genetic programming",
ISSN = "1796-217X",
URL = "http://www.academypublisher.com/jsw/vol01/no02/jsw01021223.pdf",
URL = "http://www.academypublisher.com/jsw/vol01/no02/jsw01021223.html",
size = "12 pages",
abstract = "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.",
notes = "JSW",
bibdate = "2008-08-13",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/jsw/jsw1.html#FolinoFS06",
}
@InProceedings{eurogp07:folino,
author = "Gianluigi Folino and Clara Pizzuti and Giandomenico
Spezzano",
title = "Mining Distributed Evolving Data Streams using Fractal
{GP} Ensembles",
editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and
Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar",
booktitle = "Proceedings of the 10th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "4445",
year = "2007",
address = "Valencia, Spain",
month = "11-13 " # apr,
pages = "160--169",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-71602-5",
isbn13 = "978-3-540-71602-0",
doi = "doi:10.1007/978-3-540-71605-1_15",
abstract = "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.",
notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in
conjunction with EvoCOP2007, EvoBIO2007 and
EvoWorkshops2007",
}
@InProceedings{1277301,
author = "Gianluigi Folino and Clara Pizzuti and Giandomenico
Spezzano",
title = "Stream{GP}: tracking evolving {GP} ensembles in
distributed data streams using fractal dimension",
booktitle = "GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation",
year = "2007",
editor = "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",
volume = "2",
isbn13 = "978-1-59593-697-4",
pages = "1751--1751",
address = "London",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1751.pdf",
doi = "doi:10.1145/1276958.1277301",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
month = "7-11 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming: Poster, data
mining, distributed streaming data, ensemble",
abstract = "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.",
notes = "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",
}
@Article{Folino:2008:TEC,
author = "Gianluigi Folino and Clara Pizzuti and Giandomenico
Spezzano",
title = "Training Distributed {GP} Ensemble With a Selective
Algorithm Based on Clustering and Pruning for Pattern
Classification",
journal = "IEEE Transactions on Evolutionary Computation",
year = "2008",
month = aug,
volume = "12",
number = "4",
pages = "458--468",
keywords = "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",
doi = "doi:10.1109/TEVC.2007.906658",
ISSN = "1089-778X",
abstract = "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.",
notes = "Also known as \cite{4439200}",
}
@InProceedings{Folino:2010:EuroGP,
author = "Gianluigi Folino and Giuseppe Papuzzo",
title = "Handling Different Categories of Concept Drifts in
Data Streams using Distributed {GP}",
booktitle = "Proceedings of the 13th European Conference on Genetic
Programming, EuroGP 2010",
year = "2010",
editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara
Silva and Stephen Dignum and A. Sima Uyar",
volume = "6021",
series = "LNCS",
pages = "74--85",
address = "Istanbul",
month = "7-9 " # apr,
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-12147-0",
doi = "doi:10.1007/978-3-642-12148-7_7",
abstract = "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.",
notes = "BoostCGPC, cellular GP, island model, AdaBoost,
Fractal dimension FD3, cloud computing, Minku Part of
\cite{Esparcia-Alcazar:2010:GP} EuroGP'2010 held in
conjunction with EvoCOP2010 EvoBIO2010 and
EvoApplications2010",
}
@Article{Folino:2010:GPEM,
author = "Gianluigi Folino and Clara Pizzuti and Giandomenico
Spezzano",
title = "An ensemble-based evolutionary framework for coping
with distributed intrusion detection",
journal = "Genetic Programming and Evolvable Machines",
year = "2010",
volume = "11",
number = "2",
pages = "131--146",
month = jun,
note = "Special issue on parallel and distributed evolutionary
algorithms, part II",
keywords = "genetic algorithms, genetic programming, Intrusion
detection, Ensemble classifiers, Distributed
evolutionary algorithms",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-010-9101-6",
size = "16 pages",
abstract = "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.",
}
@InProceedings{FonluptPPSN2000,
author = "Cyril W. B. Fonlupt and Denis Robilliard",
title = "Genetic Programming with Dynamic Fitness for a Remote
Sensing Application",
booktitle = "Parallel Problem Solving from Nature - PPSN VI 6th
International Conference",
editor = "Marc Schoenauer and Kalyanmoy Deb and G{\"u}nter
Rudolph and Xin Yao and Evelyne Lutton and Juan Julian
Merelo and Hans-Paul Schwefel",
year = "2000",
publisher = "Springer Verlag",
address = "Paris, France",
month = "16-20 " # sep,
volume = "1917",
series = "LNCS",
pages = "191--200",
keywords = "genetic algorithms, genetic programming",
URL = "http://www-lil.univ-littoral.fr/~robillia/Publis/lil-00-2.ps.gz",
}
@Article{fonlupt:2001:ASC,
author = "C. Fonlupt",
title = "Solving the ocean color problem using a genetic
programming approach",
journal = "Applied Soft Computing",
year = "2001",
volume = "1",
number = "1",
pages = "63--72",
month = jun,
keywords = "genetic algorithms, genetic programming, Ocean colour
problem, Phytoplankton",
URL = "http://www.sciencedirect.com/science/article/B6W86-43S6W98-6/2/ed66cf73aec7cb186639405e4a8801bb",
doi = "doi:10.1016/S1568-4946(01)00007-2",
abstract = "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.",
}
@Article{fonlupt:2005:GPEM,
author = "Cyril Fonlupt",
title = "Book Review: Genetic Programming {IV}: Routine
Human-Competitive Machine Intelligence",
journal = "Genetic Programming and Evolvable Machines",
year = "2005",
volume = "6",
number = "2",
pages = "231--233",
month = jun,
keywords = "genetic algorithms, genetic programming",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-005-7579-0",
size = "3 pages",
notes = "
Review of \cite{koza:gp4} ISBN 1-4020-7446-8 Book
authors John R. Koza, Martin A. Keane, Matthew J.
Streeter, William Mydlowec, Jessen Yu, Guido Lanza",
}
@InProceedings{fonlupt:2011:EuroGP,
author = "Cyril Fonlupt and Denis Robilliard",
title = "A Continuous Approach to Genetic Programming",
booktitle = "Proceedings of the 14th European Conference on Genetic
Programming, EuroGP 2011",
year = "2011",
month = "27-29 " # apr,
editor = "Sara Silva and James A. Foster and Miguel Nicolau and
Mario Giacobini and Penousal Machado",
series = "LNCS",
volume = "6621",
publisher = "Springer Verlag",
address = "Turin, Italy",
pages = "335--346",
organisation = "EvoStar",
keywords = "genetic algorithms, genetic programming: poster",
isbn13 = "978-3-642-20406-7",
doi = "doi:10.1007/978-3-642-20407-4_29",
size = "12 pages",
abstract = "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.",
notes = "refs \cite{ICSI-TR-95-012},
\cite{Veenhuis:2009:eurogp},
\cite{DBLP:conf/icai/ONeillB06}, \cite{langdon:book}
\cite{langdon:1998:antspace}
Part of \cite{Silva:2011:GP} EuroGP'2011 held in
conjunction with EvoCOP2011 EvoBIO2011 and
EvoApplications2011",
}
@InProceedings{Font:2010:cec,
author = "Jose M. Font and Daniel Manrique",
title = "Grammar-guided evolutionary automatic system for
autonomously building biological oscillators",
booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-6910-9",
abstract = "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.",
doi = "doi:10.1109/CEC.2010.5586377",
notes = "WCCI 2010. Also known as \cite{5586377}",
}
@Article{Font20107711,
author = "Jose M. Font and Daniel Manrique and Juan Rios",
title = "Evolutionary construction and adaptation of
intelligent systems",
journal = "Expert Systems with Applications",
volume = "37",
number = "12",
pages = "7711--7720",
year = "2010",
ISSN = "0957-4174",
doi = "doi:10.1016/j.eswa.2010.04.070",
URL = "http://www.sciencedirect.com/science/article/B6V03-501FPHF-C/2/9a2d947791e5706c203b3fed536a0e36",
keywords = "genetic algorithms, genetic programming, Evolutionary
computation, Intelligent systems, Rule-based systems,
Fuzzy rule-based systems, Artificial neural networks,
Medical prognosis",
abstract = "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.",
}
@InProceedings{Font:2011:IWINAC,
author = "Jose Font and Daniel Manrique and Eduardo Pascua",
title = "Grammar-Guided Evolutionary Construction of Bayesian
Networks",
booktitle = "Proceedings of the 4th International Work-Conference
on the Interplay Between Natural and Artificial
Computation, IWINAC 2011, Part I",
year = "2011",
editor = "Jose Manuel Ferrandez and Jose Ramon {Alvarez Sanchez}
and Felix {de la Paz} and F. Javier Toledo",
series = "Lecture Notes in Computer Science",
pages = "60--69",
volume = "6686",
address = "La Palma, Canary Islands, Spain",
month = may # " 30-" # jun # " 3",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-21343-4",
doi = "doi:10.1007/978-3-642-21344-1_7",
abstract = "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.",
affiliation = "Departamento de Inteligencia Artificial, Universidad
Politecnica de Madrid. Campus de Montegancedo, 28660
Boadilla del Monte, Spain",
}
@InProceedings{Font:evoapps12,
author = "Jose M. Font",
title = "Evolving Third-Person Shooter Enemies to Optimize
Player Satisfaction in Real-Time",
booktitle = "Applications of Evolutionary Computing,
EvoApplications 2012: EvoCOMNET, EvoCOMPLEX, EvoFIN,
EvoGAMES, EvoHOT, EvoIASP, EvoNUM, EvoPAR, EvoRISK,
EvoSTIM, EvoSTOC",
year = "2011",
month = "11-13 " # apr,
editor = "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",
series = "LNCS",
volume = "7248",
publisher = "Springer Verlag",
address = "Malaga, Spain",
publisher_address = "Berlin",
pages = "204--213",
organisation = "EvoStar",
keywords = "genetic algorithms, genetic programming, Evolutionary
computation, fuzzy rule based system, grammar-guided
genetic programming, player satisfaction",
isbn13 = "978-3-642-29177-7",
doi = "doi:10.1007/978-3-642-29178-4_21",
abstract = "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.",
notes = "EvoGames Part of \cite{DiChio:2012:EvoApps}
EvoApplications2012 held in conjunction with
EuroGP2012, EvoCOP2012, EvoBio'2012 and EvoMusArt2012",
}
@InProceedings{1068307,
author = "Nate Foreman and Matthew Evett",
title = "Preventing overfitting in {GP} with canary functions",
booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on
Genetic and evolutionary computation",
year = "2005",
editor = "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",
volume = "2",
ISBN = "1-59593-010-8",
pages = "1779--1780",
address = "Washington DC, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1779.pdf",
doi = "doi:10.1145/1068009.1068307",
publisher = "ACM Press",
publisher_address = "New York, NY, 10286-1405, USA",
month = "25-29 " # jun,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, Poster,
experimentation, overfitting, performance",
notes = "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",
}
@InProceedings{DBLP:conf/gecco/ForrestNWG09,
author = "Stephanie Forrest and ThanhVu Nguyen and Westley
Weimer and Claire {Le Goues}",
title = "A genetic programming approach to automated software
repair",
booktitle = "GECCO '09: Proceedings of the 11th Annual conference
on Genetic and evolutionary computation",
year = "2009",
editor = "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",
pages = "947--954",
address = "Montreal",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "8-12 " # jul,
organisation = "SigEvo",
note = "Best paper",
keywords = "genetic algorithms, genetic programming, Software
Engineering, Testing and Debugging, Programming
Languages, Syntax, Algorithms, Software repair,
software engineering",
isbn13 = "978-1-60558-325-9",
bibsource = "DBLP, http://dblp.uni-trier.de",
bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
doi = "doi:10.1145/1569901.1570031",
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.147.7651",
URL = "http://www.cs.virginia.edu/~weimer/p/weimer-gecco2009.pdf",
language = "en",
oai = "oai:CiteSeerXPSU:10.1.1.147.7651",
abstract = "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.",
notes = "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.",
}
@InProceedings{Forrest:2010:SPLASH,
author = "Stephanie Forrest",
title = "The Case for Evolvable Software",
booktitle = "ACM International Conference on Systems, Programming,
Languages, and Applications: Software for Humanity
(SPLASH)",
year = "2010",
pages = "1",
address = "Reno, USA",
month = "17-21 " # oct,
publisher = "ACM",
note = "Keynote",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4503-0203-6",
URL = "http://portal.acm.org/ft_gateway.cfm?id=1869539&type=pdf&CFID=114019259&CFTOKEN=22192943",
doi = "doi:10.1145/1869459.1869539",
size = "1 pages",
abstract = "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.",
notes = "Abstract only",
}
@InProceedings{Forster:2009:ISSNIP,
author = "Kilian Forster and Pascal Brem and Daniel Roggen and
Gerhard Troster",
title = "Evolving discriminative features robust to sensor
displacement for activity recognition in body area
sensor networks",
booktitle = "5th International Conference on Intelligent Sensors,
Sensor Networks and Information Processing, ISSNIP
2009",
year = "2009",
month = dec,
pages = "43--48",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1109/ISSNIP.2009.5416810",
abstract = "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.",
notes = "Also known as \cite{5416810}",
}
@Article{kybernetes:forsyth,
author = "Richard Forsyth",
title = "{BEAGLE} {A} {Darwinian} Approach to Pattern
Recognition",
journal = "Kybernetes",
year = "1981",
volume = "10",
number = "3",
pages = "159--166",
keywords = "genetic algorithms, genetic programming, soccer foot
ball pools",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/kybernetes_forsyth.pdf",
doi = "doi:10.1108/eb005587",
size = "8 pages",
ISSN = "0368-492X",
abstract = "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.",
notes = "Copy from British Library May 1994",
}
@Book{Forsyth:1986:mlESir,
author = "Richard Forsyth and Roy Rada",
title = "Machine Learning applications in Expert Systems and
Information Retrieval",
publisher = "Ellis Horwood",
year = "1986",
series = "Ellis Horwood series in artificial intelligence",
address = "Chichester, UK",
ISBN = "0-7458-0045-9",
URL = "http://www.amazon.co.uk/Machine-Learning-Applications-Information-Retrieval/dp/0745800459",
keywords = "genetic algorithms, genetic programming",
notes = "Chapters on BEAGLE",
size = "275 pages",
}
@InCollection{forsyth:1989:ei,
author = "Richard Forsyth",
title = "The evolution of intelligence",
booktitle = "Machine Learning, Priciples and Techniques",
publisher = "Chapman and Hall",
year = "1989",
editor = "Richard Forsyth",
chapter = "4",
pages = "65--82",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-412-30570-4",
notes = "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].
",
}
@InProceedings{Foster:2010:gecco,
author = "Blair Foster and Anil Somayaji",
title = "Object-level recombination of commodity applications",
booktitle = "GECCO '10: Proceedings of the 12th annual conference
on Genetic and evolutionary computation",
year = "2010",
editor = "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",
isbn13 = "978-1-4503-0072-8",
pages = "957--964",
keywords = "genetic algorithms, genetic programming, SBSE,
software recombination, ObjRecombGA, object-level
recombination, commodity programs",
month = "7-11 " # jul,
organisation = "SIGEVO",
address = "Portland, Oregon, USA",
URL = "people.scs.carleton.ca/~soma/pubs/bfoster-gecco-2010.pdf",
doi = "doi:10.1145/1830483.1830653",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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",
notes = "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 \cite{1830653} GECCO-2010 A joint meeting
of the nineteenth international conference on genetic
algorithms (ICGA-2010) and the fifteenth annual genetic
programming conference (GP-2010)",
}
@Unpublished{foster:1997:ieGPb,
author = "James A. Foster and Terence Soule",
title = "Comments on the intron/exon distinction as it relates
to genetic programming and biology",
note = "Position paper at the Workshop on Exploring Non-coding
Segments and Genetics-based Encodings at ICGA-97",
month = "21 " # jul,
year = "1997",
address = "East Lansing, MI, USA",
keywords = "genetic algorithms, genetic programming, introns",
notes = "http://www.aic.nrl.navy.mil/~aswu/icga97/",
size = "3 pages",
}
@Article{foster:2001:discipulus,
author = "James A. Foster",
title = "Review: Discipulus: {A} Commercial Genetic Programming
System",
journal = "Genetic Programming and Evolvable Machines",
year = "2001",
volume = "2",
number = "2",
pages = "201--203",
month = jun,
keywords = "genetic algorithms, genetic programming",
ISSN = "1389-2576",
doi = "doi:10.1023/A:1011516717456",
notes = "Article ID: 335720",
}
@Article{foster:2005:GPEM,
author = "James A. Foster and Erick Cantu-Paz",
title = "Introduction",
journal = "Genetic Programming and Evolvable Machines",
year = "2005",
volume = "6",
number = "1",
pages = "5--6",
month = mar,
keywords = "genetic algorithms, genetic programming, evolvable
hardware",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-005-7616-z",
notes = "Special issue best of GECCO-2003
\cite{GECCO2003-PartI}, \cite{GECCO2003-PartII}
",
}
@Article{foster:2006:sigevo,
author = "James A. Foster and Jason H. Moore",
title = "{GECCO}-2006 Highlights: Biological Applications",
journal = "SIGEVOlution",
year = "2006",
volume = "1",
number = "3",
pages = "23",
month = sep,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.sigevolution.org/2006/03/issue.pdf",
size = "0.5 pages",
}
@InProceedings{conf/evoW/FradeVC08,
title = "Modelling Video Games' Landscapes by Means of Genetic
Terrain Programming - {A} New Approach for Improving
Users' Experience",
author = "Miguel Frade and F. {Fernandez de Vega} and Carlos
Cotta",
bibdate = "2008-04-15",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/evoW/evoW2008.html#FradeVC08",
booktitle = "Proceedings of Evo{COMNET}, Evo{FIN}, Evo{HOT},
Evo{IASP}, Evo{MUSART}, Evo{NUM}, Evo{STOC}, and
EvoTransLog, Applications of Evolutionary Computing,
EvoWorkshops",
publisher = "Springer",
year = "2008",
volume = "4974",
editor = "Mario Giacobini and Anthony Brabazon and Stefano
Cagnoni and Gianni {Di Caro} and Rolf Drechsler and
Anik{\'o} Ek{\'a}rt and Anna Esparcia-Alc{\'a}zar 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",
isbn13 = "978-3-540-78760-0",
pages = "485--490",
series = "Lecture Notes in Computer Science",
doi = "doi:10.1007/978-3-540-78761-7_52",
address = "Naples",
month = "26-28 " # mar,
keywords = "genetic algorithms, genetic programming, terrain
generation, video games, evolutionary art",
abstract = "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).",
notes = "GPLAB Matlab, FFT",
}
@Article{Frade:2009:IJCGT,
author = "Miguel Frade and Francisco {Fernandez de Vega} and
Carlos Cotta",
title = "Breeding Terrains with Genetic Terrain Programming:
The Evolution of Terrain Generators",
journal = "International Journal of Computer Games Technology",
year = "2009",
volume = "2009",
note = "Special issue on Artificial Intelligence for Computer
Games",
keywords = "genetic algorithms, genetic programming, Genetic
terrain programming, evolutionary systems, terrain
generator, level of detail",
ISSN = "1687-7047",
URL = "http://downloads.hindawi.com/journals/ijcgt/2009/125714.pdf",
URL = "http://www.hindawi.com/GetArticle.aspx?doi=10.1155/2009/125714",
doi = "doi:10.1155/2009/125714",
size = "13 pages",
abstract = "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.",
notes = "Article ID 125714",
}
@InProceedings{Frade:2010:EvoGAMES,
author = "Miguel Frade and Francisco {Fernandez de Vega} and
Carlos Cotta",
title = "Evolution of Artificial Terrains for Video Games Based
on Accessibility",
booktitle = "EvoGAMES",
year = "2010",
editor = "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",
volume = "6024",
series = "LNCS",
pages = "90--99",
address = "Istanbul",
month = "7-9 " # apr,
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, genetic
terrain programming, artificial terrains, video games",
isbn13 = "978-3-642-12238-5",
doi = "doi:10.1007/978-3-642-12239-2_10",
abstract = "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.",
notes = "EvoGAMES'2010 held in conjunction with EuroGP'2010
EvoCOP2010 EvoBIO2010",
}
@InProceedings{Frade:2010:cec,
author = "Miguel Frade and F. {Fernandez de Vega} and Carlos
Cotta",
title = "Evolution of artificial terrains for video games based
on obstacles edge length",
booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-6910-9",
abstract = "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.",
doi = "doi:10.1109/CEC.2010.5586032",
notes = "WCCI 2010. Also known as \cite{5586032}",
}
@InProceedings{Francisco:2008:geccocomp,
author = "Tiago Francisco and Gustavo Miguel Jorge {dos Reis}",
title = "Evolving combat algorithms to control space ships in a
2{D} space simulation game with co-evolution using
genetic programming and decision trees",
year = "2008",
editor = "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",
isbn13 = "978-1-60558-131-6",
booktitle = "GECCO-2008 Workshop: Defense Applications of
Computational Intelligence (DAC)",
pages = "1887--1892",
address = "Atlanta, GA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1887.pdf",
doi = "doi:10.1145/1388969.1388995",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "12-16 " # jul,
keywords = "genetic algorithms, genetic programming",
notes = "Distributed on CD-ROM at GECCO-2008
ACM Order Number 910081. Also known as \cite{1388995}",
}
@InProceedings{Francisco2:2008:geccocomp,
author = "Tiago Francisco and Gustavo Miguel Jorge {dos Reis}",
title = "Evolving predator and prey behaviours with
co-evolution using genetic programming and decision
trees",
year = "2008",
editor = "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",
isbn13 = "978-1-60558-131-6",
booktitle = "GECCO-2008 Workshop: Defense Applications of
Computational Intelligence (DAC)",
pages = "1893--1900",
address = "Atlanta, GA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1893.pdf",
doi = "doi:10.1145/1388969.1388996",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "12-16 " # jul,
keywords = "genetic algorithms, genetic programming",
notes = "Distributed on CD-ROM at GECCO-2008
ACM Order Number 910081. Also known as \cite{1388996}",
}
@InProceedings{francone:1996:bench,
author = "Frank D. Francone and Peter Nordin and Wolfgang
Banzhaf",
title = "Benchmarking the Generalization Capabilities of a
Compiling Genetic programming System using Sparse Data
Sets",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "72--80",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
URL = "http://www.cs.mun.ca/~banzhaf/papers/benchmarking.pdf",
size = "9 pages",
notes = "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
",
}
@InProceedings{banzhaf:1996:mutatation,
author = "Wolfgang Banzhaf and Frank D. Francone and Peter
Nordin",
title = "The Effect of Extensive Use of the Mutation Operator
on Generalization in Genetic Programming Using Sparse
Data Sets",
booktitle = "Parallel Problem Solving from Nature IV, Proceedings
of the International Conference on Evolutionary
Computation",
year = "1996",
editor = "Hans-Michael Voigt and Werner Ebeling and Ingo
Rechenberg and Hans-Paul Schwefel",
series = "LNCS",
volume = "1141",
pages = "300--309",
address = "Berlin, Germany",
publisher_address = "Heidelberg, Germany",
month = "22-26 " # sep,
publisher = "Springer Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-61723-X",
doi = "doi:10.1007/3-540-61723-X_994",
size = "10 pages",
abstract = "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.",
notes = "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",
affiliation = "Dortmund University Department of Computer Science
Joseph-vonFraunhofer-Str. 20 44227 Dortmund Germany",
}
@Unpublished{banzhaf:1997:emvsea,
author = "Wolfgang Banzhaf and Frank D. Francone and Peter
Nordin",
title = "Some Emergent Properties of Variable Size {EA}s",
note = "Position paper at the Workshop on Evolutionary
Computation with Variable Size Representation at
ICGA-97",
month = "20 " # jul,
year = "1997",
address = "East Lansing, MI, USA",
keywords = "genetic algorithms, genetic programming, bloat,
variable size representation",
size = "4 pages",
}
@Unpublished{banzhaf:1997:wiGPge,
author = "Wolfgang Banzhaf and Peter Nordin and Frank D.
Francone",
title = "Why introns in genetic programming grow
exponentially",
note = "Position paper at the Workshop on Exploring Non-coding
Segments and Genetics-based Encodings at ICGA-97",
month = "21 " # jul,
year = "1997",
address = "East Lansing, MI, USA",
keywords = "genetic algorithms, genetic programming, introns",
notes = "http://www.aic.nrl.navy.mil/~aswu/icga97/",
size = "3 pages",
}
@InProceedings{francone:1999:HCGP,
author = "Frank D. Francone and Markus Conrads and Wolfgang
Banzhaf and Peter Nordin",
title = "Homologous Crossover in Genetic Programming",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1021--1026",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-463.pdf",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@Misc{Francone:2000:lrtads,
author = "Frank D. Francone and Peter Nordin and Wolfgang
Banzhaf and Larry M. Deschaine",
title = "Automatic Induction of Machine Code ({AIM}) Learning
Real Time Adaptive Control Strategies",
howpublished = "www document",
year = "2000",
month = "11 " # may,
keywords = "genetic algorithms, genetic programming, discipulus
automatic control, industrial control, model design,
machine learning",
URL = "http://pw2.netcom.com/%7elmdmit84/AimProcessControl2000.pdf
broken",
size = "4 pages",
notes = "high level",
}
@Manual{francone:manual,
title = "Discipulus Owner's Manual",
author = "Frank D. Francone",
year = "2001",
address = "11757 W. Ken Caryl Avenue F, PBM 512, Littleton,
Colorado, 80127-3719, USA",
edition = "Version 3.0 DRAFT",
organisation = "Register Machine Learning Technologies, Inc.",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.aimlearning.com/Discipulus%20Owners%20Manual.pdf",
size = "210",
}
@Article{francone:ebdo,
author = "Frank D. Francone and Larry M. Deschaine",
title = "Extending the boundaries of design optimization by
integrating fast optimization techniques with
machine-code-based, linear genetic programming",
journal = "Information Sciences",
volume = "161",
number = "3-4",
month = "20 " # apr,
year = "2004",
pages = "99--120",
note = "FEA 2002",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1016/j.ins.2003.05.006",
abstract = "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.",
}
@InProceedings{ASTC_2004_Getting_It_Right_from_the_Very_Start,
author = "Frank D. Francone and Larry M. Deschaine",
title = "Getting It Right at the Very Start -- Building Project
Models where Data Is Expensive by Combining Human
Expertise, Machine Learning and Information Theory",
booktitle = "2004 Business and Industry Symposium",
year = "2004",
address = "Washington, DC",
month = apr,
organisation = "Society for Modeling and Simulation",
keywords = "genetic algorithms, genetic programming, Environmental
Science, geophysics, information theory, underground
anomaly detection, machine learning, expert systems",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/deschaine/ASTC_2004_Getting_It_Right_from_the_Very_Start.pdf",
URL = "http://www.scs.org/docInfo.cfm?get=1720",
size = "7 pages",
abstract = "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.",
notes = "
",
}
@InProceedings{francone:2004:lbp,
author = "Frank D. Francone and Larry M. Deschaine and Tom
Battenhouse and Jeffrey J. Warren",
title = "Discrimination of Unexploded Ordnance from Clutter
Using Linear Genetic Programming",
booktitle = "Late Breaking Papers at the 2004 Genetic and
Evolutionary Computation Conference",
year = "2004",
editor = "Maarten Keijzer",
address = "Seattle, Washington, USA",
month = "26 " # jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/LBP022.pdf",
URL = "http://www.aimlearning.com/UXO.GECCO.2004.pdf",
abstract = "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.",
notes = "Part of \cite{keijzer:2004:GECCO:lbp}. See also
\cite{francone:2005:GPTP}.",
}
@InCollection{francone:2005:GPTP,
author = "Frank D. Francone and Larry M. Deschaine and Tom
Battenhouse and Jeffrey J. Warren",
title = "Discrimination of Unexploded Ordnance from Clutter
using Linear Genetic Programming",
booktitle = "Genetic Programming Theory and Practice {III}",
year = "2005",
editor = "Tina Yu and Rick L. Riolo and Bill Worzel",
volume = "9",
series = "Genetic Programming",
chapter = "4",
pages = "49--64",
address = "Ann Arbor",
month = "12-14 " # may,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, Unexploded
Ordnance, UXO Discrimination.",
ISBN = "0-387-28110-X",
size = "16 pages",
abstract = "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.",
notes = "part of \cite{yu:2005:GPTP} Published Jan 2006 after
the workshop",
}
@InProceedings{1277353,
author = "Frank D. Francone and Larry M. Deschaine and Jeffrey
J. Warren",
title = "Discrimination of munitions and explosives of concern
at {F}.{E}. Warren {AFB} using linear genetic
programming",
booktitle = "GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation",
year = "2007",
editor = "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",
volume = "2",
isbn13 = "978-1-59593-697-4",
pages = "1999--2006",
address = "London",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1999.pdf",
doi = "doi:10.1145/1276958.1277353",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
month = "7-11 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "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",
abstract = "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.",
notes = "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",
}
@MastersThesis{FrankKlahold:masters,
author = "Steffen Frank and Stefan Klahold",
title = "Ein System zur Untersuchung der Moglichkeiten und
Beschrankungen fur Genetisches Programmieren in {JAVA}
Bytecode",
school = "Dortmund University",
year = "1998",
address = "Germany",
month = May,
keywords = "genetic algorithms, genetic programming",
notes = "http://ls11-www.cs.uni-dortmund.de/bb/review98-99/node66.html
Joint project supervised by Robert Keller. See also
\cite{klahold:1998:eprGPJb}",
}
@InProceedings{Frankola:2008:ITI,
author = "Toni Frankola and Marin Golub and Domagoj Jakobovic",
title = "Evolutionary algorithms for the resource constrained
scheduling problem",
booktitle = "30th International Conference on Information
Technology Interfaces, ITI 2008",
year = "2008",
month = jun,
pages = "715--722",
keywords = "genetic algorithms, genetic programming, NP complete
problems, evolutionary algorithms, optimal sequence
finding, resource constrained project scheduling
problem, constraint theory, project management,
resource allocation, scheduling",
doi = "doi:10.1109/ITI.2008.4588499",
ISSN = "1330-1012",
abstract = "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.",
notes = "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 \cite{4588499}",
}
@Article{Fraser1989177,
author = "A. F. Fraser",
title = "Animal welfare theory: The keyboard of the maintenance
ethosystem",
journal = "Applied Animal Behaviour Science",
volume = "22",
number = "2",
pages = "177--190",
year = "1989",
ISSN = "0168-1591",
doi = "doi:10.1016/0168-1591(89)90053-1",
URL = "http://www.sciencedirect.com/science/article/B6T48-49NRPH9-GK/2/ff144de289e78408a13991fc32da018c",
notes = "Not on GP",
}
@InProceedings{Fraser:1994:inkbiro,
author = "A. P. Fraser and J. R. Rush",
title = "Putting {INK} into a {BIR}o: {A} discussion of problem
domain knowledge for evolutionary robotics",
booktitle = "AISB Workshop on Evolutionary Computing",
year = "1994",
editor = "T. C. Fogarty",
address = "Leeds, UK",
month = "11-13 " # apr,
organisation = "AISB",
keywords = "genetic algorithms, genetic programming",
notes = "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
",
}
@InProceedings{frayn:2005:JCIS,
author = "Colin Frayn",
title = "Genetic Programming in Finance",
booktitle = "Proceedings of the 8th Joint Conference in Information
Systems (JCIS 2005)",
year = "2005",
editor = "Heng-Da Cheng",
address = "Salt Lake City, USA",
month = "21-25 " # jul,
keywords = "genetic algorithms, genetic programming",
notes = "http://www.jcis.org/jcis_program/master_schedule.pdf",
}
@Article{freeland:2002:GPEM,
author = "Stephen J. Freeland",
title = "The Darwinian Genetic Code: An Adaptation for
Adapting?",
journal = "Genetic Programming and Evolvable Machines",
year = "2002",
volume = "3",
number = "2",
pages = "113--127",
month = jun,
keywords = "error minimization, genetic code, evolution,
adaptation",
ISSN = "1389-2576",
doi = "doi:10.1023/A:1015527808424",
abstract = "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.",
notes = "Special issue on Gene Expression
\cite{Kargupta:2002:GPEM} Article ID: 408585",
}
@InCollection{Freeland:2003,
author = "Stephen Freeland",
title = "Three Fundamentals of the Biological Genetic
Algorithm",
booktitle = "Genetic Programming Theory and Practice",
publisher = "Kluwer",
year = "2003",
editor = "Rick L. Riolo and Bill Worzel",
pages = "303--312",
chapter = "19",
keywords = "particulate genes, genetic code, phenotype, genotype,
biology envy",
notes = "Part of \cite{RioloWorzel:2003}",
}
@InProceedings{freeman:1998:lrGPcfg,
author = "Jennifer J. Freeman",
title = "A Linear Representation for {GP} using Context Free
Grammars",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "72--77",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, CFG/GP,
PORS",
ISBN = "1-55860-548-7",
notes = "GP-98",
}
@InProceedings{Freischlad:2005:ICCCE,
author = "M. Freischlad and M. Schnellenbach-Held",
title = "Multi-Objective Genetic Programming Based Design of
Fuzzy Systems",
booktitle = "Proceedings of the 2005 ASCE International Conference
on Computing in Civil Engineering",
year = "2005",
editor = "Lucio Soibelman and Feniosky Pena-Mora",
address = "Cancun, Mexico",
month = jul # " 12-15",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1061/40794(179)62",
abstract = "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.",
notes = "c2005 ASCE",
}
@InProceedings{Freitas:1997:GPf2dm,
author = "Alex A. Freitas",
title = "A Genetic Programming Framework for Two Data Mining
Tasks: Classification and Generalized Rule Induction",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming, SQL",
pages = "96--101",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
URL = "http://citeseer.nj.nec.com/43454.html",
URL = "http://kar.kent.ac.uk/21483/",
URL = "http://kar.kent.ac.uk/21483/2/A_Genetic_Programming_Framework_for_Two_Data_Mining_Tasks_Classification_and_Generalized_Rule_Induction.pdf",
size = "6 pages",
abstract = "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.",
notes = "GP-97
Lazy learning, separation of query tree encodes
Tuple-Set Descriptor (SQL), from goal attribute. Goal
subject to three types of mutation",
}
@InProceedings{freitas:1998:GAdkn,
author = "Alex A. Freitas",
title = "A Genetic Algorithm for Discovering Knowledge
Nuggets",
booktitle = "Late Breaking Papers at the Genetic Programming 1998
Conference",
year = "1998",
editor = "John R. Koza",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "Stanford, CA, USA",
month = "22-25 " # jul,
publisher = "Stanford University Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "GP-98LB",
}
@Article{freitas:2001:GPEM,
author = "Alex A. Freitas",
title = "Book Review: {Data} Mining Using Grammar-Based Genetic
Programming and Applications",
journal = "Genetic Programming and Evolvable Machines",
year = "2001",
volume = "2",
number = "2",
pages = "197--199",
month = jun,
keywords = "genetic algorithms, genetic programming, evolvable
hardware",
ISSN = "1389-2576",
URL = "http://ipsapp009.lwwonline.com/content/getfile/4723/5/7/fulltext.pdf",
doi = "doi:10.1023/A:1011564616547",
notes = "review of \cite{ManLeungWong:book} Article ID:
335718",
}
@Book{freitas:2002:book,
author = "Alex Freitas",
title = "Data Mining and Knowledge Discovery with Evolutionary
Algorithms",
publisher = "Springer-Verlag",
year = "2002",
keywords = "genetic algorithms, genetic programming, data mining,
classification, clustering",
ISBN = "0-7923-8048-7",
URL = "http://www.cs.kent.ac.uk/people/staff/aaf/my-publications-ukc.html",
}
@InCollection{Freitas:2002:SFSC,
author = "Alex Freitas",
title = "A review of evolutionary algorithms for e-commerce",
booktitle = "E-Commerce and Intelligent Methods. Studies in
Fuzziness and Soft Computing",
publisher = "Springer-Verlag",
year = "2002",
editor = "J. Segovia and P. S. Szczepaniak and M.
Niedzwiedzinski",
volume = "105",
series = "Studies in Fuzziness and Soft Computing",
chapter = "10",
pages = "159--179",
keywords = "genetic algorithms, genetic programming, e-commerce",
ISBN = "3-7908-1499-7",
URL = "http://www.cs.kent.ac.uk/people/staff/aaf/pub_papers.dir/EA-e-com.ps",
URL = "http://www.cs.kent.ac.uk/people/staff/aaf/my-publications-ukc.html",
size = "20 pages",
}
@InCollection{freitas:2002:HDMKD,
author = "Alex Alves Freitas",
title = "Evolutionary Computation",
booktitle = "Handbook of Data Mining and Knowledge Discovery",
publisher = "Oxford University Press",
year = "2002",
editor = "W. Klosgen and J. Zytkow",
chapter = "32",
pages = "698--706",
keywords = "genetic algorithms, genetic programming, data mining,
classification",
URL = "http://www.cs.kent.ac.uk/people/staff/aaf/my-publications-ukc.html",
URL = "http://www.cs.kent.ac.uk/people/staff/aaf/pub_papers.dir/Hbk-dmkd.ps",
URL = "http://citeseer.ist.psu.edu/460298.html",
abstract = "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.",
size = "pages",
}
@InCollection{Freitas:2002:AiEC,
author = "Alex Freitas",
title = "A survey of evolutionary algorithms for data mining
and knowledge discovery",
chapter = "33",
pages = "819--845",
publisher = "Springer-Verlag",
year = "2002",
keywords = "genetic algorithms, genetic programming",
booktitle = "Advances in Evolutionary Computation",
editor = "A. Ghosh and S. Tsutsui",
URL = "http://www.macs.hw.ac.uk/~dwcorne/Teaching/freitas01survey.pdf",
URL = "http://www.cs.kent.ac.uk/people/staff/aaf/my-publications-ukc.html",
abstract = "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.",
size = "27 pages",
}
@InCollection{reference/dataware/FreitasP09,
title = "Genetic Programming for Automatically Constructing
Data Mining Algorithms",
author = "Alex Alves Freitas and Gisele L. Pappa",
booktitle = "Encyclopedia of Data Warehousing and Mining",
publisher = "IGI Global",
year = "2009",
editor = "John Wang",
chapter = "144",
pages = "932--936",
edition = "2",
keywords = "genetic algorithms, genetic programming",
isbn13 = "9781605660103",
URL = "http://www.igi-global.com/bookstore/titledetails.aspx?titleid=346&detailstype=chapters",
doi = "doi:10.4018/978-1-60566-010-3",
notes = "4 Volumes.",
bibdate = "2011-01-18",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/reference/dataware/dataware2009.html#FreitasP09",
}
@InProceedings{french:2001:gecco,
title = "Evolving a Nervous System of Spiking Neurons for a
Behaving Robot",
author = "R. L. B. French and R. I. Damper",
pages = "1099--1106",
year = "2001",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2001)",
editor = "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",
address = "San Francisco, California, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "7-11 " # jul,
keywords = "genetic algorithms, genetic programming, evolutionary
robotics, spiking, neurons, emergent behaviours",
ISBN = "1-55860-774-9",
notes = "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 \cite{spector:2001:GECCO}",
}
@InProceedings{frey:2001:gecco,
title = "Evolving Strategies for Global Optimization - {A}
Finite State Machine Approach",
author = "Clemens Frey and Gunter Leugering",
pages = "27--33",
year = "2001",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2001)",
editor = "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",
address = "San Francisco, California, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "7-11 " # jul,
keywords = "genetic algorithms, genetic programming, finite state
machines, optimizing controllers, dynamic systems,
adapted spatial optimization strategies",
ISBN = "1-55860-774-9",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d01.pdf",
notes = "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 \cite{spector:2001:GECCO}",
}
@Article{frey:2002a,
author = "Clemens Frey",
title = "Co-Evolution of Finite State Machines for
Optimization: Promotion of Devices Which Search
Globally",
journal = "International Journal of Computational Intelligence
and Applications",
year = "2002",
volume = "1",
number = "2",
pages = "1--16",
keywords = "genetic algorithms, genetic programming",
ISSN = "1469-0268",
URL = "http://www.mathematik.tu-darmstadt.de/~frey/",
size = "16 p.",
abstract = "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.",
}
@PhdThesis{Frey:thesis,
author = "Clemens Frey",
title = "Virtual Ecosystems - Evolutionary and Genetic
Programming from the perspective of modern means of
ecosystem-modelling",
school = "Darmstadt University of Technology",
year = "2002",
keywords = "genetic algorithms, genetic programming",
size = "pages",
notes = "See Frey:2002",
}
@Book{frey:2002,
author = "Clemens Frey",
title = "Virtual Ecosystems - Evolutionary and Genetic
Programming from the perspective of modern means of
ecosystem-modelling",
publisher = "Institute for Terrestrial Ecosystems, Bayreuth",
year = "2002",
volume = "93",
series = "Bayreuth Forum Ecology",
address = "Bayreuth, Germany",
note = "(in German)",
email = "frey@mathematik.tu-darmstadt.de",
keywords = "genetic algorithms, genetic programming",
ISSN = "0944-4122",
URL = "http://www.bitoek.uni-bayreuth.de/bitoek/en/pub/pub/pub_detail.php?id_obj=7556",
size = "199 p.",
abstract = "The realm of Evolutionary Computation covers many
tools commonly used for solving complex discrete and
continuous global optimization problems. These methods,
which are known as Genetic Algorithms, Evolution
Strategies, Evolutionary Programming and Genetic
Programming, stem from efforts of modeling adaptive
systems, from engineering and computer science. They
are based on the idea of restating the Darwinian
principles of natural evolution in algorithmic terms in
order to get problem-solving methods for non-biological
applications. Today Genetic Algorithms, Evolution
Strategies and Evolutionary Programming mainly serve as
mathematical techniques of numerical optimization.
Genetic Programming likewise is an adaptation
technique, but there is a different focus: based on
evolutionary principles Genetic Programming enables us
to automatically generate computer programs.The
underlying hypotheses of this book is that the main
point of natural, biological evolution is its data
processing aspect. Evolution is seen as a certain way
of processing individuals' and populations' genetic
data. Referring to Evolutionary Computation there is a
very interesting question now: Is it appropriate to
employ Genetic Programming and similar algorithms in
order to investigate natural evolution? Of course this
means turning around the application profile of
Evolutionary Computation, so where do we have to alter
its algorithmic structure and the like? Finally,
supposed there is a modified method, how do the results
of both the classic algorithm and the modified variant
compare to each other?In the first chapter we state the
general notion of a search strategy. It may be a living
being's policy of resource allocation, for example, but
the notion covers optimization methods, too. A search
strategy shall be defined in mathematical terms as
being a dynamical system combined with a quality
measure which is based on the trajectories the
dynamical system produces. The author proposes a
precise formulation for what a search strategy is
generally claimed to accomplish, namely to generate
dynamic behavior which gets us to the neighborhood of a
predefined goal, possibly obeying certain constraints
within the dynamics of the search process.Chapter two
contains a gentle introduction into the field of
Evolutionary Computation, namely Adaptive Systems,
Genetic Algorithms, Evolution Strategies and
Evolutionary Programming. We focus on Genetic
Programming, however, and take a look at a paradigmatic
experiment for automatically finding search strategies,
i.e. the so-called artificial ant-experiment. In doing
so the reader is also shown how the mathematical
framework built in the first chapter may be used to
formulate the artificial ant-problem.",
abstract = "The following chapter addresses the issue of
artificially creating evolution in virtual or simulated
ecosystems and the question whether this can be done
with the help of Evolutionary Computation. Since we
want to analyse shortcomings of the conventional
approaches and necessary adjustments, basic features of
natural evolution are stated and discussed at first.
Then we take a closer look at the area of Artificial
Life and discuss specific software from this field.
This discussion is concerned with so-called strong
approaches like tierra and avida as well as weak
approaches like the ecosystem-oriented Tragic++ system;
besides, connections to social learning paradigms and
Nouveau Artificial Intelligence are highlighted. Taking
this broad view into account we conclude this chapter
by listing a set of features which have to be comprised
by a serious a model for evolution in virtual
ecosystems. The gist of these desired features says
that it is feasible to represent strategy programs as
trees like in Genetic Programming, for this kind of
representation causes a non-trivial, morphogenic
mapping between the genotypic and the phenotypic space.
It has to be conceded, however, that exogenously and
a-priori given fitness-functions as well as the
synchronous reproduction schemes which are almost
always used in Genetic Programming are not appropriate
for modeling evolution in virtual ecosystems. Chapters
four to six describe how a system called MathEvEco was
formulated and implemented according to these
guidelines. Chapter four focuses on strongly typed tree
representations of programs. Feasible sets of strongly
typed program trees are defined precisely and their
relationship with context-free grammars and the
parameter-dependent evaluation of program trees are
investigated in mathematical terms. These mathematical
tools having been made available, genetic operators and
initialization procedures of MathEvEco are stringently
formulated in the fifth chapter. The system was
supposed to be as flexible as possible. To this end the
author has not only accessed a strongly typed version
of the very classic crossover operator, but included a
bunch of strongly typed mutation operators and the
novel PTC2 algorithm for randomly generating program
trees. In order to allow algorithmic comparisons the
operators may be assembled in two fundamentally
different ways; they may either be merged into a system
of common Genetic Programming or they may be assembled
as the desired system for modeling evolution in virtual
ecosystems. Both of these possibilities are described,
still in mathematical terms.The resulting systems are
called MathEvEco-GP and MathEvEco-AL, respectively.",
abstract = "While chapter five has been written in order to allow
these systems to be communicated in a transparent and
precise manner, chapter six shall illuminate their
actual implementation within the scope of the
mathematical software system Mathematica. To this end
we show how program trees are handled in Mathematica,
how model-specific and problem-specific knowledge is to
be inserted by the user of MathEvEco, and in which way
the various genetic operators have been implemented.
Since MathEvEco can not only be run on a single machine
but rather on clusters of workstations, there is a
special treatment of aspects of parallel programming,
too. Finally the functionality of MathEvEco is
exemplified by means of a symbolic regression
problem.The final chapter seven is dedicated to a case
study. It consists of automatically generating search
devices which is a special case of the general setting
having been introduced in chapter one. There are a two
different interpretations of this special problem. On
the one hand side it may be understood in terms of
numerical optimization; we presuppose an multi-modal
objective function which may be imagined as a
three-dimensional surface having many peaks. Strategies
have to be evolved by MathEvEco-GP which are only
provided with local information about this surface but
are nevertheless required to lead the search devices to
one of the highest peaks. On the other hand side the
special problem may be understood in terms of an
ecosystem where many organisms struggle for allocating
a resource. It is quite important to realize that in
this case there is a natural component of interaction
since individual organisms consume resources from their
immediate neighborhood and thus affect organisms there,
too. For this kind of ecosystem simulation we have used
the MathEvEco-AL evolution variant which provides
implicit fitness assessment and asynchronous
reproduction of 'living` organisms, i.e. devices.In
both cases program trees represent strategies for
potentially interacting devices. From a computer
science point of view each of these devices is made up
of a finite state machine and an input filter which
maps continuous input from various channels into a
finite set of symbols. The finite state machine's
output iteratively controls the search device during a
predefined maximum number of steps. The results of our
various experiments with MathEvEco-GP show that if
interaction and thus parallel search are introduced, it
is much more likely that global optima, i.e. the
highest peaks of the objective function will be located
by the devices. In all experiments we were able to find
robust strategies; this means that under certain
conditions strategies evolved in conjunction with an
objective function A will also perform well if acting
on a different function B.",
abstract = "We have also undertaken experiments involving the
co-evolution of strategies and test cases; we show that
co-evolution increases search capabilities of the
strategies evolved with MathEvEco-GP.Compared to this
system realizing classical yet strongly typed Genetic
Programming, MathEvEco-AL is fundamentally different
because of its modeling claims. The results of our
experiments indicate, however, that in this system
evolution of search strategies is realized, too. This
is supported by many parameters of the evolving
ecosystem, e.g. increases in average ages of
individuals, increases in their average resource load
and a steady increase of the overall population size
over time. These observations point out that the
virtual organisms evolve and gradually learn how to
deal with exterior constraints defined mainly by the
resource distribution objective function. Moreover,
because we have used the same design for the devices
evolved in MathEvEco-GP as well as in MathEvEco-AL, the
resulting strategies compare very well. The extra
advantage of the latter system is, of course, that it
enables us to seriously investigate the interaction of
ecosystems and their evolutionary formation without
having to presuppose artifacts like explicit fitness
functions. The mathematical tool for doing this are
hierarchical dynamic systems. We conclude, after all,
that it is possible to start from classical Genetic
Programming and build a system for answering relevant
questions about ecosystem-related evolution processes.
Because of common building blocks of both the classical
and the new system, the results can be compared quite
easily. The Mathematica packages MathEvEco comprising
these systems may be obtained from the author.For this
book touches many different scientific issues there is
an detailed section of annotations deepening biological
and ecosystem modeling aspects as well as referring to
the scientific history of Evolutionary Computation. An
appendix covers software engineering with Mathematica.
The extensive bibliography allows readers to take a
closer look at the issues having been addressed. A
subject index and a list of mathematical symbols
conclude this work.",
title_german = "Evolution{\"{a}}re und Genetische Programmierung im
Lichte moderner {\"{O}}kosystemmodellierung",
abstract = "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{\"{u}}rlicher 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{\"{u}}rliche
Evolution einen Verar-beitungsprozess genetischer
Information darstellt. Es wird untersucht, ob
Evolutionsalgorithmen in Umkehrung ihres bisherigen
Profils auch als Modell f{\"{u}}r 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{\"{u}}hrlich
dargestellt, ebenso wie die vielen durchgef{\"{u}}hrten
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
{\"{O}}kosystemen, sondern wird schlie\sslich auch in
der Lage sein, eigene Evolu-tionsexperimente
durchzuf{\"{u}}hren.",
notes = "Bayreuther Forum Okologie 93, 1-199 (2002)",
}
@InProceedings{Freyeretal1998,
author = "Stephan Freyer and J{\"o}rg Graefe and Matthias
Heinzel and Peter Marenbach",
address = "Aachen, Germany",
booktitle = "Eufit '98, 6th European Congress on Intelligent
Techniques and Soft Computing, ELITE - European
Laboratory for Intelligent TechniquesEngineering",
editor = "Hans-J{\"u}rgen Zimmermann",
pages = "1471--1475",
title = "Evolutionary Generation and Refinement of Mathematical
Process Models",
volume = "III",
year = "1998",
keywords = "genetic algorithms, genetic programming, SMOG,
bioprocess, modelling",
URL = "http://www.rtr.tu-darmstadt.de/fileadmin/literature/rst_98_08.pdf",
URL = "http://www.rt.e-technik.tu-darmstadt.de/LIT",
size = "5 page",
email = "pmarenbach@gmx.net",
abstract = "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.",
notes = "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.
",
}
@Article{Frias-Martinez:2007,
author = "Enrique Frias-Martinez and Fernand Gobet",
title = "Automatic Generation of Cognitive Theories using
Genetic Programming",
journal = "Minds and Machines",
volume = "17",
number = "3",
year = "2007",
pages = "287--309",
month = oct,
address = "Hingham, MA, USA",
publisher = "Kluwer Academic Publishers",
keywords = "genetic algorithms, genetic programming, Cognitive
neuroscience, Computational neuroscience, Automatic
generation of cognitive theories,
Delayed-match-to-sample",
ISSN = "0924-6495",
doi = "doi:10.1007/s11023-007-9070-6",
size = "23 pages",
abstract = "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.",
notes = "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 \cite{1298700}",
}
@Article{Friedberg:1958:LMI,
author = "R. M. Friedberg",
title = "A learning machine: {I}",
journal = "IBM Journal of Research and Development",
volume = "2",
number = "1",
pages = "2--13",
month = jan,
year = "1958",
CODEN = "IBMJAE",
ISSN = "0018-8646",
MRclass = "68.0X",
MRnumber = "19,1085c",
bibdate = "Mon Feb 12 08:25:35 2001",
acknowledgement = "Nelson H. F. Beebe, University of Utah, Department
of Mathematics, 110 LCB, 155 S 1400 E RM 233, Salt Lake
City, UT 84112-0090, USA, Tel: +1 801 581 5254, FAX: +1
801 581 4148, e-mail: \path|beebe@math.utah.edu|,
\path|beebe@acm.org|, \path|beebe@computer.org|
(Internet), URL:
\path|http://www.math.utah.edu/~beebe/|",
reviewer = "M. L. Minsky",
mrnumber-url = "http://www.ams.org/mathscinet-getitem?mr=19%2c1085c",
keywords = "Machine Learning, intron, schema",
URL = "http://www.research.ibm.com/journal/rd/021/ibmrd0201B.pdf",
size = "12 pages",
abstract = "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.",
notes = "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 \cite{spector:1996:ctiGP}.
One bit identity (move a bit). Two bit sum (failed),
high bit of sum, low bit of sum. One bit complement
(not).",
}
@InProceedings{Friedlander:2011:MaFRUGPfCVTW,
title = "Meta-Learning and Feature Ranking Using Genetic
Programming for Classification: Variable Terminal
Weighting",
author = "Anna Friedlander and Kourosh Neshatian and Mengjie
Zhang",
pages = "940--947",
booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary
Computation",
year = "2011",
editor = "Alice E. Smith",
month = "5-8 " # jun,
address = "New Orleans, USA",
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming,
Classification, clustering, data analysis and data
mining",
abstract = "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.",
notes = "CEC2011 sponsored by the IEEE Computational
Intelligence Society, and previously sponsored by the
EPS and the IET.",
}
@InCollection{friedman:2000:EPPR,
author = "Patri Friedman",
title = "Evolving a Program to Play Rock-Paper-Scissors",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2000",
year = "2000",
editor = "John R. Koza",
pages = "143--152",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "part of \cite{koza:2000:gagp}",
}
@InProceedings{friedrich:1996:emfgbb,
author = "Christoph M. Friedrich and Claudio Moraga",
title = "An Evolutionary Method to Find Good Building-Blocks
for Architectures of Artificial Neural Networks",
booktitle = "Proceedings of the Sixth International Conference on
Information Processing and Management of Uncertainty in
Knowledge-Based Systems (IPMU '96)",
year = "1996",
pages = "951--956",
address = "Granada, Spain",
keywords = "genetic algorithms, genetic programming",
broken = "ftp://archive.cis.ohio-state.edu/pub/neuroprose/friedrich.ipmu96.ps.Z",
URL = "http://citeseer.ist.psu.edu/friedrich96evolutionary.html",
abstract = "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.",
}
@InProceedings{fry:2003:gecco,
author = "Rodney Fry and Andy Tyrrell",
title = "Enhancing the Performance of {GP} Using an
Ancestry-Based Mate Selection Scheme",
booktitle = "Genetic and Evolutionary Computation -- GECCO-2003",
editor = "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",
year = "2003",
pages = "1804--1805",
address = "Chicago",
publisher_address = "Berlin",
month = "12-16 " # jul,
volume = "2724",
series = "LNCS",
ISBN = "3-540-40603-4",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, poster",
abstract = "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.",
notes = "GECCO-2003. A joint meeting of the twelfth
International Conference on Genetic Algorithms
(ICGA-2003) and the eighth Annual Genetic Programming
Conference (GP-2003)",
}
@InProceedings{fry:2005:CEC,
author = "Rodney Fry and Stephen L. Smith and Andy M. Tyrrell",
title = "A Self-Adaptive Mate Selection Model for Genetic
Programming",
booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary
Computation",
year = "2005",
editor = "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",
volume = "3",
pages = "2707--2714",
address = "Edinburgh, UK",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "2-5 " # sep,
organisation = "IEEE Computational Intelligence Society, Institution
of Electrical Engineers (IEE), Evolutionary Programming
Society (EPS)",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-9363-5",
abstract = "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.",
notes = "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.
",
}
@InProceedings{LeeannFu:1998:XCSQ,
author = "Leeann L. Fu",
title = "The {XCS} Classifier System and {Q}-learning",
booktitle = "Late Breaking Papers at the Genetic Programming 1998
Conference",
year = "1998",
editor = "John R. Koza",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "Stanford, CA, USA",
month = "22-25 " # jul,
publisher = "Stanford University Bookstore",
keywords = "genetic algorithms, Classifier Systems",
notes = "GP-98LB",
}
@InProceedings{Fu:2010:ICNC,
author = "Weizhong Fu and Yuntao Zhang and Zhengjun Cheng",
title = "Improved gene expression programming and its
application to {QSAR}",
booktitle = "Sixth International Conference on Natural Computation
(ICNC, 2010)",
year = "2010",
volume = "8",
pages = "4057--4061",
month = "10-12 " # aug,
keywords = "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",
isbn13 = "978-1-4244-5958-2",
doi = "10.1109/ICNC.2010.5584850",
abstract = "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.",
}
@InProceedings{Fu:2011:GPFEDAGA,
title = "Genetic Programming For Edge Detection: {A} Global
Approach",
author = "Wenlong Fu and Mark Johnston and Mengjie Zhang",
pages = "254--261",
booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary
Computation",
year = "2011",
editor = "Alice E. Smith",
month = "5-8 " # jun,
address = "New Orleans, USA",
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "CEC2011 sponsored by the IEEE Computational
Intelligence Society, and previously sponsored by the
EPS and the IET.",
}
@InProceedings{fuchs:1996:esnnGA,
author = "Matthias Fuchs",
title = "Evolving Strategies Based on the Nearest Neighbor Rule
and a Genetic Algorithm",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "Genetic Algorithms",
pages = "485--490",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96 GA paper",
}
@InProceedings{Fuchs:1997:spclGP,
author = "Matthias Fuchs and Dirk Fuchs and Marc Fuchs",
title = "Solving Problems of Combinatory Logic with Genetic
Programming",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "102--110",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/Fuchs_1997_spclGP.pdf",
size = "9 pages",
notes = "GP-97",
}
@InProceedings{fuchs:1998:xmetsc,
author = "Matthias Fuchs",
title = "Crossover versus Mutation: An Empirical and
Theoretical Case Study",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "78--85",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-548-7",
notes = "GP-98",
}
@TechReport{fuchs:1998:ARP-09,
author = "Matthias Fuchs",
title = "A Data Mining Approach to Support the Creation of Loop
Invariants Using Genetic Programming",
institution = "Computer Science Laboaratory, Australian National
University",
year = "1999",
type = "Technical Report",
number = "TR-ARP-09-98",
address = "Canberra, ACT 0200, Australia",
month = "12 " # oct,
keywords = "genetic algorithms, genetic programming",
URL = "http://arp.anu.edu.au/ftp/techreports/1998/TR-ARP-09-98.ps.gz",
abstract = "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.",
size = "11 pages",
}
@InProceedings{fuchs:1999:GLTPSUGP,
author = "Marc Fuchs and Dirk Fuchs and Matthias Fuchs",
title = "Generating Lemmas for Tableau-based Proof Search Using
Genetic Programming",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1027--1032",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-400.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-400.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{fuchs:1999:LPANATBCIGP,
author = "Matthias Fuchs",
title = "Large Populations Are Not Always The Best Choice In
Genetic Programming",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1033--1038",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-410.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-410.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{Fuchs:1999:AJ,
author = "Matthias Fuchs",
title = "Evolving Gallery Layouts With Genetic Programming",
booktitle = "Proceedings of The Third Australia-Japan Joint
Workshop on Intelligent and Evolutionary Systems",
year = "1999",
editor = "Bob McKay and Yasuhiro Tsujimura and Ruhul Sarker and
Akira Namatame and Xin Yao and Mitsuo Gen",
address = "School of Computer Science Australian Defence Force
Academy, Canberra, Australia",
month = "22-25 " # nov,
keywords = "genetic algorithms, genetic programming",
notes = "http://www.cs.adfa.edu.au/archive/conference/aj99/programme.html
The Australian National University",
}
@TechReport{fuchs:2000:AEASWDtr,
author = "Matthias Fuchs",
title = "An Evolutionary Approach To Support Web Page Design",
institution = "Computer Science Laboaratory, Australian National
University",
year = "2000",
type = "Technical Report",
number = "TR-ARP-01-2000",
address = "Canberra, ACT 0200, Australia",
month = "4 " # jan,
keywords = "Hill climbing",
URL = "http://arp.anu.edu.au/ftp/techreports/2000/TR-ARP-01-00.ps.gz",
URL = "http://citeseer.ist.psu.edu/295439.html",
abstract = "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.",
notes = "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 \cite{fuchs:2000:AEASWD}",
size = "13 pages",
}
@InProceedings{fuchs:2000:AEASWD,
author = "Matthias Fuchs",
title = "An Evolutionary Approach to Support Web-Page Design",
booktitle = "Proceedings of the 2000 Congress on Evolutionary
Computation CEC00",
year = "2000",
pages = "1312--1319",
address = "La Jolla Marriott Hotel La Jolla, California, USA",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "6-9 " # jul,
organisation = "IEEE Neural Network Council (NNC), Evolutionary
Programming Society (EPS), Institution of Electrical
Engineers (IEE)",
publisher = "IEEE Press",
keywords = "novel applications i",
ISBN = "0-7803-6375-2",
notes = "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
\cite{fuchs:2000:AEASWDtr}",
}
@InProceedings{fuhner:2001:gecco,
title = "EvolVision - an Evolvica visualization tool",
author = "Tim Fuhner and Christian Jacob",
pages = "176",
year = "2001",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2001)",
editor = "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",
address = "San Francisco, California, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "7-11 " # jul,
keywords = "genetic algorithms, genetic programming: Poster,
EvolVision, Evolvica, visualization, Mathematica, Java,
client/server application, plug-in architecture,
pedigree diagrams",
ISBN = "1-55860-774-9",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d02.pdf",
notes = "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 \cite{spector:2001:GECCO}",
}
@InProceedings{ga:Dickinson87,
author = "Cory Fujiki and John Dickinson",
title = "Using the Genetic Algorithm to Generate Lisp Source
Code to Solve the Prisoner's Dilemma",
booktitle = "Genetic Algorithms and their Applications: Proceedings
of the second international conference on Genetic
Algorithms",
year = "1987",
editor = "John J. Grefenstette",
pages = "236--240",
address = "MIT, Cambridge, MA, USA",
month = "28-31 " # jul,
organisation = "AAAI, Naval Research Laboratory, Bolt Beranek and
Newman, Inc",
publisher_address = "Hillsdale, NJ, USA",
publisher = "Lawrence Erlbaum Associates",
keywords = "genetic algorithms",
size = "5 pages",
notes = "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.",
}
@InProceedings{fukunaga:1995:dsef,
author = "Alex S. Fukunaga and Andrew B. Kahng",
title = "Improving the Performance of Evolutionary Optimization
by Dynamically Scaling the Evolution Function",
booktitle = "1995 IEEE Conference on Evolutionary Computation",
year = "1995",
volume = "1",
pages = "182--187",
address = "Perth, Australia",
publisher_address = "Piscataway, NJ, USA",
month = "29 " # nov # " - 1 " # dec,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
URL = "http://metahack.org/Fukunaga-Kahng-ICEC-1995.pdf",
URL = "http://citeseer.ist.psu.edu/fukunaga95improving.html",
size = "6 pages",
abstract = "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.",
notes = "ICEC-95 Editors not given by IEEE, Organisers David
Fogel and Chris deSilva.
",
}
@InProceedings{fukunaga:1998:gchpGP,
author = "Alex Fukunaga and Andre Stechert and Darren Mutz",
title = "A Genome Compiler for High Performance Genetic
Programming",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "86--94",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-548-7",
URL = "http://fukunaga.bol.ucla.edu/gp98-compiler.pdf",
URL = "http://citeseer.ist.psu.edu/fukunaga98genome.html",
abstract = "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...",
notes = "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",
}
@InProceedings{fukunaga:1998:enlpmllicGP,
author = "Alex Fukunaga and Andre Stechert",
title = "Evolving Nonlinear Predictive Models for Lossless
Image Compression with Genetic Programming",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "95--102",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-548-7",
URL = "http://www.bol.ucla.edu/~fukunaga/gp98-compress.pdf",
URL = "http://citeseer.ist.psu.edu/507773.html",
size = "8 pages",
abstract = "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.",
notes = "GP-98",
}
@InProceedings{fukunaga:1999:PGA,
author = "Alex S. Fukunaga",
title = "Portfolios of Genetic Algorithms",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "786",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems, poster
papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-840.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-840.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{fukunaga:2002:AAAI,
author = "Alex Fukunaga",
title = "Automated Discovery of Composite {SAT} Variable
Selection Heuristics",
booktitle = "Proceedings of the National Conference on Artificial
Intelligence (AAAI)",
year = "2002",
pages = "641--648",
keywords = "genetic algorithms, genetic programming,
satisfiability, constraint satisfaction, local search",
URL = "http://citeseer.nj.nec.com/506523.html",
URL = "http://www.bol.ucla.edu/~fukunaga/AAAI02.pdf",
abstract = "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.",
}
@InProceedings{Fukunaga:2004:sat,
author = "Alex Fukunaga",
title = "Efficient Implementations of {SAT} Local Search",
booktitle = "The Seventh International Conference on Theory and
Applications of Satisfiability Testing (SAT 2004)",
year = "2004",
address = "Vancouver, BC, Canada",
month = "10-13 " # may,
keywords = "Poster",
URL = "http://www.satisfiability.org/SAT04/programme/106.pdf",
URL = "http://metahack.org/sat2004.pdf",
size = "6 pages",
abstract = "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.",
notes = "cited by \cite{Fukunaga:2009:cec} Does not mention GP.
Does not appear to be in LNCS",
}
@InProceedings{fukunaga:els:gecco2004,
author = "Alex S. Fukunaga",
title = "Evolving Local Search Heuristics for {SAT} Using
Genetic Programming",
booktitle = "Genetic and Evolutionary Computation -- GECCO-2004,
Part II",
year = "2004",
editor = "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",
series = "Lecture Notes in Computer Science",
pages = "483--494",
address = "Seattle, WA, USA",
publisher_address = "Heidelberg",
month = "26-30 " # jun,
organisation = "ISGEC",
publisher = "Springer-Verlag",
volume = "3103",
ISBN = "3-540-22343-6",
ISSN = "0302-9743",
URL = "http://alexf04.maclisp.org/gecco2004.pdf",
URL = "http://link.springer.de/link/service/series/0558/bibs/3103/31030483.htm",
size = "12 pages",
keywords = "genetic algorithms, genetic programming",
abstract = "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).",
notes = "GECCO-2004 A joint meeting of the thirteenth
international conference on genetic algorithms
(ICGA-2004) and the ninth annual genetic programming
conference (GP-2004)",
}
@Article{Fukunaga:2008:EC,
author = "Alex S. Fukunaga",
title = "Automated Discovery of Local Search Heuristics for
Satisfiability Testing",
journal = "Evolutionary Computation",
year = "2008",
volume = "16",
number = "1",
pages = "31--61",
month = "Spring",
keywords = "genetic algorithms, genetic programming, STGP,
satisfiability, constraint satisfaction, SAT,
hyper-heuristic, hybrid genetic-local search",
ISSN = "1063-6560",
URL = "http://metahack.org/ecj08-web-preprint.pdf",
doi = "doi:10.1162/evco.2008.16.1.31",
size = "31 pages",
abstract = "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.",
}
@InProceedings{Fukunaga:2009:lisp,
author = "Alex S. Fukunaga",
title = "A Parallel, Lisp-Based Genetic Programming System for
Discovering Satisfiability Solvers",
booktitle = "International Lisp Conference, ILC 2009",
year = "2009",
editor = "Guy L. {Steele, Jr.}",
pages = "137--148",
address = "Massachusetts Institute of Technology, Cambridge,
Massachusetts, USA",
month = mar # " 22-25",
organisation = "ALU",
keywords = "genetic algorithms, genetic programming",
notes = "http://www.international-lisp-conference.org/2009/speakers",
}
@InProceedings{Fukunaga:2009:cec,
author = "Alex S. Fukunaga",
title = "Massively Parallel Evolution of {SAT} Heuristics",
booktitle = "2009 IEEE Congress on Evolutionary Computation",
year = "2009",
editor = "Andy Tyrrell",
pages = "1478--1485",
address = "Trondheim, Norway",
month = "18-21 " # may,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-2959-2",
file = "P308.pdf",
doi = "doi:10.1109/CEC.2009.4983117",
size = "8 pages",
abstract = "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.",
keywords = "genetic algorithms, genetic programming, STGP,
hyperheuristics, MPI",
notes = "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",
}
@Article{Fukunaga:2012:GPEM,
author = "Alex Fukunaga and Hideru Hiruma and Kazuki Komiya and
Hitoshi Iba",
title = "Evolving controllers for high-level applications on a
service robot: a case study with exhibition visitor
flow control",
journal = "Genetic Programming and Evolvable Machines",
year = "2012",
volume = "13",
number = "2",
pages = "239--263",
month = jun,
keywords = "genetic algorithms, genetic programming, Evolutionary
robotics, Service robotics Applications",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-011-9152-3",
size = "25 pages",
abstract = "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.",
affiliation = "The University of Tokyo, Tokyo, Japan",
}
@InProceedings{fukuyama:1999:APSORPVCEPS,
author = "Yoshikazu Fukuyama and Shinichi Takayama and Yosuke
Nakanishi and Hirotaka Yoshida",
title = "A Particle Swarm Optimization for Reactive Power and
Voltage Control in Electric Power Systems",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1523--1528",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "real world applications",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-713.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-713.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@TechReport{cs-97-197,
author = "Pablo Funes and Elizabeth Sklar and Hugues Juille and
Jordan Pollack",
title = "The Internet as a Virtual Ecology: Coevolutionary Arms
Races Between Human and Artificial Populations",
institution = "Computer Science, Brandeis University",
year = "1997",
type = "Technical Report",
number = "CS-97-197",
address = "415 South St., Waltham MA 02254 USA",
keywords = "genetic algorithms, genetic programming, autonomous
agents, adaptive software, evolutionary robotics, game
learning, coevolution, Tron, interactive evolution",
URL = "http://helen.cs-i.brandeis.edu/papers/cs-97-197.pdf",
URL = "http://helen.cs-i.brandeis.edu/papers/cs-97-197.ps.gz",
URL = "http://helen.cs-i.brandeis.edu/papers/cs-97-197.ps",
URL = "http://www.demo.cs.brandeis.edu/papers/long.html#cs-97-197",
abstract = "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.",
notes = "See also \cite{funes_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.
",
size = "20 pages",
}
@InProceedings{funes_ecal97,
author = "Pablo Funes and Jordan Pollack",
title = "Computer Evolution of Buildable Objects",
booktitle = "Fourth European Conference on Artificial Life",
year = "1997",
editor = "P. Husbands and I. Harvey",
pages = "358--367",
publisher = "MIT Press",
keywords = "genetic algorithms, genetic programming, evolutionary
design, evolutionary robotics, computer simulation",
URL = "http://www.demo.cs.brandeis.edu/papers/ecal97.pdf",
URL = "http://www.demo.cs.brandeis.edu/papers/ecal97.ps.gz",
size = "10 pages",
abstract = "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.",
notes = "An earlier revision of this paper is available in
html: Brandeis University Computer Science Technical
Report CS-97-191",
}
@InProceedings{funes_sab98,
author = "Pablo Funes and Elizabeth Sklar and Hugues Juille and
Jordan Pollack",
title = "Animal-Animat Coevolution: Using the Animal Population
as Fitness Function",
booktitle = "From Animals to Animats 5: Proceedings of the Fifth
International Conference on Simulation of Adaptive
Behavior",
year = "1998",
editor = "Rolf Pfeifer and Bruce Blumberg and Jean-Arcady Meyer
and Stewart W. Wilson",
pages = "525--533",
address = "Zurich, Switzerland",
month = aug # " 17-21",
publisher = "MIT Press",
keywords = "genetic algorithms, genetic programming, adaptive
agents, internet evolution, computer game playing",
ISBN = "0-262-66144-6",
URL = "http://www.demo.cs.brandeis.edu/papers/tronsab98.pdf",
URL = "http://www.demo.cs.brandeis.edu/papers/tronsab98.ps.gz",
URL = "http://www.demo.cs.brandeis.edu/papers/tronsab98.ps",
size = "9 pages",
abstract = "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.",
notes = "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",
}
@TechReport{funes_cs98-198,
author = "Pablo J. Funes and Jordan B. Pollack",
title = "Componential Structural Simulator",
institution = "Computer Science, Brandeis University",
year = "1998",
type = "Technical Report",
number = "CS-98-198",
address = "415 South St., Waltham MA 02254 USA",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.demo.cs.brandeis.edu/papers/cs98-198.pdf",
abstract = "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.",
notes = "not about GP but about part of fitness function used
in GP experiments, eg in \cite{funes_alife}",
size = "17 pages",
}
@Article{funes_alife,
author = "Pablo Funes and Jordan Pollack",
title = "Evolutionary Body Building: Adaptive Physical Designs
for Robots",
journal = "Artificial Life",
year = "1998",
volume = "4",
number = "4",
pages = "337--357",
month = "Fall",
keywords = "genetic algorithms, genetic programming, evolutionary
robotics, body and brain coevolution, adaptive bodies,
evolutionary design, lego, children's building blocks",
ISSN = "1064-5462",
URL = "http://www.demo.cs.brandeis.edu/papers/funpolalife.pdf",
URL = "http://www.demo.cs.brandeis.edu/papers/funpolalife.ps.gz",
bytes14139576 = "http://www.demo.cs.brandeis.edu/papers/funpolalife.ps",
URL = "http://mitpress.mit.edu/catalog/item/default.asp?sid=8F59C20B-F846-405E-9C5C-6F86770D37BB&ttype=6&tid=109",
doi = "doi:10.1162/106454698568639",
size = "21 pages",
abstract = "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.",
notes = "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.
\cite{koza: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
\cite{Tackett:1995:grgsscp} soft brood
selection).
Saftey margin only 0.2
Cites \cite{funes_cs98-198}",
}
@InCollection{funes_edc98,
author = "Pablo J. Funes and Jordan B. Pollack",
title = "Computer Evolution of Buildable Objects",
booktitle = "Evolutionary Design by Computers",
publisher = "Morgan Kaufmann",
year = "1999",
editor = "Peter J. Bentley",
chapter = "17",
pages = "387--403",
address = "San Francisco, USA",
keywords = "genetic algorithms, genetic programming, evolutionary
design, evolutionary robotics, computer simulation",
ISBN = "1-55860-605-X",
URL = "http://www.demo.cs.brandeis.edu/papers/edc98.pdf",
URL = "http://www.demo.cs.brandeis.edu/papers/edc98.ps.gz",
bytes12043580 = "http://www.demo.cs.brandeis.edu/papers/edc98.ps",
abstract = "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.",
notes = "http://www.amazon.com/exec/obidos/ASIN/155860605X/qid=1114257064/sr=2-1/ref=pd_bbs_b_2_1/103-2923288-2944615",
size = "pages",
}
@PhdThesis{funes_phd,
author = "Pablo Funes",
title = "Evolution of Complexity in Real-World Domains",
school = "Computer Science, Brandeis University",
year = "2001",
month = may,
keywords = "genetic algorithms, genetic programming, AI",
URL = "http://www.demo.cs.brandeis.edu/papers/funes_phd.pdf",
URL = "http://www.demo.cs.brandeis.edu/papers/funes_phd.ps",
URL = "http://www.demo.cs.brandeis.edu/papers/funes_phd.html",
size = "167 pages",
abstract = "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.",
}
@Article{funes:2007:sigevo,
author = "Pablo Jose Funes",
title = "Buildable Evolution",
journal = "SIGEVOlution",
year = "2007",
volume = "2",
number = "3",
pages = "6--19",
month = "Autumn",
keywords = "genetic algorithms, genetic programming, LEGO",
URL = "http://www.sigevolution.org/issues/pdf/SIGEVOlution200703.pdf",
size = "14 pages",
abstract = "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).",
notes = "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.",
}
@InProceedings{furuholmen2008continuous,
author = "Marcus Furuholmen and Mats Hovin and Jim Torresen and
Kyrre Glette",
title = "Continuous Adaptation in Robotic Systems by Indirect
Online Evolution",
booktitle = "ECSIS Symposium on Learning and Adaptive Behaviors for
Robotic Systems, LAB-RS 2008",
year = "2008",
pages = "71--76",
address = "Edinburgh",
month = "6-8 " # aug,
publisher = "IEEE",
keywords = "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",
isbn13 = "978-0-7695-3272-1",
doi = "doi:10.1109/LAB-RS.2008.13",
size = "6 pages",
abstract = "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.",
notes = "Also known as \cite{4599430}",
}
@InProceedings{furuholmen2008indirect,
author = "Marcus Furuholmen and Kyrre Glette and Jim Torresen
and Mats Hovin",
title = "Indirect Online Evolution - {A} Conceptual Framework
for Adaptation in Industrial Robotic Systems",
booktitle = "8th International Conference on Evolvable Systems:
From Biology to Hardware, ICES 2008",
year = "2008",
editor = "Gregory Hornby and Lukas Sekanina and Pauline C.
Haddow",
series = "Lecture Notes in Computer Science",
volume = "5216",
pages = "165--176",
address = "Prague, Czech Republic",
month = sep # " 21-24",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-540-85856-0",
doi = "doi:10.1007/978-3-540-85857-7_15",
size = "12 pages",
abstract = "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.",
notes = "ICES",
bibsource = "DBLP, http://dblp.uni-trier.de",
}
@InProceedings{Furuholmen:2009:cec,
author = "Marcus Furuholmen and Kyrre Glette and Mats Hovin and
Jim Torresen",
title = "Coevolving Heuristics for The Distributor's Pallet
Packing Problem",
booktitle = "2009 IEEE Congress on Evolutionary Computation",
year = "2009",
editor = "Andy Tyrrell",
pages = "-",
address = "Trondheim, Norway",
month = "18-21 " # may,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-2959-2",
file = "P260.pdf",
abstract = "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.",
keywords = "genetic algorithms, genetic programming, gene
expression programming",
notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and
the IET. IEEE Catalog Number: CFP09ICE-CDR",
}
@InProceedings{DBLP:conf/gecco/FuruholmenGHT09,
author = "Marcus Furuholmen and Kyrre Harald Glette and Mats
Erling Hovin and Jim Torresen",
title = "Scalability, generalization and coevolution --
experimental comparisons applied to automated facility
layout planning",
booktitle = "GECCO '09: Proceedings of the 11th Annual conference
on Genetic and evolutionary computation",
year = "2009",
editor = "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",
pages = "691--698",
address = "Montreal",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "8-12 " # jul,
organisation = "SigEvo",
keywords = "genetic algorithms, genetic programming, Gene
Expression Programming",
isbn13 = "978-1-60558-325-9",
bibsource = "DBLP, http://dblp.uni-trier.de",
doi = "doi:10.1145/1569901.1569997",
abstract = "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.",
notes = "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.",
}
@InProceedings{Furuholmen:2010:EuroGP,
author = "Marcus Furuholmen and Kyrre Glette and Mats Hovin and
Jim Torressen",
title = "An Indirect Approach to the Three-dimensional
Multi-pipe Routing Problem",
booktitle = "Proceedings of the 13th European Conference on Genetic
Programming, EuroGP 2010",
year = "2010",
editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara
Silva and Stephen Dignum and A. Sima Uyar",
volume = "6021",
series = "LNCS",
pages = "86--97",
address = "Istanbul",
month = "7-9 " # apr,
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-12147-0",
doi = "doi:10.1007/978-3-642-12148-7_8",
abstract = "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.",
notes = "Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010
held in conjunction with EvoCOP2010 EvoBIO2010 and
EvoApplications2010",
}
@InProceedings{furuholmen2010evolutionary,
author = "Marcus Furuholmen and Kyrre Glette and Mats H{\o}vin
and Jim Torresen",
title = "Evolutionary Approaches to the Three-dimensional
Multi-pipe Routing Problem: {A} Comparative Study Using
Direct Encodings",
booktitle = "Evolutionary Computation in Combinatorial
Optimization, 10th European Conference, EvoCOP 2010,
Istanbul, Turkey, April 7-9, 2010. Proceedings",
year = "2010",
editor = "Peter I. Cowling and Peter Merz",
volume = "6022",
series = "Lecture Notes in Computer Science",
pages = "71--82",
publisher = "Springer",
keywords = "genetic algorithms",
doi = "doi:10.1007/978-3-642-12139-5_7",
notes = "'not using GP, but rather GA - however, the results
are compared with GP in another paper which is in GP
bib.'",
bibsource = "DBLP, http://dblp.uni-trier.de",
}
@InProceedings{Furuholmen:2010:cec,
author = "Marcus Furuholmen and Kyrre Glette and Mats Hovin and
Jim Torresen",
title = "A Coevolutionary, Hyper Heuristic approach to the
optimization of Three-dimensional Process Plant Layouts
-{A} comparative study",
booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-6910-9",
abstract = "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.",
doi = "doi:10.1109/CEC.2010.5586329",
notes = "WCCI 2010. Also known as \cite{5586329}",
}
@InProceedings{furutani:1999:ASIPGAML,
author = "Hiroshi Furutani",
title = "Analytical Solutions for Infinite Population Genetic
Algorithms on Multiplicative Landscape",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "204--211",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
abstract = "eigen values, eigenvectors, walsh functions",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@Article{fyfe:1999:AFE,
author = "Colin Fyfe and John Paul Marney and Heather F. E.
Tarbert",
title = "Technical analysis versus market efficiency - a
genetic programming approach",
journal = "Applied Financial Economics",
year = "1999",
volume = "9",
number = "2",
pages = "183--191",
month = apr,
keywords = "genetic algorithms, genetic programming",
URL = "http://alidoro.catchword.com/vl=8080356/cl=18/nw=1/fm=docpdf/rpsv/catchword/routledg/09603107/v9n2/s7/p183",
doi = "doi:10.1080/096031099332447",
size = "9 pages",
abstract = "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.",
}
@InProceedings{Gabbouj:2010:IPTA,
author = "Moncef Gabbouj",
title = "Multidimensional particle swarm optimization and
applications in data clustering and image retrieval",
booktitle = "Image Processing Theory Tools and Applications (IPTA),
2010 2nd International Conference on",
year = "2010",
month = jul,
pages = "5",
abstract = "Particle swarm optimization (PSO) was introduced by
Kennedy and Eberhart in 1995 as a population based
stochastic search and optim",
doi = "doi:10.1109/IPTA.2010.5586831",
ISSN = "2154-5111",
notes = "Also known as \cite{5586831}",
}
@InProceedings{Gabel:2010:FSE,
author = "Mark Gabel and Zhendong Su",
title = "A Study of the Uniqueness of Source Code",
booktitle = "Proceedings of the eighteenth ACM SIGSOFT
international symposium on Foundations of software
engineering",
year = "2010",
pages = "147--156",
address = "Santa Fe, New Mexico, USA",
month = "7-11 " # nov,
publisher = "ACM",
acmid = "1882315",
keywords = "genetic algorithms, genetic programming, large scale
study, software uniqueness, source code",
isbn13 = "978-1-60558-791-2",
URL = "http://www.cs.ucdavis.edu/~su/publications/fse10.pdf",
doi = "doi:10.1145/1882291.1882315",
size = "10 pages",
abstract = "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.",
notes = "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,
\cite{koza:book} and \cite{Weimer:2009:ICES}.
FSE '10, Gabel:2010:SUS:1882291.1882315",
}
@InProceedings{gagne:2002:gecco,
author = "Christian Gagn{\'e} and Marc Parizeau",
title = "Open {BEAGLE}: {A} New {C++} Evolutionary Computation
Framework",
booktitle = "GECCO 2002: Proceedings of the Genetic and
Evolutionary Computation Conference",
editor = "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",
year = "2002",
pages = "888",
address = "New York",
publisher_address = "San Francisco, CA 94104, USA",
month = "9-13 " # jul,
publisher = "Morgan Kaufmann Publishers",
keywords = "genetic algorithms, genetic programming, poster paper,
artificial intelligence, evolutionary computation
framework, object oriented genetic programming,
software engineering, software tools",
ISBN = "1-55860-878-8",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/GP272.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-15.pdf",
notes = "GECCO-2002. A joint meeting of the eleventh
International Conference on Genetic Algorithms
(ICGA-2002) and the seventh Annual Genetic Programming
Conference (GP-2002)",
}
@InProceedings{gagne:2002:gecco:lbp,
title = "Open {BEAGLE:} {A} New Versatile {C}++ Framework for
Evolutionary Computation",
author = "Christian Gagn{\'e} and Marc Parizeau",
booktitle = "Late Breaking Papers at the Genetic and Evolutionary
Computation Conference ({GECCO-2002})",
editor = "Erick Cant{\'u}-Paz",
year = "2002",
month = jul,
pages = "161--168",
address = "New York, NY",
publisher = "AAAI",
publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
keywords = "genetic algorithms, genetic programming",
URL = "http://vision.gel.ulaval.ca/en/publications/Id_43/PublDetails.php",
notes = "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",
}
@InProceedings{gagne:2003:HPCS,
author = "Christian Gagne and Marc Parizeau and Marc Dubreuil",
title = "Distributed {BEAGLE}: An Environment for Parallel and
Distributed Evolutionary Computations",
booktitle = "Procceedings of the 17th Annual International
Symposium on High Performance Computing Systems and
Applications (HPCS) 2003",
year = "2003",
address = "Sherbrooke, Quebec, Canada",
month = may # " 11-14",
keywords = "genetic algorithms, genetic programming",
URL = "http://vision.gel.ulaval.ca/~cgagne/pubs/hpcs03.pdf",
URL = "http://vision.gel.ulaval.ca/fr/publications/Id_439/PublDetails.php",
URL = "http://vision.gel.ulaval.ca/en/publications/Id_439/PublDetails.php",
size = "8 pages",
abstract = "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.",
}
@InProceedings{Gagne:2003:gecco,
author = "Christian Gagne and Marc Parizeau and Marc Dubreuil",
title = "The Master-Slave Architecture for Evolutionary
Computations Revisited",
booktitle = "Genetic and Evolutionary Computation -- GECCO-2003",
editor = "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",
year = "2003",
pages = "1578--1579",
address = "Chicago",
publisher_address = "Berlin",
month = "12-16 " # jul,
volume = "2724",
series = "LNCS",
ISBN = "3-540-40603-4",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, poster",
URL = "http://www.gel.ulaval.ca/~cgagne/pubs/master-gecco03.pdf",
URL = "http://vision.gel.ulaval.ca/en/publications/Id_440/PublDetails.php",
abstract = "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.",
notes = "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",
}
@InProceedings{gagne:gecco03lbp,
title = "A Robust Master-Slave Distribution Architecture for
Evolutionary Computations",
pages = "80--87",
author = "Christian Gagne and Marc Parizeau and Marc Dubreuil",
year = "2003",
address = "Chicago, USA",
month = "12--16 " # jul,
editor = "Bart Rylander",
booktitle = "Genetic and Evolutionary Computation Conference Late
Breaking Papers",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.gel.ulaval.ca/~cgagne/pubs/lbp-gecco03.pdf",
URL = "http://vision.gel.ulaval.ca/en/publications/Id_456/PublDetails.php",
abstract = "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.",
notes = "GECCO-2003LB",
}
@PhdThesis{gagne:thesis,
author = "Christian Gagne",
title = "Algorithmes evolutionnaires appliques a la
reconnaissance des formes et a la conception optique",
school = "Laval University",
year = "2005",
address = "Quebec (QC), Canada",
month = may,
keywords = "genetic algorithms, genetic programming",
URL = "http://vision.gel.ulaval.ca/en/publications/Id_528/PublDetails.php",
URL = "http://vision.gel.ulaval.ca/~cgagne/pubs/these-cgagne.pdf",
URL = "http://www.theses.ulaval.ca/2005/22701/22701.pdf",
size = "212 pages",
abstract = "Evolutionary Algorithms (EA) encompass a family of
robust search algorithms loosely inspired by natural
evolution. These algorithms are particularly useful to
solve problems for which classical algorithms of
optimisation, learning, or automatic design cannot
produce good results. In this thesis, we propose a
common methodological approach for the development of
EA-based intelligent systems. This methodological
approach is based on five principles: 1) to use
algorithms and representations that are problem
specific; 2) to develop hybrids between EA and
heuristics from the application field; 3) to take
advantage of multi-objective evolutionary optimization;
4) to do co-evolution for the simultaneous resolution
of several sub-problems of a common application and for
promoting robustness; and 5) to use generic software
tools for rapid development of unconventional EA. This
methodological approach is illustrated on four
applications of EA to hard problems. Moreover, the
fifth principle is explained in the study on genericity
of EA software tools.
The application of EA to complex problems requires the
use of generic software tool, for which we propose six
genericity criteria. Many EA software tools are
available in the community, but only a few are really
generic. Indeed, an evaluation of some popular tools
tells us that only three respect all these criteria, of
which the framework Open BEAGLE, developed during the
Ph.D. Open BEAGLE is organised into three main software
layers. The basic layer is made of the object oriented
foundations, over which there is the generic framework
layer, consisting of the general mechanisms of the
tool, and then the final layer, containing several
specialised frameworks implementing different EA
flavours. The tool also includes two extensions,
respectively to distribute the computations over many
computers and to visualise results.
Three applications illustrate different approaches for
using EA in the context of pattern recognition. First,
nearest neighbour classifiers are optimised, with the
prototype selection using a genetic algorithm
simultaneously to the Genetic Programming (GP) of
neighbourhood metrics. We add to this cooperative two
species co-evolution a third co-evolving competitive
species for selecting test data in order to improve the
generalisation capability of solutions. A second
application consists in designing representations with
GP for handwritten character recognition. This
evolutionary engineering is conducted with an automatic
positioning of regions in a window of attention,
combined with the selection of fuzzy sets for feature
extraction. This application is used to automate
character representation search, which is usually
conducted by human experts with a trial and error
process. For the third application in pattern
recognition, we propose an extensible system for the
hierarchical combination of classifiers into a fuzzy
decision tree. In this system, the tree topology is
evolved with GP while the numerical parameters of
classification units are determined by specialized
learning techniques. The system is tested with three
simple types of classification units. All of these
applications in pattern recognition have been
implemented using a two-objective fitness measure in
order to minimise classification errors and solutions
complexity. The last application demonstrate the
efficiency of EA for lens system design.
Self-adaptative evolution strategies, hybridised with a
specialised local optimisation technique, are used to
solve two complex optical design problems. In both
cases, the experiments demonstrate that hybridized EA
are able to produce results that are comparable or
better than those obtained by human experts. These
results are encouraging from the standpoint of a fully
automated optical design process. An additional
experiment is also conducted with a two-objectives
fitness measure that tries to maximise image quality
while minimising lens system cost.",
abstract = "Les algorithmes {\'e}volutionnaires (AE) constituent
une famille d{'}algorithmes inspir{\'e}s de
l{'}{\'e}volution naturelle. Ces algorithmes sont
particuli{\`e}rement utiles pour la r{\'e}solution de
probl{\`e}mes o{\`u} les algorithmes classiques
d{'}optimisation, d{'}apprentissage ou de conception
automatique sont incapables de produire des
r{\'e}sultats satisfaisants. On propose dans cette
th{\`e}se une approche m{\'e}thodologique pour le
d{\'e}veloppement de syst{\`e}mes intelligents
bas{\'e}s sur les AE. Cette approche m{\'e}thodologique
repose sur cinq principes : 1) utiliser des algorithmes
et des repr{\'e}sentations adapt{\'e}s au probl{\`e}me
; 2) d{\'e}velopper des hybrides entre des AE et des
heuristiques du domaine d{'}application ; 3) tirer
profit de l{'}optimisation {\'e}volutionnaire {\`a}
plusieurs objectifs ; 4) faire de la co-{\'e}volution
pour r{\'e}soudre simultan{\'e}ment plusieurs
sous-probl{\`e}mes d{'}une application ou favoriser la
robustesse ; et 5) utiliser un outil logiciel
g{\'e}n{\'e}rique pour le d{\'e}veloppement rapide
d{'}AE non conventionnels. Cette approche
m{\'e}thodologique est illustr{\'e}e par quatre
applications des AE {\`a} des probl{\`e}mes difficiles.
De plus, le cinqui{\`e}me principe est appuy{\'e} par
l{'}{\'e}tude sur la g{\'e}n{\'e}ricit{\'e} dans les
outils logiciels d{'}AE. Le d{\'e}veloppement
d{'}applications complexes avec les AE exige
l{'}utilisation d{'}un outil logiciel
g{\'e}n{\'e}rique. Six crit{\`e}res sont propos{\'e}s
ici pour {\'e}valuer la g{\'e}n{\'e}ricit{\'e} des
outils d{'}AE. De nombreux outils logiciels d{'}AE sont
disponibles dans la communaut{\'e}, mais peu d{'}entre
eux peuvent {\^e}tre v{\'e}ritablement qualifi{\'e}s de
g{\'e}n{\'e}riques. En effet, une {\'e}valuation de
quelques outils relativement populaires nous indique
que seulement trois satisfont pleinement {\`a} tous ces
crit{\`e}res, dont la framework d{'}AE Open BEAGLE,
d{\'e}velopp{\'e}e durant le doctorat.",
abstract = "Open BEAGLE est organis{\'e} en trois couches
logicielles principales, avec {\`a} la base les
fondations orient{\'e}es objet, sur lesquelles
s{'}ajoute une framework g{\'e}n {\'e}rique comprenant
les m{\'e}canismes g{\'e}n{\'e}raux de l{'}outil, ainsi
que plusieurs frameworks sp{\'e}cialis{\'e}es qui
implantent diff{\'e}rentes saveurs d{'}AE. L{'}outil
comporte {\'e}galement deux extensions servant {\`a}
distribuer des calculs sur plusieurs ordinateurs et
{\`a} visualiser des r{\'e}sultats. Ensuite, trois
applications illustrent diff{\'e}rentes approches
d{'}utilisation des AE dans un contexte de
reconnaissance des formes. Premi{\`e}rement, on
optimise des classifieurs bas{\'e}s sur la r{\`e}gle du
plus proche voisin avec la s{\'e}lection de prototypes
par un algorithme g{\'e}n{\'e}tique, simultan{\'e}ment
{\`a} la construction de mesures de voisinage par
programmation g{\'e}n{\'e}tique (PG). {\`A} cette
co-{\'e}volution coop{\'e}rative {\`a} deux
esp{\`e}ces, on ajoute la co-{\'e}volution
comp{\'e}titive d{'}une troisi{\`e}me esp{\`e}ce pour
la s{\'e}lection de donn{\'e}es de test, afin
d{'}am{\'e}liorer la capacit{\'e} de
g{\'e}n{\'e}ralisation des solutions. La deuxi{\`e}me
application consiste en l{'}ing{\'e}nierie de
repr{\'e}sentations par PG pour la reconnaissance de
caract{\`e}res manuscrits. Cette ing{\'e}nierie
{\'e}volutionnaire s{'}effectue par un positionnement
automatique de r{\'e}gions dans la fen{\^e}tre
d{'}attention jumel{\'e} {\`a} la s{\'e}lection
d{'}ensembles flous pour l{'}extraction de
caract{\'e}ristiques. Cette application permet
d{'}automatiser la recherche de repr{\'e}sentations de
caract{\`e}res, op{\'e}ration g{\'e}n{\'e}ralement
effectu{\'e}e par des experts humains suite {\`a} un
processus d{'}essais et erreurs. Pour la troisi{\`e}me
application en reconnaissance des formes, on propose un
syst{\`e}me extensible pour la combinaison
hi{\'e}rarchique de classifieurs dans un arbre de
d{\'e}cision flou. Dans ce syst{\`e}me, la topologie
des arbres est {\'e}volu{\'e}e par PG alors que les
param{\`e}tres num{\'e}riques des unit{\'e}s de
classement sont d{\'e}termin {\'e}s par des techniques
d{'}apprentissage sp{\'e}cialis{\'e}es. Le syst{\`e}me
est test{\'e} avec trois types simples d{'}unit{\'e}s
de classement. Pour toutes ces applications en
reconnaissance des formes, on utilise une mesure
d{'}ad{\'e}quation {\`a} deux objectifs afin de
minimiser les erreurs de classement et la
complexit{\'e} des solutions. Une derni{\`e}re
application d{\'e}montre l{'}efficacit{\'e} des AE pour
la conception de syst` emes de lentilles. On utilise
des strat{\'e}gies d{'}{\'e}volution auto-adaptatives
hybrid{\'e}es avec une technique d{'}optimisation
locale sp{\'e}cialis{\'e}e pour la r{\'e}solution de
deux probl{\`e}mes complexes de conception optique.
Dans les deux cas, on d{\'e}montre que les AE hybrides
sont capables de g{\'e}n{\'e}rer des r{\'e}sultats
comparables ou sup{\'e}rieurs {\`a} ceux produits par
des experts humains. Ces r{\'e}sultats sont prometteurs
dans la perspective d{'}une automatisation plus
pouss{\'e}e de la conception optique. On pr{\'e}sente
{\'e}galement une exp{\'e}rience suppl{\'e}mentaire
avec une mesure {\`a} deux objectifs servant {\`a}
maximiser la qualit{\'e} de l{'}image et {\`a}
minimiser le co{\^u}t du syst{\`e}me de lentilles.;",
bibsource = "OAI-PMH server at oai.collectionscanada.ca",
contributor = "Marc Parizeau",
identifier = "TC-QQLA-22701",
language = "FR",
oai = "oai:collectionscanada.ca:QQLA.2005/22701",
rights = "{\copyright} Christian Gagn{\'e}, 2005",
notes = "Cf posting to GP-list Tue, 11 Oct 2005 09:50:18
+0200
Entirely written in French",
}
@TechReport{oai:hal.ccsd.cnrs.fr:inria-00000996_v1,
title = "Genetic Programming, Validation Sets, and Parsimony
Pressure",
author = "Christian Gagn{\'e} and Marc Schoenauer and Marc
Parizeau and Marco Tomassini",
publisher = "HAL - CCSd - CNRS",
year = "2006",
month = jan # "~09",
institution = "l'Equipe TAO INRIA Futurs",
type = "ARTCOLLOQUE",
number = "inria-00000996",
address = "LRI Bat. 490, Universite Paris Sud, 91405 Orsay CEDEX,
France",
annote = "Christian Gagn{\'e} ",
bibsource = "OAI-PMH server at hal.ccsd.cnrs.fr",
contributor = "Christian Gagn{\'e} ",
identifier = "inria-00000996 (version 1)",
oai = "oai:hal.ccsd.cnrs.fr:inria-00000996_v1",
keywords = "genetic algorithms, genetic programming, Computer
Science/Learning",
URL = "http://hal.inria.fr/inria-00000996/en/",
URL = "http://hal.ccsd.cnrs.fr/docs/00/05/44/78/PDF/gagne-paper.pdf",
URL = "http://arxiv.org/abs/cs/0601044",
abstract = "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.",
size = "12 pages",
}
@InProceedings{eurogp06:GagneSchoenauerParizeauTomassini,
author = "Christian Gagn\'e and Marc Schoenauer and Marc
Parizeau and Marco Tomassini",
title = "Genetic Programming, Validation Sets, and Parsimony
Pressure",
editor = "Pierre Collet and Marco Tomassini and Marc Ebner and
Steven Gustafson and Anik\'o Ek\'art",
booktitle = "Proceedings of the 9th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3905",
year = "2006",
address = "Budapest, Hungary",
month = "10 - 12 " # apr,
organisation = "EvoNet",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-33143-3",
pages = "109--120",
URL = "http://hal.ccsd.cnrs.fr/docs/00/05/44/78/PDF/gagne-paper.pdf",
URL = "http://hal.inria.fr/inria-00000996/en/",
URL = "http://link.springer.de/link/service/series/0558/papers/3905/39050109.pdf",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
notes = "Part of \cite{collet:2006:GP} EuroGP'2006 held in
conjunction with EvoCOP2006 and EvoWorkshops2006
Also known as
\cite{oai:hal.ccsd.cnrs.fr:inria-00000996_v1}
overfitting, regularisation, V-C dimension, MDL, UCI,
fit the noise.",
}
@Article{Gagne:2006:IJAIT,
author = "Christian Gagn\'e and Marc Parizeau",
title = "Genericity in Evolutionary Computation Software Tools:
Principles and Case Study",
journal = "International Journal on Artificial Intelligence
Tools",
year = "2006",
volume = "15",
number = "2",
pages = "173--194",
month = apr,
note = "22 pages",
keywords = "genetic algorithms, genetic programming, Evolutionary
computation, genetic algorithms, software engineering,
object oriented programming",
URL = "http://vision.gel.ulaval.ca/~parizeau/Publications/IJAIT06.pdf",
doi = "doi:10.1142/S021821300600262X",
abstract = "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.",
notes = "Laboratoire de Vision et Systemes Numeriques (LVSN),
Departement de Genie electrique et de Genie
Informatique, Universite Laval, Quebec (QC), Canada,
G1K 7P4, Canada",
}
@Article{gagne:2006:sigevo,
author = "Christian Gagn\'e and Marc Parizeau",
title = "Open {BEAGLE} {A} {C}++ Framework for your Favorite
Evolutionary Algorithm",
journal = "SIGEvolution",
year = "2006",
volume = "1",
number = "1",
pages = "12--15",
month = apr,
keywords = "genetic algorithms, genetic programming, CMA-ES,
NSGA-II, NSGA2, coevolution, onemax",
URL = "http://www.sigevolution.org/2006/01/issue.pdf",
}
@InProceedings{Gagne:PPSN:2006,
author = "Christian Gagne and Marc Schoenauer and Michele Sebag
and Marco Tomassini",
title = "Genetic Programming for Kernel-Based Learning with
Co-evolving Subsets Selection",
booktitle = "Parallel Problem Solving from Nature - PPSN IX",
year = "2006",
editor = "Thomas Philip Runarsson and Hans-Georg Beyer and
Edmund Burke and Juan J. Merelo-Guervos and L. Darrell
Whitley and Xin Yao",
volume = "4193",
pages = "1008--1017",
series = "LNCS",
address = "Reykjavik, Iceland",
publisher_address = "Berlin",
month = "9-13 " # sep,
publisher = "Springer-Verlag",
ISBN = "3-540-38990-3",
keywords = "genetic algorithms, genetic programming,
hyperheuristic, DSS, coevolution, open beagle",
URL = "http://ppsn2006.raunvis.hi.is/proceedings/287.pdf",
URL = "http://arxiv.org/abs/cs/0611135",
doi = "doi:10.1007/11844297_102",
size = "10 pages",
abstract = "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.",
notes = "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.",
}
@Article{Gagne:2006:ijDAR,
author = "Christian Gagne and Marc Parizeau",
title = "Genetic Engineering of Hierarchical Fuzzy Regional
Representations for Handwritten Character Recognition",
journal = "International Journal on Document Analysis and
Recognition",
year = "2006",
volume = "8",
number = "4",
pages = "223--231",
month = sep,
keywords = "genetic algorithms, genetic programming",
URL = "http://vision.gel.ulaval.ca/fr/publications/Id_607/PublDetails.php",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1007/s10032-005-0005-6",
abstract = "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.",
}
@Article{Gagne:2007:ijPRAI,
author = "Christian Gagn\'e and Marc Parizeau",
title = "Co-evolution of Nearest Neighbor Classifiers",
journal = "International Journal of Pattern Recognition and
Artificial Intelligence",
year = "2007",
volume = "21",
number = "5",
pages = "921--946",
month = aug,
keywords = "genetic algorithms, genetic programming",
ISSN = "0218-0014",
URL = "http://vision.gel.ulaval.ca/en/publications/Id_692/PublDetails.php",
doi = "doi:10.1142/S0218001407005752",
abstract = "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.",
}
@InProceedings{gaivoronski:1999:MCESAN,
author = "Alexei A. Gaivoronski",
title = "Modeling of Complex Economic Systems with Agent Nets",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1265--1272",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "artificial life, adaptive behavior and agents",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-041.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-041.pdf",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{conf/evoW/GajdaK08,
title = "Evolving a Vision-Driven Robot Controller for
Real-World Indoor Navigation",
author = "Pawel Gajda and Krzysztof Krawiec",
bibdate = "2008-04-15",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/evoW/evoW2008.html#GajdaK08",
booktitle = "Proceedings of Evo{COMNET}, Evo{FIN}, Evo{HOT},
Evo{IASP}, Evo{MUSART}, Evo{NUM}, Evo{STOC}, and
EvoTransLog, Applications of Evolutionary Computing,
EvoWorkshops",
publisher = "Springer",
year = "2008",
volume = "4974",
editor = "Mario Giacobini and Anthony Brabazon and Stefano
Cagnoni and Gianni {Di Caro} and Rolf Drechsler and
Anik{\'o} Ek{\'a}rt and Anna Esparcia-Alc{\'a}zar 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",
isbn13 = "978-3-540-78760-0",
pages = "184--193",
series = "Lecture Notes in Computer Science",
doi = "doi:10.1007/978-3-540-78761-7_19",
address = "Naples",
month = "26-28 " # mar,
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
}
@InProceedings{Gajda:2009:cec,
author = "Zbysek Gajda and Lukas Sekanina",
title = "Gate-Level Optimization of Polymorphic Circuits Using
Cartesian Genetic Programming",
booktitle = "2009 IEEE Congress on Evolutionary Computation",
year = "2009",
editor = "Andy Tyrrell",
pages = "1599--1604",
address = "Trondheim, Norway",
month = "18-21 " # may,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-2959-2",
file = "P186.pdf",
doi = "doi:10.1109/CEC.2009.4983133",
abstract = "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.",
keywords = "genetic algorithms, genetic programming, cartesian
genetic programming",
notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and
the IET. IEEE Catalog Number: CFP09ICE-CDR",
}
@InProceedings{Gajda:2010:gecco,
author = "Zbysek Gajda and Lukas Sekanina",
title = "When does Cartesian genetic programming minimize the
phenotype size implicitly?",
booktitle = "GECCO '10: Proceedings of the 12th annual conference
on Genetic and evolutionary computation",
year = "2010",
editor = "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",
isbn13 = "978-1-4503-0072-8",
pages = "983--984",
keywords = "genetic algorithms, genetic programming, Cartesian
genetic programming, Poster",
month = "7-11 " # jul,
organisation = "SIGEVO",
address = "Portland, Oregon, USA",
doi = "doi:10.1145/1830483.1830661",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{1830661} GECCO-2010 A joint
meeting of the nineteenth international conference on
genetic algorithms (ICGA-2010) and the fifteenth annual
genetic programming conference (GP-2010)",
}
@InProceedings{Gajda:2010:ICES,
author = "Zbysek Gajda and Lukas Sekanina",
title = "An Efficient Selection Strategy for Digital Circuit
Evolution",
booktitle = "Proceedings of the 9th International Conference
Evolvable Systems: From Biology to Hardware, ICES
2010",
year = "2010",
editor = "Gianluca Tempesti and Andy M. Tyrrell and Julian F.
Miller",
series = "Lecture Notes in Computer Science",
volume = "6274",
pages = "13--24",
address = "York",
month = sep # " 6-8",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, cartesian
genetic programming",
isbn13 = "978-3-642-15322-8",
doi = "doi:10.1007/978-3-642-15323-5_2",
size = "12 pages",
abstract = "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.",
}
@InProceedings{galeano:2002:stiosaapgople,
author = "G. Galeano and F. Fernandez and M. Tomassini and L.
Vanneschi",
title = "Studying the influence of Synchronous and Asynchronous
parallel {GP} on Programs' Length Evolution",
booktitle = "Proceedings of the 2002 Congress on Evolutionary
Computation CEC2002",
editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
and Garry Greenwood and Hitoshi Iba and Paul Marrow and
Mark Shackleton",
pages = "1727--1732",
year = "2002",
publisher = "IEEE Press",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
organisation = "IEEE Neural Network Council (NNC), Institution of
Electrical Engineers (IEE), Evolutionary Programming
Society (EPS)",
ISBN = "0-7803-7278-6",
month = "12-17 " # may,
notes = "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)",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
}
@InProceedings{gallagher:1999:EADNNAIVLSM,
author = "John C. Gallagher and Randall D. Beer",
title = "Evolution and Analysis of Dynamical Neural Networks
for Agents Integrating Vision, Locomotion, and
Short-Term Memory",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1273--1280",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "artificial life, adaptive behavior and agents",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-005.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-005.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{gallagher:1999:REOFPDE,
author = "Marcus Gallagher and Marcus Frean and Tom Downs",
title = "Real-valued Evolutionary Optimization using a Flexible
Probability Density Estimator",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "840--846",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "evolution strategies and evolutionary programming",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/gallagher_gecco99.ps.gz",
URL = "http://www.itee.uq.edu.au/~marcusg/papers/gallagher_gecco99.ps.gz",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{galos:2003:gecco,
author = "Peter Galos and Peter Nordin and Joel Ols{\'e}n and
Kristofer Sund{\'e}n Ringn{\'e}r",
title = "A General Approach to Automatic Programming Using
{O}ccam's Razor, Compression, and Self-Inspection",
booktitle = "Genetic and Evolutionary Computation -- GECCO-2003",
editor = "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",
year = "2003",
pages = "1806--1807",
address = "Chicago",
publisher_address = "Berlin",
month = "12-16 " # jul,
volume = "2724",
series = "LNCS",
ISBN = "3-540-40603-4",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, poster",
abstract = "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.",
notes = "GECCO-2003. A joint meeting of the twelfth
International Conference on Genetic Algorithms
(ICGA-2003) and the eighth Annual Genetic Programming
Conference (GP-2003)",
}
@Article{Gamage:2010:jcise,
author = "L. B. Gamage and C. W. {de Silva}",
title = "A System Framework With Online Monitoring and
Evaluation for Design Evolution of Engineering
Systems",
journal = "Journal of Computing and Information Science in
Engineering",
year = "2010",
volume = "10",
number = "3",
month = sep,
note = "Technical Briefs",
keywords = "genetic algorithms, genetic programming",
ISSN = "1530-9827",
doi = "doi:10.1115/1.3462919",
abstract = "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",
notes = "Industrial Automation Laboratory, Department of
Mechanical Engineering, The University of British
Columbia, Vancouver, BC, V6T 1Z4, Canada
American Society of Mechanical Engineers",
}
@InProceedings{Gan:2009:ICNC,
author = "Zhaohui Gan and Tao Shang and Gang Shi and Min Jiang",
title = "Evolutionary Design of Combinational Logic Circuits
Using an Improved Gene Expression-Based Clonal
Selection Algorithm",
booktitle = "Fifth International Conference on Natural Computation,
ICNC '09",
year = "2009",
month = aug,
volume = "4",
pages = "37--41",
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/ICNC.2009.308",
notes = "Also known as \cite{5365151}",
}
@Article{Gan20093996,
author = "Zhaohui Gan and Tommy W. S. Chow and W. N. Chau",
title = "Clone selection programming and its application to
symbolic regression",
journal = "Expert Systems with Applications",
volume = "36",
number = "2, Part 2",
pages = "3996--4005",
year = "2009",
ISSN = "0957-4174",
doi = "DOI:10.1016/j.eswa.2008.02.030",
URL = "http://www.sciencedirect.com/science/article/B6V03-4S02048-9/2/d5a34ad92d4cf0f6f5e33f4407a2776f",
keywords = "genetic algorithms, genetic programming, gene
expression programming, Clone selection, Programming,
Immune system, Gene expression",
abstract = "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.",
}
@Article{Gan20101887,
author = "Zhaohui Gan and Zhenkun Yang and Tao Shang and Tianyou
Yu and Min Jiang",
title = "Automated synthesis of passive analog filters using
graph representation",
journal = "Expert Systems with Applications",
volume = "37",
number = "3",
pages = "1887--1898",
year = "2010",
ISSN = "0957-4174",
doi = "doi:10.1016/j.eswa.2009.07.013",
URL = "http://www.sciencedirect.com/science/article/B6V03-4WXHBSP-5/2/32ead9142a06172b08c290d1ce58b362",
keywords = "genetic algorithms, genetic programming, Analog
passive filter synthesis, Automatic design, Clone
selection algorithm, Graph-based encoding scheme",
abstract = "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.",
}
@Article{Gandomi2008338,
author = "A. H. Gandomi and A. H. Alavi and S. S. Sadat
Hosseini",
title = "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]",
journal = "Applied Ocean Research",
volume = "30",
number = "4",
pages = "338--339",
year = "2008",
ISSN = "0141-1187",
doi = "doi:10.1016/j.apor.2009.02.001",
URL = "http://www.sciencedirect.com/science/article/B6V1V-4VXJVY5-1/2/f5aca485c623afab39556b3979e70bff",
keywords = "genetic algorithms, genetic programming, Linear
structure, Wave height",
size = "2 pages",
notes = "Discussion of \cite{Kalra200799}. See also reply
\cite{Deo2008340}",
abstract = "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.",
}
@Article{Gandomi20091738,
author = "A. H. Gandomi and A. H. Alavi and S. Kazemi and M. M.
Alinia",
title = "Behavior appraisal of steel semi-rigid joints using
Linear Genetic Programming",
journal = "Journal of Constructional Steel Research",
volume = "65",
number = "8-9",
pages = "1738--1750",
year = "2009",
ISSN = "0143-974X",
doi = "doi:10.1016/j.jcsr.2009.04.010",
URL = "http://www.sciencedirect.com/science/article/B6V3T-4W8KHNW-4/2/4833ff184048303a27710677ee1f047f",
keywords = "genetic algorithms, genetic programming, Semi-rigid
joints, Steel structures",
abstract = "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.",
}
@Article{Gandomi:2010:jMST,
author = "Amir Hossein Gandomi and Amir Hossein Alavi and
Mohammad Ghasem Sahab and Parvin Arjmandi",
title = "Formulation of elastic modulus of concrete using
linear genetic programming",
journal = "Journal of Mechanical Science and Technology",
year = "2010",
volume = "24",
number = "6",
pages = "1273--1278",
month = jun,
email = "ah_alavi@hotmail.com, a.h.gandomi@gmail.com",
keywords = "genetic algorithms, genetic programming, Tangent
elastic modulus, Linear genetic programming,
Compressive strength, Normal and high strength
concrete, Formulation",
ISSN = "1738-494X",
URL = "http://www.springerlink.com/content/h0m3414774224425/",
doi = "doi:10.1007/s12206-010-0330-7",
size = "6 pages",
abstract = "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.",
notes = "1Structural Health Monitoring Research Group, College
of Civil Engineering, Tafresh University, Tafresh,
Iran",
}
@Article{Gandomi:2010:MS,
author = "Amir Hossein Gandomi and Amir Hossein Alavi and
Mohammad Ghasem Sahab",
title = "New formulation for compressive strength of {CFRP}
confined concrete cylinders using linear genetic
programming",
journal = "Materials and Structures",
year = "2010",
volume = "43",
number = "7",
pages = "963--983",
month = aug,
email = "ah_alavi@hotmail.com, a.h.gandomi@gmail.com",
keywords = "CFRP confinement, Linear genetic programming,
Formulation, Concrete compressive strength",
ISSN = "1359-5997",
doi = "doi:10.1617/s11527-009-9559-y",
size = "21 pages",
abstract = "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.",
notes = "College of Civil Engineering, Tafresh University,
Tafresh, Iran",
}
@Article{Gandomi:2010:JMCE,
author = "Amir Hossein Gandomi and Amir Hossein Alavi and
Mohammad Reza Mirzahosseini and Fereidoon Moghadas
Nejad",
title = "Nonlinear Genetic-Based Models for Prediction of Flow
Number of Asphalt Mixtures",
journal = "ASCE Journal of Materials in Civil Engineering",
year = "2011",
volume = "23",
number = "3",
pages = "248--263",
month = mar,
email = "ah_alavi@hotmail.com, a.h.gandomi@gmail.com",
keywords = "genetic algorithms, genetic programming, gene
expression programming, Marshall mix design,
Formulation",
URL = "http://ascelibrary.org/mto/resource/1/jmcee7/v23/i3/p248_s1",
doi = "doi:10.1061/(ASCE)MT.1943-5533.0000154",
size = "16 pages",
abstract = "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.",
notes = "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.",
}
@Article{Gandomi20111096,
author = "Amir Hossein Gandomi and Seyed Morteza Tabatabaei and
Mohammad Hossein Moradian and Ata Radfar and Amir
Hossein Alavi",
title = "A new prediction model for the load capacity of
castellated steel beams",
journal = "Journal of Constructional Steel Research",
volume = "67",
number = "7",
pages = "1096--1105",
year = "2011",
ISSN = "0143-974X",
doi = "doi:10.1016/j.jcsr.2011.01.014",
URL = "http://www.sciencedirect.com/science/article/B6V3T-52BVR2R-1/2/9f40e5717143288037afed5176f8d52e",
keywords = "genetic algorithms, genetic programming, Castellated
beam, Failure load, Gene expression programming",
abstract = "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.",
}
@Article{Gandomi2011717,
author = "Amir Hossein Gandomi and Amir Hossein Alavi and Mehdi
Mousavi and Seyed Morteza Tabatabaei",
title = "A hybrid computational approach to derive new
ground-motion prediction equations",
journal = "Engineering Applications of Artificial Intelligence",
volume = "24",
number = "4",
pages = "717--732",
year = "2011",
ISSN = "0952-1976",
doi = "doi:10.1016/j.engappai.2011.01.005",
URL = "http://www.sciencedirect.com/science/article/B6V2M-52C83TR-1/2/0e8d2ec5097e6a0e7eef643a7e26d527",
keywords = "genetic algorithms, genetic programming, Time-domain
ground-motion parameters, Prediction equations,
Orthogonal least squares, Nonlinear modelling",
abstract = "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.",
}
@InProceedings{gang:2004:eurogp,
author = "Wang Gang and Terence Soule",
title = "How to Choose Appropriate Function Sets for {GP}",
booktitle = "Genetic Programming 7th European Conference, EuroGP
2004, Proceedings",
year = "2004",
editor = "Maarten Keijzer and Una-May O'Reilly and Simon M.
Lucas and Ernesto Costa and Terence Soule",
volume = "3003",
series = "LNCS",
pages = "198--207",
address = "Coimbra, Portugal",
publisher_address = "Berlin",
month = "5-7 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-21346-5",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=198",
abstract = "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.",
notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in
conjunction with EvoCOP2004 and EvoWorkshops2004",
}
@InProceedings{conf/icnc/GaoGCLG09,
title = "Cyberspace Situation Prediction Based on Gene
Expression Programming",
author = "HongLei Gao and WenZhong Guo and GuoLong Chen and
YanHua Liu and Mei Gao",
booktitle = "Fifth International Conference on Natural Computation,
2009. ICNC '09",
year = "2009",
editor = "Haiying Wang and Kay Soon Low and Kexin Wei and
Junqing Sun",
month = "14-16 " # aug,
address = "Tianjian, China",
publisher = "IEEE Computer Society",
isbn13 = "978-0-7695-3736-8",
keywords = "genetic algorithms, genetic programming, gene
expression programming",
pages = "191--195",
bibdate = "2010-01-22",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/icnc/icnc2009-4.html#GaoGCLG09",
doi = "doi:10.1109/ICNC.2009.42",
abstract = "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.",
}
@Article{Gao:2001:CCE,
author = "Li Gao and Norman W. Loney",
title = "Evolutionary polymorphic neural network in chemical
process modeling",
journal = "Computers \& Chemical Engineering",
year = "2001",
volume = "25",
pages = "1403--1410",
number = "11-12",
keywords = "genetic algorithms, genetic programming, Evolutionary
polymorphic neural network (EPNN), Neural network,
Process modeling",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6TFT-449TFB0-2/2/b9c50f18933d4b739a9d8a2843b45548",
ISSN = "0098-1354",
doi = "doi:10.1016/S0098-1354(01)00708-6",
abstract = "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.",
}
@PhdThesis{LiGao:thesis,
author = "Li Gao",
title = "Evolutionary Polymorphic Neural Networks in Chemical
Engineering Modeling",
school = "Department of Chemical Engineering, New Jersey
Institute of Technology",
year = "2001",
month = aug,
keywords = "genetic algorithms, genetic programming, Evolutionary
Polymorphic Neural Network (EPNN), Artificial
intelligence, Evolutionary computing",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/LiGao_thesis.pdf",
size = "146 pages",
abstract = "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.",
notes = "Advisory Committee: Loney, Norman W. Baltzis, Basil C.
Barat, Robert B. Knox, Dana E. Blackmore, Denis Wasser,
Daniel J.",
}
@InProceedings{Gao:2006:icirs,
author = "Xueshan Gao and Koki Kikuchi and Xiaobing Wu and
Katsuya Kanai and Keisuke Somiya",
title = "Study on the Symmetry of Evolutionary Robotic System",
booktitle = "2006 IEEE/RSJ International Conference on Intelligent
Robots and Systems",
year = "2006",
pages = "1638--1643",
address = "Beijing",
month = oct,
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-4244-0259-X",
doi = "doi:10.1109/IROS.2006.282055",
abstract = "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",
notes = "Sch. of Mechatronic Eng., Beijing Inst. of Technol.",
}
@InProceedings{garces-perez:1996:sflp,
author = "Jaime Garces-Perez and Dale A. Schoenefeld and Roger
L. Wainwright",
title = "Solving Facility Layout Problems Using Genetic
Programming",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "182--190",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
URL = "http://euler.utulsa.edu/~rogerw/papers/Garces-Perez-flp.pdf",
size = "9 pages",
abstract = "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",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96",
}
@InProceedings{Garcia:2008:gecco,
author = "Beatriz Garcia and Ricardo Aler and Agapito Ledezma
and Araceli Sanchis",
title = "Protein-protein functional association prediction
using genetic programming",
booktitle = "GECCO '08: Proceedings of the 10th annual conference
on Genetic and evolutionary computation",
year = "2008",
editor = "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",
isbn13 = "978-1-60558-130-9",
pages = "347--348",
address = "Atlanta, GA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p347.pdf",
doi = "doi:10.1145/1389095.1389156",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "12-16 " # jul,
abstract = "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.",
keywords = "genetic algorithms, genetic programming,
bioinformatics, classifier systems, control bloat, data
integration, evolutionary computation, machine
learning, protein interaction prediction, computational
biology: Poster",
notes = "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
\cite{1389156}
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 \cite{poli03} effective.",
}
@InProceedings{DBLP:conf/iberamia/GarciaALS08,
author = "Beatriz Garcia and Ricardo Aler and Agapito Ledezma
and Araceli Sanchis",
title = "Genetic Programming for Predicting Protein Networks",
booktitle = "Proceedings of the 11th Ibero-American Conference on
AI, IBERAMIA 2008",
year = "2008",
editor = "Hector Geffner and Rui Prada and Isabel Machado
Alexandre and Nuno David",
volume = "5290",
series = "Lecture Notes in Computer Science",
pages = "432--441",
address = "Lisbon, Portugal",
month = oct # " 14-17",
publisher = "Springer",
note = "Advances in Artificial Intelligence",
keywords = "genetic algorithms, genetic programming, Protein
interaction prediction, data integration,
bioinformatics, evolutionary computation, machine
learning, classification, control bloat",
isbn13 = "978-3-540-88308-1",
URL = "http://www.caos.inf.uc3m.es/~beatriz/papers/garcia_et.al._iberamia08-paper_InPress.pdf",
doi = "doi:10.1007/978-3-540-88309-8_44",
size = "10 pages",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
notes = "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.",
}
@InCollection{garcia:2000:EMFFASGA,
author = "Guillermo Garcia",
title = "Estimation of Multiple Fundamental Frequencies in
Audio Signals using a Genetic Algorithm",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2000",
year = "2000",
editor = "John R. Koza",
pages = "153--159",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms",
size = "7 pages",
notes = "part of \cite{koza:2000:gagp}",
}
@InProceedings{oai:CiteSeerPSU:454347,
author = "Ricardo A. Garcia",
title = "Towards the Automatic Generation of Sound Synthesis
Techniques: Preparatory Steps",
booktitle = "AES 109th Convention",
year = "2000",
address = "Los Angeles",
month = "22-25 Sepetember",
organisation = "Audio Engineering Society",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.ragomusic.com/publications/ragoAES2000.pdf",
URL = "http://citeseer.ist.psu.edu/454347.html",
citeseer-isreferencedby = "oai:CiteSeerPSU:66836",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:454347",
rights = "unrestricted",
size = "7 pages",
abstract = "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.",
notes = "http://www.aes.org/events/109/",
}
@InProceedings{oai:CiteSeerPSU:569030,
author = "Ricardo A. Garcia",
title = "Automating The Design Of Sound Synthesis Techniques
Using Evolutionary Methods",
booktitle = "Proceedings of the COST G-6 Conference on Digital
Audio Effects (DAFX-01)",
year = "2001",
editor = "Mikael Fernstrom",
address = "Limerick, Ireland",
month = dec # " 6-8",
keywords = "genetic algorithms, genetic programming",
citeseer-isreferencedby = "oai:CiteSeerPSU:97342",
citeseer-references = "oai:CiteSeerPSU:32686; oai:CiteSeerPSU:286517",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:569030",
rights = "unrestricted",
URL = "http://www.csis.ul.ie/dafx01/proceedings/navig/../papers/garcia.pdf",
URL = "http://citeseer.ist.psu.edu/569030.html",
abstract = "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).",
notes = "http://www.csis.ul.ie/dafx01/programme.html",
}
@InProceedings{oai:RePEc:sce:scecfa:489,
author = "Alma Lilia Garcia-Almanza and Edward P. K. Tsang",
title = "Forecasting stock prices using Genetic Programming and
Chance Discovery",
booktitle = "12th International Conference On Computing In
Economics And Finance",
year = "2006",
pages = "number 489",
month = jul,
organisation = "Society for Computational Economics",
bibsource = "OAI-PMH server at oai.repec.openlib.org",
description = "Forecasting, Chance discovery, Genetic programming,
machine learning",
identifier = "RePEc:sce:scecfa:489",
oai = "oai:RePEc:sce:scecfa:489",
keywords = "genetic algorithms, genetic programming",
URL = "http://repec.org/sce2006/up.13879.1141401469.pdf",
URL = "http://privatewww.essex.ac.uk/~algarc/Publications/CEF2006.pdf",
URL = "http://ideas.repec.org/p/sce/scecfa/489.html",
abstract = "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.",
notes = "CEF 2006",
}
@InProceedings{Garcia-Almanza_2006_CEC,
author = "Alma Lilia Garcia-Almanza and Edward P. K. Tsang",
title = "Simplifying Decision Trees Learned by Genetic
Programming",
booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary
Computation",
year = "2006",
pages = "7906--7912",
address = "Vancouver",
month = "6-21 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-9487-9",
URL = "http://privatewww.essex.ac.uk/~algarc/Publications/WCCI2006.pdf",
size = "7 pages",
abstract = "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.",
notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
the IEE.",
}
@InProceedings{Garcia:2006a,
author = "Alma L Garcia-Almanza and Edward P. K. Tsang",
title = "The Repository Method for Chance Discovery in
Financial Forecasting",
ISSN = "0302-9743",
year = "2006",
editor = "Bogdan Gabrys and Robert J. Howlett and Lakhmi C.
Jain",
series = "Lecture Notes in Computer Science",
volume = "4253",
booktitle = "KES 2006, Proceedings of the 10th International
Conference on Knowledge-Based Intelligent Information
and Engineering Systems",
pages = "30--37",
address = "Bournemouth, UK",
month = oct # " 9-11",
publisher = "Springer-Verlag",
note = "Part III",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-46542-1",
bibsource = "DBLP, http://dblp.uni-trier.de",
doi = "doi:10.1007/11893011_5",
abstract = "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.",
}
@InProceedings{Garcia-Almanza:2007:cec,
author = "Alma Lilia Garcia-Almanza and Edward P. K. Tsang",
title = "Repository Method to Suit Different Investment
Strategies",
booktitle = "2007 IEEE Congress on Evolutionary Computation",
year = "2007",
editor = "Dipti Srinivasan and Lipo Wang",
pages = "790--797",
address = "Singapore",
month = "25-28 " # sep,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "1-4244-1340-0",
file = "1986.pdf",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and
the IET.
IEEE Catalog Number: 07TH8963C",
}
@Article{journals/kes/Garcia-AlmanzaT07,
author = "Alma Lilia Garcia-Almanza and Edward P. K. Tsang",
title = "Detection of stock price movements using chance
discovery and genetic programming",
journal = "International Journal of Knowledge-Based and
Intelligent Engineering Systems",
year = "2007",
volume = "11",
number = "5",
pages = "329--344",
publisher = "IOS",
keywords = "genetic algorithms, genetic programming",
ISSN = "1327-2314",
URL = "http://iospress.metapress.com/content/k30kgl00u6r42812/",
size = "16 pages",
abstract = "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.",
bibdate = "2008-08-06",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/kes/kes11.html#Garcia-AlmanzaT07",
}
@Article{Garcia:2008,
author = "Alma Lilia Garcia-Almanza and Edward P. K. Tsang",
title = "Evolving Decision Rules to Predict Investment
Opportunities",
journal = "International Journal of Automation and Computing",
year = "2008",
volume = "5",
number = "1",
pages = "22--31",
month = jan,
keywords = "genetic algorithms, genetic programming, Machine
learning, classification, imbalanced classes, evolution
of rules",
publisher = "Institute of Automation, Chinese Academy of Sciences,
co-published with Springer-Verlag GmbH",
ISSN = "1476-8186",
doi = "doi:10.1007/s11633-008-0022-2",
size = "10 pages",
abstract = "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.",
affiliation = "University of Essex Department of Computer Science
Wivenhoe Park Colchester CO4 3SQ UK",
}
@PhdThesis{Garcia-Almanza:thesis,
author = "Alma Lilia {Garcia Almanza}",
title = "New Classification Methods for Gathering Patterns in
the Context of Genetic Programming",
school = "Department of Computing and Electronic Systems,
University of Essex",
year = "2008",
address = "Colchester, UK",
month = jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.bracil.net/finance/papers/Garcia-PhD2008.pdf",
size = "244 pages",
abstract = "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.",
}
@InProceedings{Garcia-Almanza:2010:CERMA,
author = "Alma Lilia Garcia-Almanza and Biliana
Alexandrova-Kabadjova and Serafin Martinez-Jaramillo",
title = "Understanding Bank Failure: {A} Close Examination of
Rules Created by Genetic Programming",
booktitle = "Electronics, Robotics and Automotive Mechanics
Conference (CERMA), 2010",
year = "2010",
month = "28 " # sep # "-" # oct # " 1",
pages = "34--39",
abstract = "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.",
keywords = "genetic algorithms, genetic programming, bank failure
detection, bankruptcy prediction, data set, evolving
decision rules, financial ratio, receiving operating
characteristic space, banking, sensitivity analysis",
doi = "doi:10.1109/CERMA.2010.14",
notes = "Also known as \cite{5692308}",
}
@Book{Garcia-Almanza:book,
author = "Alma Lilia {Garcia Almanza} and Edward Tsang",
title = "Evolutionary Applications for Financial Prediction:
Classification Methods to Gather Patterns Using Genetic
Programming",
publisher = "VDM Verlag Dr. Muller",
year = "2011",
address = "Saarbrucken, Germany",
keywords = "genetic algorithms, genetic programming",
ISBN = "3639307674",
URL = "http://www.bracil.net/finance/GarciaTsang-book2011/",
URL = "http://www.amazon.com/Evolutionary-Applications-Financial-Prediction-Classification/dp/3639307674/ref=sr_1_1?ie=UTF8&qid=1305383401&sr=8-1",
abstract = "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.",
size = "172 pages",
}
@InCollection{Garcia-Almanza:2011:Yap,
author = "Alma Lilia {Garcia Almanza} and Serafin {Martinez
Jaramillo} and Biliana Alexandrova-Kabadjova and Edward
Tsang",
title = "Using Genetic Programming Systems as Early Warning to
Prevent Bank Failure",
booktitle = "Information Systems for Global Financial Markets:
Emerging Developments and Effects",
publisher = "IGI global",
year = "2011",
editor = "Alexander Y. Yap",
chapter = "14",
pages = "369--382",
month = nov,
keywords = "genetic algorithms, genetic programming",
ISBN = "1-61350-162-5",
URL = "http://www.amazon.com/Information-Systems-Global-Financial-Markets/dp/1613501625",
doi = "doi:10.4018/978-1-61350-162-7.ch014",
abstract = "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",
}
@Article{Garcia-Arnau:2007:KBS,
author = "M. Garcia-Arnau and D. Manrique and J. Rios and A.
Rodriguez-Paton",
title = "Initialization method for grammar-guided genetic
programming",
journal = "Knowledge-Based Systems",
year = "2007",
volume = "20",
number = "2",
pages = "127--133",
month = mar,
note = "AI 2006, The 26th SGAI International Conference on
Innovative Techniques and Applications of Artificial
Intelligence",
keywords = "genetic algorithms, genetic programming,
Grammar-guided genetic programming, Initialisation
method, Tree-generation algorithm, Breast cancer
prognosis, GGGP",
doi = "doi:10.1016/j.knosys.2006.11.006",
abstract = "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.",
}
@InProceedings{Garcia-Sanchez:2008:gecco,
author = "P. Garcia-Sanchez and J. J. Merelo and J. P. Sevilla
and J. L. J. Laredo and A. M. Mora and P. A. Castillo",
title = "Automatic generation of {XSLT} stylesheets using
evolutionary algorithms",
booktitle = "GECCO '08: Proceedings of the 10th annual conference
on Genetic and evolutionary computation",
year = "2008",
editor = "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",
isbn13 = "978-1-60558-130-9",
pages = "1701--1702",
address = "Atlanta, GA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1701.pdf",
doi = "doi:10.1145/1389095.1389417",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "12-16 " # jul,
keywords = "genetic algorithms, genetic programming, evolutionary
computation techniques, style sheets, XML, XSLT,
Real-World application: Poster",
notes = "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 \cite{1389417}",
}
@InProceedings{Garcia-Sanchez:2008:PPSN,
author = "Pablo Garcia-Sanchez and Juan J. Merelo and Juan L. J.
Laredo and Antonio Mora and Pedro A. Castillo",
title = "Evolving {XSLT} Stylesheets for Document
Transformation",
booktitle = "Parallel Problem Solving from Nature - PPSN X",
year = "2008",
editor = "Gunter Rudolph and Thomas Jansen and Simon Lucas and
Carlo Poloni and Nicola Beume",
volume = "5199",
series = "LNCS",
pages = "1021--1030",
address = "Dortmund",
month = "13-17 " # sep,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-87699-5",
doi = "doi:10.1007/978-3-540-87700-4_101",
size = "pages",
abstract = "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.",
notes = "PPSN X",
}
@InProceedings{Gardner:2011:GECCOcomp,
author = "Marc-Andre Gardner and Christian Gagne and Marc
Parizeau",
title = "Bloat control in genetic programming with a
histogram-based accept-reject method",
booktitle = "GECCO '11: Proceedings of the 13th annual conference
companion on Genetic and evolutionary computation",
year = "2011",
editor = "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",
isbn13 = "978-1-4503-0690-4",
keywords = "genetic algorithms, genetic programming: Poster",
pages = "187--188",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001858.2001963",
publisher = "ACM",
publisher_address = "New York, NY, USA",
size = "2 pages",
abstract = "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.",
notes = "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 \cite{2001963} Distributed on CD-ROM at
GECCO-2011.
ACM Order Number 910112.",
}
@InProceedings{gargano:1998:GAfssmsttec,
author = "Michael L. Gargano and William Edelson and Olga
Koval",
title = "A Genetic Algorithm With Feasible Search Space For
Minimal Spanning Trees With Time-Dependent Edge Costs",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "495",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms",
notes = "SGA-98",
}
@Article{Garibay:2006:GPEM,
author = "Ivan Garibay and Annie S. Wu and Ozlem Garibay",
title = "Emergence of genomic self-similarity in location
independent representations Favoring positive
correlation between the form and quality of candidate
solutions",
journal = "Genetic Programming and Evolvable Machines",
year = "2006",
volume = "7",
number = "1",
pages = "55--80",
month = mar,
keywords = "genetic algorithms, Representation, Proportional
genetic algorithm, Self-organisation, Genomic
self-similarity, Emergence",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-006-7011-4",
size = "26 pages",
abstract = "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.",
notes = "white noise",
}
@Article{Garibay:2010:GPEM,
author = "Ivan Garibay",
title = "Dario Floreano and Claudio Mattiussi (eds):
Bio-inspired artificial intelligence: theories,
methods, and technologies",
journal = "Genetic Programming and Evolvable Machines",
year = "2010",
volume = "11",
number = "3/4",
pages = "441--443",
month = sep,
note = "Book review",
keywords = "genetic algorithms, genetic programming",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-010-9104-3",
size = "3 pages",
notes = "See Erratum \cite{Garibay:2011:GPEM}",
}
@Article{Garibay:2011:GPEM,
author = "Ivan Garibay",
title = "Erratum to: Dario Floreano and Claudio Mattiussi:
Bio-inspired artificial intelligence: theories,
methods, and technologies",
journal = "Genetic Programming and Evolvable Machines",
year = "2011",
volume = "12",
number = "1",
pages = "89--89",
month = mar,
keyword = "Computer Science",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-010-9123-0",
abstract = "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.",
notes = "Correction to \cite{Garibay:2010:GPEM}",
affiliation = "University of Central Florida, Orlando, FL USA",
}
@InProceedings{garmendia-doval:1998:etrsf,
author = "A. Beatriz Garmendia-Doval and Chilukuri K. Mohan and
Mohit K. Prasad",
title = "Evolving Tree Representations of Stack Filters",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "103--108",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-548-7",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/24116/http:zSzzSzwww.scms.rgu.ac.ukzSzstaffzSzbgdzSzGP98.pdf/garmendia-doval98evolving.pdf",
URL = "http://citeseer.ist.psu.edu/494447.html",
size = "6 pages",
abstract = "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.",
notes = "GP-98",
}
@InProceedings{garmendia-doval:2003:EA,
author = "A. Beatriz Garmendia-Doval and S. David Morley and
Szilveszter Juhos",
title = "Post Docking Filtering Using Cartesian Genetic
Programming",
booktitle = "Evolution Artificielle, 6th International Conference",
year = "2003",
editor = "Pierre Liardet and Pierre Collet and Cyril Fonlupt and
Evelyne Lutton and Marc Schoenauer",
volume = "2936",
series = "Lecture Notes in Computer Science",
pages = "189--200",
address = "Marseilles, France",
month = "27-30 " # oct,
publisher = "Springer",
note = "Revised Selected Papers",
keywords = "genetic algorithms, genetic programming, Artificial
Evolution, Cartesian Genetic Programming",
ISBN = "3-540-21523-9",
doi = "doi:10.1007/b96080",
abstract = "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.",
bibsource = "DBLP, http://dblp.uni-trier.de",
notes = "EA'03
HSP90 data.
RiboTargets Ltd, Granta Park, Cambridge, England, CB1
6GB",
}
@InCollection{garmendia-doval:2004:GPTP,
author = "A. Beatriz Garmendia-Doval and Julian Miller and S.
David Morley",
title = "Post Docking Filtering Using Cartesian Genetic
Programming",
booktitle = "Genetic Programming Theory and Practice {II}",
year = "2004",
editor = "Una-May O'Reilly and Tina Yu and Rick L. Riolo and
Bill Worzel",
chapter = "14",
pages = "225--244",
address = "Ann Arbor",
month = "13-15 " # may,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, cartesian
genetic programming, molecular docking prediction,
virtual screening, machine learning, evolutionary
algorithms, neutral evolution",
ISBN = "0-387-23253-2",
doi = "doi:10.1007/0-387-23254-0_14",
abstract = "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.",
notes = "part of \cite{oreilly:2004:GPTP2}",
}
@InProceedings{Garzon:1997:mDNAc,
author = "M. Garzon and P. Neathery and R. Deaton and R. C.
Murphy and D. R. Franschetti and S. E. {Stevens Jr.}",
title = "A New Metric for {DNA} Computing",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "DNA Computing",
pages = "472--478",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
notes = "GP-97",
}
@InProceedings{garzon:1999:OSG,
author = "Max H. Garzon and Russell J. Deaton and Ken Barnes",
title = "On Self-Assembling Graphs in vitro",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1805--1809",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "dna and molecular computing",
ISBN = "1-55860-611-4",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{garzon1998:egDNAc,
author = "Max Garzon and Rusell Deaton and Luis F. Nino and Ed
Stevens and Michal Wittner",
title = "Encoding Genomes for {DNA} Computing",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "684--690",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "DNA Computing",
ISBN = "1-55860-548-7",
URL = "http://www.csce.uark.edu/~rdeaton/dna/papers/gp98c-2.pdf",
notes = "GP-98",
}
@Article{garzon:2003:GPEMe,
author = "Max H. Garzon",
title = "Biomolecular Machines and Artificial Evolution",
journal = "Genetic Programming and Evolvable Machines",
year = "2003",
volume = "4",
number = "2",
pages = "107--109",
month = jun,
keywords = "DNA computing",
ISSN = "1389-2576",
doi = "doi:10.1023/A:1023960327580",
notes = "Special Issue on Biomolecular Machines and Artificial
Evolution Article ID: 5122739",
}
@Article{garzon:2003:GPEM,
author = "Max Garzon and Derrel Blain and Kiran Bobba and Andrew
Neel and Michael West",
title = "Self-Assembly of {DNA}-like Structures In Silico",
journal = "Genetic Programming and Evolvable Machines",
year = "2003",
volume = "4",
number = "2",
pages = "185--200",
month = jun,
keywords = "Hamiltonian path problem, online genetic algorithms,
DNA-based associative memories, efficiency of DNA
computing, reaction kinetics in DNA-based computational
protocols",
ISSN = "1389-2576",
doi = "doi:10.1023/A:1023989130306",
abstract = "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.",
notes = "Special Issue on Biomolecular Machines and Artificial
Evolution Article ID: 5122745",
}
@Unpublished{gathercole:1994:stss,
author = "Chris Gathercole and Peter Ross",
title = "Some Training Subset Selection Methods for Supervised
Learning in Genetic Programming",
note = "Presented at ECAI'94 Workshop on Applied Genetic and
other Evolutionary Algorithms",
year = "1994",
keywords = "genetic algorithms, genetic programming, LEF, DSS",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/733/ftp:zSzzSzftp.dai.ed.ac.ukzSzpubzSzuserzSzchrisgzSzchrisg_dss_paper_resubmitted_to_ecai94workshop.pdf/gathercole94some.pdf",
URL = "http://citeseer.ist.psu.edu/gathercole94some.html",
abstract = "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...",
size = "13 pages",
}
@InProceedings{ga94aGathercole,
author = "Chris Gathercole and Peter Ross",
title = "Dynamic Training Subset Selection for Supervised
Learning in Genetic Programming",
booktitle = "Parallel Problem Solving from Nature III",
year = "1994",
editor = "Yuval Davidor and Hans-Paul Schwefel and Reinhard
M{\"a}nner",
series = "LNCS",
volume = "866",
pages = "312--321",
address = "Jerusalem",
publisher_address = "Berlin, Germany",
month = "9-14 " # oct,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-58484-6",
URL = "http://citeseer.ist.psu.edu/gathercole94dynamic.html",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/94-006.ps.gz",
doi = "doi:10.1007/3-540-58484-6_275",
size = "10 pages",
abstract = "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.",
notes = "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.",
}
@TechReport{Gathercole,
author = "Chris Gathercole and Peter Ross",
title = "The {MAX} Problem for Genetic Programming -
Highlighting an Adverse Interaction between the
Crossover Operator and a Restriction on Tree Depth",
institution = "Department of Artificial Intelligence, University of
Edinburgh",
year = "1995",
address = "80 South Bridge, Edinburgh, EH1 1HN, UK
",
keywords = "genetic algorithms, genetic programming",
broken = "ftp://ftp.dai.ed.ac.uk/pub/user/chrisg/max-problem-in-GP.for_submission_to_gp-96.ps.gz",
URL = "http://citeseer.ist.psu.edu/gathercole95max.html",
abstract = "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...",
size = "10 pages",
notes = "
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 \cite{Gathercole:1996:aicrtd}",
}
@InProceedings{Gathercole:1996:aicrtd,
author = "Chris Gathercole and Peter Ross",
title = "An Adverse Interaction between Crossover and
Restricted Tree Depth in Genetic Programming",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "291--296",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
broken = "ftp://ftp.dai.ed.ac.uk/pub/user/chrisg/chrisg_max-problem-in-GP_camera-ready-version.for-GP-96.ps.gz",
URL = "http://citeseer.ist.psu.edu/153919.html",
size = "6 pages",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "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",
}
@InProceedings{Gathercole:1997:sp,
author = "Chris Gathercole and Peter Ross",
title = "Small Populations over Many Generations can beat Large
Populations over Few Generations in Genetic
Programming",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "111--118",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
URL = "http://citeseer.ist.psu.edu/189252.html",
size = "8 pages",
notes = "GP-97 slides at
http://www.dai.ed.ac.uk/students/chrisg/gp97/small_pops/slides.html
tictactoe, noughts and crosses, uci thyroid",
}
@InProceedings{Gathercole:1997:lef,
author = "Chris Gathercole and Peter Ross",
title = "Tackling the {Boolean} Even {N} Parity Problem with
Genetic Programming and Limited-Error Fitness",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "119--127",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
broken = "ftp://ftp.dai.ed.ac.uk/pub/user/chrisg/chrisg_for_public_gp97_lef.ps.gz",
URL = "http://citeseer.ist.psu.edu/79389.html",
notes = "GP-97 slides at
http://www.dai.ed.ac.uk/students/chrisg/gp97/lef/slides.html",
}
@PhdThesis{gathercole:thesis,
author = "Chris Gathercole",
title = "An Investigation of Supervised Learning in Genetic
Programming",
school = "University of Edinburgh",
year = "1998",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.era.lib.ed.ac.uk/dspace/bitstream/1842/533/3/Gathercole.pdf",
URL = "http://www.dai.ed.ac.uk/pub/daidb/papers/pt9810.ps.gz",
URL = "http://hdl.handle.net/1842/533",
size = "207 pages",
abstract = "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.
",
}
@Article{Gatlin1963360,
author = "L. L. Gatlin",
title = "Triplet frequencies in {DNA} and the genetic program",
journal = "Journal of Theoretical Biology",
volume = "5",
number = "3",
pages = "360--371",
year = "1963",
ISSN = "0022-5193",
doi = "doi:10.1016/0022-5193(63)90083-3",
URL = "http://www.sciencedirect.com/science/article/B6WMD-4F1J81C-T5/2/12c96a984135797062556122da338822",
notes = "Not on GP",
}
@Article{Gaur20081166,
author = "Surabhi Gaur and M. C. Deo",
title = "Real-time wave forecasting using genetic programming",
journal = "Ocean Engineering",
volume = "35",
number = "11-12",
pages = "1166--1172",
year = "2008",
ISSN = "0029-8018",
doi = "doi:10.1016/j.oceaneng.2008.04.007",
URL = "http://www.sciencedirect.com/science/article/B6V4F-4SD6SSR-1/2/619ec0df2657e8e39b38b7d533d37ec4",
keywords = "genetic algorithms, genetic programming, Wave
forecasts, Wave heights, Real-time forecasting",
abstract = "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.",
}
@Article{Gavrilis20081358,
author = "Dimitris Gavrilis and Ioannis G. Tsoulos and Evangelos
Dermatas",
title = "Selecting and constructing features using grammatical
evolution",
journal = "Pattern Recognition Letters",
volume = "29",
number = "9",
pages = "1358--1365",
year = "2008",
ISSN = "0167-8655",
doi = "doi:10.1016/j.patrec.2008.02.007",
URL = "http://www.sciencedirect.com/science/article/B6V15-4S01WDH-4/2/aaff3c40c5eca125dfacb426d88fa177",
keywords = "genetic algorithms, genetic programming, Grammatical
evolution, Artificial neural networks, Feature
selection, Feature construction",
abstract = "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.",
}
@InCollection{gearhart:2003:GPPSMDP,
author = "Chris Gearhart",
title = "Genetic Programming as Policy Search in Markov
Decision Processes",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2003",
year = "2003",
editor = "John R. Koza",
pages = "61--67",
address = "Stanford, California, 94305-3079 USA",
month = "4 " # dec,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.genetic-programming.org/sp2003/Gearhart.pdf",
notes = "part of \cite{koza:2003:gagp}",
}
@Article{GeethaRamani:2009:IJARS,
author = "R. {Geetha Ramani} and R. Subramanian and P.
Viswanath",
title = "Genetic Programming Method of Evolving the Robotic
Soccer Player Strategies with Ant Intelligence",
journal = "International Journal of Advanced Robotic Systems",
year = "2009",
volume = "6",
number = "2",
pages = "79--90",
keywords = "genetic algorithms, genetic programming, Robotic
Soccer, Social Insect Behaviors, Ant intelligence,
Learning methods, E CJ simulator, Teambots.",
ISSN = "1729-8806",
URL = "http://intechweb.org/downloadpdf.php?id=6314",
URL = "http://intechweb.org/Genetic_Programming_Method_of_Evolving_the_Robotic_Soccer_Player_Strategies_with_Ant_Intelligence.pdf",
size = "12 pages",
abstract = "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.",
notes = "football Dept. Of CSE & IT, Pondicherry Engineering
College http://intechweb.org/journal.php?id=3",
}
@InProceedings{gelenbe:1996:GAas,
author = "Erol Gelenbe",
title = "Genetic Algorithms with Analytical Solution",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "Genetic Algorithms",
pages = "437--443",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96 GA paper",
}
@InProceedings{1068309,
author = "Sylvain Gelly and Olivier Teytaud and Nicolas Bredeche
and Marc Schoenauer",
title = "A statistical learning theory approach of bloat",
booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on
Genetic and evolutionary computation",
year = "2005",
editor = "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",
volume = "2",
ISBN = "1-59593-010-8",
pages = "1783--1784",
address = "Washington DC, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1783.pdf",
URL = "http://www.lri.fr/~teytaud/eabloat.pdf",
URL = "http://www.lri.fr/~teytaud/eabloat/eabloat.html",
doi = "doi:10.1145/1068009.1068309",
publisher = "ACM Press",
publisher_address = "New York, NY, 10286-1405, USA",
month = "25-29 " # jun,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, Poster, code
bloat, code growth, reliability, statistical learning
theory, theory",
size = "2 pages",
abstract = "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
\cite{gelly:2005:longBloat}",
notes = "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",
}
@Misc{gelly:2005:longBloat,
author = "Sylvain Gelly and Olivier Teytaud and Nicolas Bredeche
and Marc Schoenauer",
title = "A Statistical Learning Theory Approach of Bloat",
howpublished = "www",
year = "2005",
keywords = "genetic algorithms, genetic programming,
Vapnik-Chervonenkis, VC dimension, bloat",
URL = "http://www.lri.fr/~teytaud/longBloat.pdf",
URL = "http://www.lri.fr/~gelly/paper/antibloatGecco2005_long_version.pdf",
size = "8 pages",
abstract = "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.",
notes = "cited by \cite{1068309} Replaced by
\cite{DBLP:conf/cfap/GellyTBS05}
Equipe TAO - INRIA Futurs LRI, Bat. 490, University
Paris-Sud 91405 Orsay Cedex. France",
}
@InProceedings{DBLP:conf/cfap/GellyTBS05,
author = "Sylvain Gelly and Olivier Teytaud and Nicolas Bredeche
and Marc Schoenauer",
title = "Apprentissage statistique et programmation
g{\'e}n{\'e}tique: la croissance du code est-elle
in{\'e}vitable?",
booktitle = "Actes de CAP 05, Conf{\'e}rence francophone sur
l'apprentissage automatique",
year = "2005",
editor = "Fran\c{c}ois Denis",
pages = "163--178",
address = "Nice, France",
month = "31 " # may # "-3 " # jun,
publisher = "PUG",
note = "A Statistical Learning Theory Approach of Bloat",
bibsource = "DBLP, http://dblp.uni-trier.de",
keywords = "genetic algorithms, genetic programming, VC, Bloat",
URL = "http://www.lri.fr/~gelly/paper/bloatCap2005.pdf",
size = "16 pages",
abstract = "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.",
notes = "CAP 2005 http://www.lif.univ-mrs.fr/~fdenis/cap05/ In
english.
an improved version of \cite{gelly:2005:longBloat}
Part of DBLP:conf/cfap/2005",
}
@Article{oai:hal.archives-ouvertes.fr:inria-00112840_v1,
title = "Universal Consistency and Bloat in {GP}",
title_2 = "Some theoretical considerations about Genetic
Programming from a Statistical Learning Theory
viewpoint",
author = "Sylvain Gelly and Olivier Teytaud and Nicolas Bredeche
and Marc Schoenauer",
journal = "Revue d'Intelligence Artificielle",
year = "2006",
volume = "20",
number = "6",
pages = "805--827",
note = "Issue on New Methods in Machine Learning. Theory and
Applications",
publisher = "HAL - CCSd - CNRS",
annote = "Sylvain Gelly ",
bibsource = "OAI-PMH server at hal.archives-ouvertes.fr",
contributor = "Sylvain Gelly ",
identifier = "inria-00112840 (version 1)",
oai = "oai:hal.archives-ouvertes.fr:inria-00112840_v1",
keywords = "genetic algorithms, genetic programming, Computer
Science/Learning; Mathematics/Optimization and
Control",
ISSN = "0992-499X",
URL = "http://hal.inria.fr/docs/00/11/28/40/PDF/riabloat.pdf",
URL = "http://hal.inria.fr/inria-00112840/en/",
URL = "http://ria.revuesonline.com/article.jsp?articleId=8936",
resume = "Dans cet article, nous proposons une etude de la
Programmation Genetique (PG) du point de vue de la
theorie de l'Apprentissage Statistique dans le cadre de
la regression symbolique. En particulier, nous nous
sommes interesses a la consistence universelle en PG,
c'est-adire la convergence presque sure vers l'erreur
bayesienne a mesure que le nombre d'exemples augmente,
ainsi qu'au probleme bien connu en PG de la croissance
incontrolee de la taille du code (i.e. le {"}bloat{"}).
Les resultats que nous avons obtenus montrent d'une
part que l'on peut identifier plusieurs types de bloat
et d'autre part que la consistence universelle et
l'absence de bloat peuvent etre obtenues sous certaines
conditions. Nous proposons finalement une methode ad
hoc evitant justement le bloat tout en garantissant la
consistence universelle.",
abstract = "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.",
notes = "in english",
}
@PhdThesis{Gelly:thesis,
author = "Sylvain Gelly",
title = "A contribution to Reinforcement Learning: Application
to Computer-Go",
school = "Universite, Paris-Sud",
year = "2007",
address = "91405 Orsay, Cedex, France",
month = "25 " # sep,
keywords = "genetic algorithms, Monte-Carlo Random Trees, UCT,
MoGo, OpenDP, SVM, CMA-ES",
URL = "http://bibliographie.jeudego.org/these_sylvain-gelly.pdf",
size = "283 pages",
citeulike-article-id = "2990577",
notes = "Informatique, Number 8754. Written in english.
TAO, LRI.FR OpenBeagle implementation used.
",
}
@InProceedings{Gelly:2009:eurogp,
author = "Nur Merve Amil and Nicolas Bredeche and Christian
Gagn{\'e} and Sylvain Gelly and Marc Schoenauer and
Olivier Teytaud",
title = "A Statistical Learning Perspective of Genetic
Programming",
booktitle = "Proceedings of the 12th European Conference on Genetic
Programming, EuroGP 2009",
year = "2009",
editor = "Leonardo Vanneschi and Steven Gustafson and Alberto
Moraglio and Ivanoe {De Falco} and Marc Ebner",
volume = "5481",
series = "LNCS",
pages = "327--338",
address = "Tuebingen",
month = apr # " 15-17",
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, poster",
isbn13 = "978-3-642-01180-1",
doi = "doi:10.1007/978-3-642-01181-8_28",
abstract = "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.",
notes = "Also known as \cite{DBLP:conf/eurogp/AmilBGGST09}
Part of \cite{conf/eurogp/2009} EuroGP'2009 held in
conjunction with EvoCOP2009, EvoBIO2009 and
EvoWorkshops2009",
}
@Article{George:2009:MBT,
author = "Ajish D. George and Scott A. Tenenbaum",
title = "Informatic Resources for Identifying and Annotating
Structural {RNA} Motifs",
journal = "Molecular Biotechnology",
year = "2009",
volume = "41",
number = "2",
pages = "180--193",
month = feb,
URL = "http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2770092/pdf/nihms152441.pdf",
doi = "doi:10.1007/s12033-008-9114-z",
size = "14 pages",
abstract = "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.",
notes = "Refers briefly to \cite{Yuh-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",
}
@InProceedings{1144159,
author = "Ashley George and Malcolm I. Heywood",
title = "Improving {GP} classifier generalization using a
cluster separation metric",
booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference
on Genetic and evolutionary computation",
year = "2006",
editor = "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",
volume = "1",
ISBN = "1-59593-186-4",
pages = "939--940",
address = "Seattle, Washington, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p939.pdf",
doi = "doi:10.1145/1143997.1144159",
publisher = "ACM Press",
publisher_address = "New York, NY, 10286-1405, USA",
month = "8-12 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming: Poster,
classification, clustering, evaluation",
notes = "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",
}
@InProceedings{Georgiou:2006:10WSEAS,
author = "Loukas Georgiou and William J. Teahan",
title = "j{GE} - {A} Java implementation of Grammatical
Evolution",
booktitle = "10th WSEAS International Conference on Systems",
year = "2006",
pages = "534--869",
address = "Athens, Greece",
month = jul # " 10-15",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, genetic algorithms, evolutionary
computation,agents, jGE, libGE, GP, GE",
abstract = "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.",
}
@Article{Georgiou:2006:WSEAS,
author = "Loukas Georgiou and William J. Teahan",
title = "Implication of Prior Knowledge and Population Thinking
in Grammatical Evolution: Toward a Knowledge Sharing
Architecture",
journal = "WSEAS Transactions on Systems",
year = "2006",
volume = "5",
number = "10",
pages = "2338--2345",
month = oct,
keywords = "genetic algorithms, genetic programming, grammatical
evolution, genetic algorithms, evolutionary
computation,agents, jGE, libGE, GP, GE",
abstract = "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).",
notes = "Check title",
}
@InCollection{Georgiou:2008:K-DC,
author = "Loukas Georgiou and William J. Teahan",
title = "Experiments with Grammatical Evolution in Java",
booktitle = "Knowledge-Driven Computing: Knowledge Engineering and
Intelligent Computations",
publisher = "Springer",
year = "2008",
editor = "C. Cotta and S. Reich and R. Schaefer and A. Ligeza",
volume = "102",
series = "Studies in Computational Intelligence",
chapter = "4",
pages = "45--62",
keywords = "genetic algorithms, genetic programming, Grammatical
Evolution, Evolutionary Computation, jGE, libGE, GP",
isbn13 = "978-3-540-77474-7",
doi = "doi:10.1007/978-3-540-77475-4_4",
abstract = "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.",
notes = "Online version of book available
http://www.springer.com/engineering/book/978-3-540-77474-7?detailsPage=toc",
}
@InProceedings{Georgiou:2010:ICEC,
author = "Loukas Georgiou and William J. Teahan",
title = "Grammatical Evolution and the Santa Fe Trail Problem",
booktitle = "Proceedings of the International Conference on
Evolutionary Computation (ICEC 2010)",
year = "2010",
pages = "10--19",
editor = "Agostinho Rosa",
address = "Valencia, Spain",
month = "24-26 " # oct,
keywords = "genetic algorithms, genetic programming, Grammatical
Evolution, Artificial Ant Problem, Santa Fe Trail
Problem, Genetic Programming, Genetic Algorithms, jGE,
jGE NetLogo, Java, NetLogo",
abstract = "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.",
notes = "http://www.icec.ijcci.org/ICEC2010/home.asp
http://www.ecta.ijcci.org/Abstracts/2010/ICEC_2010_Abstracts.htm",
}
@InProceedings{Georgiou:2011:IJCAI,
author = "Loukas Georgiou and William J. Teahan",
title = "Constituent Grammatical Evolution",
booktitle = "Proceedings of the Twenty-Second International Joint
Conference on Artificial Intelligence",
year = "2011",
editor = "Toby Walsh",
pages = "1261--1268",
address = "Barcelona, Spain",
publisher_address = "Menlo Park, California, USA",
month = "16-22 " # jul,
organisation = "International Joint Conferences on Artificial
Intelligence",
publisher = "AAAI Press",
keywords = "genetic algorithms, genetic programming, Grammatical
Evolution",
isbn13 = "978-1-57735-512-0",
URL = "http://ijcai.org/papers11/Papers/IJCAI11-214.pdf",
size = "8 pages",
abstract = "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).",
notes = "Santa Fe Ant, Lost Altos Hills, Hampton Court Maze,
jGE http://ijcai.org/papers11/contents.php",
}
@InProceedings{conf/setn/GeorgopoulosZAVL08,
title = "A Genetic Programming Environment for System
Modeling",
author = "Efstratios F. Georgopoulos and George P. Zarogiannis
and Adam V. Adamopoulos and Anastasios P. Vassilopoulos
and Spiridon D. Likothanassis",
bibdate = "2008-09-26",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/setn/setn2008.html#GeorgopoulosZAVL08",
booktitle = "5th Hellenic Conference on AI, SETN 2008",
publisher = "Springer",
year = "2008",
volume = "5138",
editor = "John Darzentas and George A. Vouros and Spyros
Vosinakis and Argyris Arnellos",
isbn13 = "978-3-540-87880-3",
pages = "85--96",
series = "Lecture Notes in Computer Science",
doi = "doi:10.1007/978-3-540-87881-0_9",
address = "Syros, Greece",
month = oct # " 2-4",
keywords = "genetic algorithms, genetic programming, Evolutionary
Algorithms, System Modeling, MEG modeling, fatigue
modeling",
size = "12 pages",
abstract = "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.",
notes = "SQUID, MEG",
}
@Article{Georgoulas200769,
author = "George Georgoulas and Dimitris Gavrilis and Ioannis G.
Tsoulos and Chrysostomos Stylios and Joao Bernardes and
Peter P. Groumpos",
title = "Novel approach for fetal heart rate classification
introducing grammatical evolution",
journal = "Biomedical Signal Processing and Control",
volume = "2",
number = "2",
pages = "69--79",
year = "2007",
ISSN = "1746-8094",
doi = "DOI:10.1016/j.bspc.2007.05.003",
URL = "http://www.sciencedirect.com/science/article/B7XMN-4P9K9C1-1/2/26899c02af37c6edf88c6baa6282a061",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, Fetal heart rate, Multilayer perceptron,
Feature construction, Classification",
abstract = "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).",
}
@Article{Gepp:2009:GPEM,
author = "Adrian Gepp and Phil Stocks",
title = "A review of procedures to evolve quantum algorithms",
journal = "Genetic Programming and Evolvable Machines",
year = "2009",
volume = "10",
number = "2",
pages = "181--228",
month = jun,
keywords = "genetic algorithms, genetic programming, Evolving
quantum algorithms, Quantum computing, Evolutionary
algorithms, Quantum algorithms",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-009-9080-7",
abstract = "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.",
}
@InProceedings{conf/asc/GestalRDP06,
title = "Description of {RANNs} and their generalisation
capabilities by means of rule extraction by genetic
programming",
author = "Marcos Gestal and Juan R. Rabu{\~n}al and Julian
Dorado and Javier {Pereira Loureiro}",
booktitle = "Artificial Intelligence and Soft Computing",
publisher = "IASTED/ACTA Press",
year = "2006",
editor = "Angel P. Del Pobil",
ISBN = "0-88986-612-0",
pages = "323--328",
address = "Palma de Mallorca, Spain",
month = aug # " 28-30",
bibdate = "2007-01-26",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/asc/asc2006.html#GestalRDP06",
keywords = "genetic algorithms, genetic programming, Recurrent
Artificial Neural Networks, Rule Extraction, Algorithm
of Example Generation, Generalisation Capabilities,
Series Prediction",
URL = "http://www.actapress.com/PaperInfo.aspx?PaperID=28200",
URL = "http://sabia.tic.udc.es/sabia/secciones/publications/?id=311",
abstract = "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.",
}
@InCollection{Geyer:1990:pfss,
author = "A. Geyer and Andreas Geyer-Schulz and A. Taudes",
title = "A Fuzzy Times Series Analyzer",
booktitle = "Progress in Fuzzy Sets and Systems",
publisher = "Kluwer Academic Publishers",
year = "1990",
editor = "Wolfgang H. Janko and Marc Roubens and H.-J.
Zimmermann",
volume = "5",
series = "Series D: Systems Theory, Knowledge Engineering and
Problem Solving",
pages = "63--74",
address = "The Netherlands",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Geyer_1990_pfss.pdf",
size = "12 pages",
}
@InProceedings{GeyerSchulz92d,
crossref = "Hoehle92",
author = "Andreas Geyer--Schulz",
title = "Fuzzy Rule Languages and Genetic Algorithms",
year = "1992",
pages = "36--38",
keywords = "genetic algorithms, genetic programming",
notes = "In \cite{Hoehle92}",
}
@Proceedings{Hoehle92,
editor = "Ulrich H{\"o}hle and Peter Klement",
booktitle = "$14^{th}$ Linz Seminar on Fuzzy Set Theory:
Non-Classical Logics and their Applications",
title = "$14^{th}$ Linz Seminar on Fuzzy Set Theory:
Non-Classical Logics and their Applications",
year = "1992",
publisher = "Johannes Kepler Universit{\"a}t Linz",
address = "Linz",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{GeyerSchulz92b,
crossref = "Lowen92",
author = "Andreas Geyer--Schulz",
title = "Fuzzy Classifier Systems",
year = "1992",
pages = "345--354",
notes = "In \cite{Lowen92}",
}
@Proceedings{Lowen92,
editor = "Robert Lowen and Marc Roubens",
booktitle = "Fuzzy Logic: State of the Art",
title = "Fuzzy Logic: State of the Art",
year = "1993",
series = "Series D: System Theory, Knowledge Engineering and
Problem Solving",
organisation = "IFSA",
publisher = "Kluwer Academic Publishers",
address = "Dordrecht",
}
@InProceedings{GeyerSchulz92c,
crossref = "Bandemer92",
author = "Andreas Geyer--Schulz",
title = "On the Specification of Fuzzy Data in Management",
year = "1992",
pages = "105--110",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/GeyerSchulz92c.pdf",
keywords = "genetic algorithms, genetic programming",
size = "6 pages",
notes = "In \cite{Bandemer92}",
}
@Proceedings{Bandemer92,
editor = "Hans Bandemer",
booktitle = "Modelling Uncertain Data",
title = "Modelling Uncertain Data",
year = "1993",
volume = "68",
series = "Mathematical Research",
organisation = "GAMM",
publisher = "Akademie Verlag",
address = "Berlin",
ISBN = "3-05-501578-9",
URL = "http://books.google.co.uk/books?id=FzjvAAAAMAAJ",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{GeyerSchulz93b,
crossref = "Frisch93",
author = "Andreas Geyer--Schulz",
title = "{Z}ur {B}eschleunigung des {L}ernens genetischer
{A}lgorithmen mittels unscharfer {R}egelsprachen",
year = "1993",
pages = "73--85",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/GeyerSchulz93b.pdf",
keywords = "genetic algorithms, genetic programming",
notes = "In \cite{Frisch93}. In German",
}
@Proceedings{Frisch93,
editor = "Walter Frisch and Alfred Taudes",
booktitle = "Informationswirtschaft",
title = "Informationswirtschaft, Aktuelle Entwicklungen und
Perspektiven : Symposion",
year = "1993",
month = "29-30 " # sep,
publisher = "Physica-Verlag",
publisher_address = "Heidelberg",
address = "Vienna",
ISBN = "3-7908-0727-3",
URL = "http://books.google.co.uk/books?id=PAXYPQAACAAJ",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{Geyer-Schulz:1993:EUFIT,
author = "Andreas Geyer-Schulz",
title = "Speeding Up Genetic Machine Learning -- {A} case for
Fuzzy Rule Languages",
booktitle = "First European Congress on Fuzzy and Intelligent
Technologies, EUFIT'93",
year = "1993",
volume = "2",
pages = "1083--1089",
address = "Aachen, Germany",
publisher_address = "D-52076 Aachen",
month = "7-10 " # sep,
publisher = "Elite-Foundation",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Geyer-Schulz_1993_EUFIT.pdf",
size = "7 pages",
notes = "Boston Consulting Group rule language grammar",
}
@Book{GeyerSchulz95a,
author = "Andreas Geyer--Schulz",
title = "Fuzzy Rule-Based Expert Systems and Genetic Machine
Learning",
publisher = "Physica-Verlag",
address = "Heidelberg",
year = "1995",
volume = "3",
series = "Studies in Fuzziness",
URL = "http://www.amazon.com/Rule-Based-Systems-Learning-Fuzziness-Computing/dp/3790809640",
ISBN = "3-7908-0830-X",
keywords = "genetic algorithms, genetic programming",
notes = "reviewed by Dick Bowman, Dogon Research
http://www.apl.demon.co.uk/aplandj/fuzzy.htm",
}
@TechReport{GeyerSchulz95c,
author = "Andreas Geyer--Schulz",
title = "Genetic Machine Learning",
institution = "ACM SIGAPL",
address = "New York, N.Y.",
year = "1995",
note = "Tutorial held at APL'95 at San Antonio, Texas",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{GeyerSchulz96a,
crossref = "Herrera96",
author = "Andreas Geyer--Schulz",
title = "The {M}{I}{T} Beer Distribution Game Revisited:
Genetic Machine Learning and Managerial Behavior in a
Dynamic Decision Making Experiment",
year = "1996",
pages = "658--682",
keywords = "genetic algorithms, genetic programming, Experimental
economics, organizational learning, simulation, gaming,
system dynamics, fuzzy genetic programming.",
abstract = "The paper reports on the experiment of applying
genetic machine learning methods to breeding heuristic
for playing the MIT beer distribution game. In the MIT
beer distribution game a team of four subjects acts as
managers of a simulated industrial production and
distribution system with the aim of minimising total
inventory. The system consists of a chain of ofur
coupled stock management systems with uncertain demand,
tiem delays, feedbacks, multiple actors,
non-linearities and restricted information
availability. The complexity of the system - it is a
23rd order non-linear difference equation - renders
calculation of the optimal behaviour intractable. In
the experiment threee genetic machine learning methods
(a simple genetic algorithm, genetic programming, and
fuzzy genetic programming) are applied to the beer
distribution game. The results of the methods are
compared with the previously known best solution and
with the performance of a group of subjects which
actually played the game.",
notes = "In \cite{Herrera96}
http://decsai.ugr.es/~herrera/abstracts.html#c30",
}
@Proceedings{Herrera96,
editor = "F. Herrera and J. L. Verdegay",
booktitle = "Genetic Algorithms and Soft Computing",
title = "Genetic Algorithms and Soft Computing",
year = "1996",
month = sep,
volume = "8",
series = "Studies in Fuzziness and Soft Computing",
organisation = "Physica-Verlag",
publisher = "Physica-Verlag",
address = "Heidelberg",
ISBN = "3-7908-0956-X",
broken = "http://decsai.ugr.es/~herrera/ga-sc.html",
URL = "http://www.amazon.co.uk/gp/search?index=books&linkCode=qs&keywords=379080956X",
keywords = "genetic algorithms, genetic programming",
}
@Book{GeyerSchulz96b,
author = "Andreas Geyer--Schulz",
title = "Fuzzy Rule-Based Expert Systems and Genetic Machine
Learning",
publisher = "Physica-Verlag",
address = "Heidelberg",
year = "1996",
volume = "3",
series = "Studies in Fuzziness and Soft Computing",
edition = "2nd revised",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.amazon.co.uk/gp/search?index=books&linkCode=qs&keywords=3790809640",
}
@Proceedings{Biethahn96,
editor = "J. Biethahn and A. H{\"o}hnerloh and J. Kuhl and V.
Nissen",
booktitle = "Betriebliche Anwendungen von Fuzzy Technologien",
title = "Betriebliche Anwendungen von Fuzzy Technologien",
year = "1996",
organisation = "AFN -- Arbeitsgemeinschaft Fuzzy Logik und
Softcomputing Norddeutschland",
publisher = "Georg-August Universit{\"a}t G{\"o}ttingen, Institut
f{\"u}r Wirtschaftsinformatik",
address = "G{\"o}ttingen",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.amazon.de/Betriebliche-Anwendungen-von-Fuzzy-Technologien-Softcomputing/dp/B003E8W9ZE",
}
@InProceedings{GeyerSchulz96c,
crossref = "Biethahn96",
author = "Andreas Geyer--Schulz",
title = "{D}as {L}ernen von {B}estellregeln in
{D}istributionsketten: {E}ine betriebswirtschaftliche
{A}nwendung von {F}uzzy {G}enetic {P}rogramming",
year = "1996",
pages = "92--106",
keywords = "genetic algorithms, genetic programming",
notes = "In \cite{Biethahn96}",
}
@Article{GeyerSchulz96d,
author = "Andreas Geyer--Schulz",
title = "Fuzzy Genetic Programming and Dynamic Decision
Making",
journal = "Proc. ICSE'96",
year = "1996",
month = jun,
pages = "686--691",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{GeyerSchulz96e,
author = "Andreas Geyer--Schulz",
title = "Compound Derivations in Fuzzy Genetic Programming",
booktitle = "1996 Biennial Conference of the North American Fuzzy
Information Processing Society, NAFIPS",
year = "1996",
month = jul,
pages = "510--514",
doi = "doi:10.1109/NAFIPS.1996.534787",
keywords = "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)",
size = "5 pages",
abstract = "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",
}
@InProceedings{GeyerSchulz96f,
author = "Andreas Geyer--Schulz",
title = "Learning Strategies for Managing New and Innovative
Products",
booktitle = "Classification and Knowledge Organization Proceedings
of the 20th Annual Conference of the Gesellschaft fuer
Klassifikation e.V., GfKl'96",
editor = "Ruediger Klar and Otto Opitz",
volume = "XX",
year = "1996",
month = "6-8 " # mar,
series = "Studies in Classification, Data Analysis, and
Knowledge Organization",
pages = "262--269",
address = "University of Freiburg, Germany",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-540-62981-8",
URL = "http://www.springer.com/economics/book/978-3-540-62981-8?cm_mmc=Google-_-Book%20Search-_-Springer-_-0",
size = "8 pages",
notes = "published 1997 (2012 Currently out of stock)",
}
@Article{GeyerSchulz96g,
author = "Andreas Geyer--Schulz",
title = "Fuzzy Genetic Algorithms",
journal = "Handbook of Fuzzy Systems",
year = "1996",
month = apr,
note = "Work in progress",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{Geyer-Schulz:1997:700,
author = "Andreas Geyer-Schulz",
title = "The Next 700 Programming Languages for Genetic
Programming",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "128--136",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/Geyer-Schulz_1997_700.pdf",
size = "9 pages",
notes = "GP-97",
}
@InProceedings{Ghanea-Hercock:1994:Earca,
author = "R. Ghanea-Hercock and A. P. Fraser",
title = "Evolution of autonomous robot control architectures",
booktitle = "Evolutionary Computing, AISB workshop",
year = "1994",
editor = "T. C. Fogarty",
address = "Leeds, UK",
month = "11-13 " # apr,
organisation = "AISB",
keywords = "genetic algorithms, genetic programming",
notes = "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",
}
@InProceedings{ghanea-hercock:1999:DGPMA,
author = "Robert Ghanea-Hercock and Divine T. Ndumu and Jaron
Collis",
title = "Distributed Genetic Programming with Mobile Agents",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1441",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, artificial
life, adaptive behavior and agents, poster papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-004.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-004.ps",
abstract = "java based mobil agents, MATS",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{GhaniC09,
author = "Kamran Ghani and John A. Clark",
title = "Widening the Goal Posts: Program Stretching to Aid
Search Based Software Testing",
booktitle = "Proceedings of the 1st International Symposium on
Search Based Software Engineering (SSBSE'09)",
year = "2009",
address = "Cumberland Lodge, Windsor, UK",
month = "13-15 " # may,
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming, SBSE",
doi = "doi:10.1109/SSBSE.2009.26",
abstract = "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.",
}
@InProceedings{Ghani:2009:ICSEA,
author = "Kamran Ghani and John A. Clark",
title = "Automatic Test Data Generation for Multiple Condition
and {MCDC} Coverage",
booktitle = "Fourth International Conference on Software
Engineering Advances, ICSEA'09",
year = "2009",
month = sep,
pages = "152--157",
note = "Winner of top paper prize",
keywords = "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",
isbn13 = "978-0-7695-3675-0",
doi = "doi:10.1109/ICSEA.2009.31",
abstract = "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.",
notes = "Also known as \cite{5298463}",
}
@InProceedings{ghozeil:1996:dpspdEP,
author = "Adam Ghozeil and David B. Fogel",
title = "Discovering Patterns in Spatial Data using
Evolutionary Programming",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "Evolutionary Programming",
pages = "521--527",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96 EP paper",
}
@InProceedings{giacobini:2002:gecco,
author = "Mario Giacobini and Marco Tomassini and Leonardo
Vanneschi",
title = "How Statistics Can Help In Limiting The Number Of
Fitness Cases In Genetic Programming",
booktitle = "GECCO 2002: Proceedings of the Genetic and
Evolutionary Computation Conference",
editor = "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",
year = "2002",
pages = "889",
address = "New York",
publisher_address = "San Francisco, CA 94104, USA",
month = "9-13 " # jul,
publisher = "Morgan Kaufmann Publishers",
keywords = "genetic algorithms, genetic programming, poster paper,
entropy, fitness Cases, statistics",
ISBN = "1-55860-878-8",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/GP073.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/GP073.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-15.pdf",
notes = "GECCO-2002. A joint meeting of the eleventh
International Conference on Genetic Algorithms
(ICGA-2002) and the seventh Annual Genetic Programming
Conference (GP-2002)",
}
@InProceedings{giacobini:ppsn2002:pp371,
author = "Mario Giacobini and Marco Tomassini and Leonardo
Vanneschi",
title = "Limiting the Number Fitness Cases in Genetic
Programming Using Statistics",
booktitle = "Parallel Problem Solving from Nature - PPSN VII",
address = "Granada, Spain",
month = "7-11 " # sep,
pages = "371--380",
year = "2002",
editor = "Juan J. Merelo-Guervos and Panagiotis Adamidis and
Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and
Hans-Paul Schwefel",
number = "2439",
series = "Lecture Notes in Computer Science, LNCS",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, Parameter
tuning, Fitness Evaluation, Theory of evolutionary
computing",
ISBN = "3-540-44139-5",
annote = "Available from
http://link.springer.de/link/service/series/0558/papers/2439/243900371.pdf",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2439&spage=371",
doi = "doi:10.1007/3-540-45712-7_36",
abstract = "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.",
}
@InProceedings{Giagkos:2009:TAROS,
author = "Alexandros Giagkos and Myra S. Wilson",
title = "A Cross-layer Design for Bee-Inspired Routing
Protocols in {MANET}s",
booktitle = "TAROS 2009 Towards Autonomous Robotic Systems",
year = "2009",
editor = "Theocharis Kyriacou and Ulrich Nehmzow and Chris
Melhuish and Mark Witkowski",
series = "Intelligent Systems Research Centre Technical Report
Series",
pages = "25--32",
address = "University of Ulster, Londonderry, United Kingdom",
month = aug # " 31 - " # sep # " 2",
keywords = "wireless, mobile, ad hoc, bee-inspired, crosslayering,
routing",
URL = "http://isrc.ulster.ac.uk/images/stories/publications/report-series/TAROS_2009.pdf",
size = "8 pages",
abstract = "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.",
notes = "Inspired by GP?
http://www.infm.ulst.ac.uk/~ulrich/Taros09/",
}
@InProceedings{giani:1998:spccs,
author = "Antonella Giani",
title = "A Study of Parallel Cooperative Classifier Systems",
booktitle = "Late Breaking Papers at the Genetic Programming 1998
Conference",
year = "1998",
editor = "John R. Koza",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "Stanford, CA, USA",
month = "22-25 " # jul,
publisher = "Stanford University Bookstore",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.ai.mit.edu/people/unamay/phd-ws-abstracts/gianni.ps
broken",
notes = "GP-98LB, GP-98PhD Student Workshop see
http://www.di.unipi.it/phd/tesi/tesi_1999.html",
}
@InProceedings{gibbs:1996:eikGP,
author = "Jonathan Gibbs",
title = "Easy Inverse Kinematics using Genetic Programming",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "422",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
size = "1 page",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96",
}
@Article{gibbs:1996:GP96review,
author = "W. Wayt Gibbs",
title = "Programming with Primordial Ooze",
journal = "Scientific American",
year = "1996",
volume = "275",
number = "4",
pages = "30--31",
month = oct,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.genetic-programming.com/published/scientificamerican1096.html",
URL = "http://www.sciamdigital.com/index.cfm?fa=Products.ViewIssuePreview&ISSUEID_CHAR=999FE651-ED4C-44FB-A474-45B350186E9&ARTICLEID_CHAR=F6E35349-F7F4-4AA4-8027-C720B177667",
size = "1 page",
abstract = "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 Holland¿s 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.",
notes = "Summary Report on GP96. Notes on papers by Jamie J.
Fernandez, Conor Ryan, Brian Howley, Lee Spector and
Adrian Thompson",
}
@Article{gibbs:2001:sciam,
author = "W. Wayt Gibbs",
title = "Cybernetic Cells",
journal = "Scientific American",
year = "2001",
volume = "265",
number = "2",
pages = "42--47",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.sciamdigital.com/index.cfm?fa=Products.ViewIssuePreview&ARTICLEID_CHAR=56B6AD77-E68F-4CD9-86A3-97B9BAD6FD6",
size = "6 pages",
notes = "favourable mention of Koza's psb 2001 work
\cite{koza:2001:PSB} PMID: 11478002 [PubMed - indexed
for MEDLINE]",
}
@InCollection{gibbs:2002:IENBCGP,
author = "Kevin A. Gibbs",
title = "Implementation and Evaluation of a Novel
{``}Branch{''} Construct for Genetic Programming",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2002",
year = "2002",
editor = "John R. Koza",
pages = "93--101",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.genetic-programming.org/sp2002/Gibbs.pdf",
language = "en",
oai = "oai:CiteSeerXPSU:10.1.1.141.205",
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.205",
abstract = "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",
notes = "part of \cite{koza: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.
",
}
@Article{Gielen1994120,
author = "C. Gielen",
title = "Genetic programming: {J}.{R}. Koza. The {MIT} Press,
Cambridge, {MA}. {ISBN} 0-262-11170-5. 819 pp., \$
74,25",
journal = "Neurocomputing",
volume = "6",
number = "1",
pages = "120--122",
year = "1994",
note = "Backpropagation, Part III",
ISSN = "0925-2312",
doi = "doi:10.1016/0925-2312(94)90038-8",
URL = "http://www.sciencedirect.com/science/article/B6V10-48TCT75-5M/2/118608d812226c1e01a24920532a2702",
notes = "\cite{koza:book}",
}
@InProceedings{gigure:1998:psosGA1,
author = "Philippe Gigure and David E. Goldberg",
title = "Population Sizing for Optimum Sampling with Genetic
Algorithms: {A} Case Study of the Onemax Problem",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "496--503",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms",
notes = "SGA-98",
}
@Article{Gilani:2006:IJASET,
author = "Labiba Gilani and Asifullah Khan and Anwar M. Mirza",
title = "Distortion Estimation in Digital Image Watermarking
using Genetic Programming",
journal = "International Journal of Applied Science, Engineering
and Technology",
year = "2006",
volume = "15",
number = "20",
pages = "103--108",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.waset.org/ijaset/v15/v15-20.pdf",
size = "6 pages",
abstract = "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.",
notes = "http://www.waset.org/ijaset/ Lena",
}
@InProceedings{gilbert:1998:GPvshdd,
author = "Richard J. Gilbert and Royston Goodacre and Beverly
Shann and Douglas B. Kell and Janet Taylor and Jem J.
Rowland",
title = "Genetic Programming-Based Variable Selection for
High-Dimensional Data",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "109--115",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-548-7",
notes = "GP-98",
}
@Article{gilbert:1997:,
author = "Richard J. Gilbert and Royston Goodacre and Andrew M.
Woodward and Douglas B. Kell",
title = "Genetic programming: {A} novel method for the
quantitative analysis of pyrolysis mass spectral data",
journal = "ANALYTICAL CHEMISTRY",
year = "1997",
volume = "69",
number = "21",
pages = "4381--4389",
keywords = "genetic algorithms, genetic programming",
URL = "http://pubs.acs.org/journals/ancham/article.cgi/ancham/1997/69/i21/pdf/ac970460j.pdf",
doi = "doi:10.1021/ac970460j",
size = "9 pages",
abstract = "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%.",
notes = "
",
}
@InProceedings{gilbert:1999:,
author = "Richard J. Gilbert and Helen E. Johnson and Michael K.
Winson and Jem J. Rowland and Royston Goodacre and
Aileen R. Smith and Michael A. Hall and Douglas B.
Kell",
title = "Genetic Programming as an Analytical Tool for
Metabolome Data",
booktitle = "Late-Breaking Papers of EuroGP-99",
year = "1999",
editor = "W. B. Langdon and Riccardo Poli and Peter Nordin and
Terry Fogarty",
pages = "23--33",
address = "Goteborg, Sweden",
month = "26-27 " # may,
organisation = "EvoGP",
keywords = "genetic algorithms, genetic programming",
URL = "ftp://ftp.cwi.nl/pub/CWIreports/SEN/SEN-R9913.pdf",
URL = "ftp://ftp.cwi.nl/pub/CWIreports/SEN/SEN-R9913.ps.Z",
abstract = "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.",
notes = "EuroGP'99LB part of \cite{langdon:1999:egplb}",
}
@Misc{gilbert:p450,
author = "Richard Gilbert and Kris Birchall and William Bains",
title = "Classification of Cytochrome {P450} 3{A4} Ligands
Using Genetic Programming",
year = "2002",
email = "info@amedis-pharma.com",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.amedis-pharma.com/Docs/3A4_ligand_poster.ppt",
abstract = "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.",
notes = "Amedis Pharmaceuticals Limited, Upton House, Baldock
Street, Royston, Herts SG8 5AY, UK",
}
@Article{EVL-2000-444,
author = "Andrew Gildfind and Michael A. Gigante and Ghassan
Al-Qaimari",
title = "Evolving performance control systems for digital
puppetry",
journal = "Journal of Visualization and Computer Animation",
year = "2000",
volume = "11",
number = "4",
pages = "169--183",
month = "3 " # oct,
publisher = "John Wiley & Sons, Ltd.",
keywords = "genetic algorithms, genetic programming, performance
animation, motion capture, performance control systems,
puppetry, adaptive user interfaces",
URL = "http://www3.interscience.wiley.com/cgi-bin/abstract/73502730/ABSTRACT",
URL = "http://visinfo.zib.de/EVlib/Show?EVL-2000-444",
doi = "doi:10.1002/1099-1778(200009)11:4<169::AID-VIS217>3.0.CO;2-L",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/21796/http:zSzzSzgoanna.cs.rmit.edu.auzSz~gildfindzSzthesiszSzpdfzSzjvca.pdf/gildfind00evolving.pdf",
URL = "http://citeseer.ist.psu.edu/438189.html",
size = "16 pages",
abstract = "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.",
}
@InCollection{Gillespie:1997:GAspspsd,
author = "Jaysen Gillespie",
title = "A Genetic Algorithm Solution to the Project Selection
Problem Using Static and Dynamic Fitness Functions",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1997",
publisher = "Stanford Bookstore",
year = "1997",
editor = "John R. Koza",
pages = "76--85",
address = "Stanford, California, 94305-3079 USA",
month = "17 " # mar,
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-205981-2",
notes = "part of \cite{koza:1997:GAGPs}",
}
@InProceedings{1998APS..MAR.U2403G,
author = "S. D. Gillmor and Q. Liu and L. Wang and C. E. Jordan
and A. G. Frutos and A. J. Theil and T. C. Stother and
A. E. Condon and R. M. Corn and L. M. Smith and M. G.
Lagally",
title = "Addressed-Array Approach to {DNA} Computation Readout
through {UV} Photopatterning",
booktitle = "1998 March Meeting of the American Physical Society",
year = "1998",
month = "16-20 " # mar,
pages = "2403-+",
address = "Los Angeles",
organisation = "APS",
keywords = "genetic algorithms, genetic programming",
URL = "http://flux.aps.org/meetings/YR98/BAPSMAR98/abs/S4160003.html",
abstract = "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.",
adsurl = "http://adsabs.harvard.edu/cgi-bin/nph-bib_query?bibcode=1998APS..MAR.U2403G&db_key=PHY",
adsnote = "Provided by the Smithsonian/NASA Astrophysics Data
System",
notes = "See \cite{gillmor:1998:aaaDNAcrUVp}",
}
@InProceedings{gillmor:1998:aaaDNAcrUVp,
author = "S. D. Gillmor and Q. Liu and L. Wang and C. E. Jordan
and A. G. Frutos and A. J. Theil and T. C. Stother and
A. E. Condon and R. M. Corn and L. M. Smith and M. G.
Lagally",
title = "Addressed-Array Approach to {DNA} Computation Readout
through {UV} Photopatterning",
booktitle = "Late Breaking Papers at the Genetic Programming 1998
Conference",
year = "1998",
editor = "John R. Koza",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "Stanford, CA, USA",
month = "22-25 " # jul,
publisher = "Stanford University Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "GP-98LB
See \cite{1998APS..MAR.U2403G}",
}
@PhdThesis{Giordani:thesis,
author = "Ilaria Giordani",
title = "Relational clustering for knowledge discovery in life
sciences",
school = "Universita degli Studi di Milano-Bicocca",
year = "2009",
address = "Italy",
month = oct,
keywords = "genetic algorithms, genetic programming, Relational
Clustering, Feature Selection, Knowledge integration,
Mixed data types",
URL = "http://boa.unimib.it/handle/10281/7830",
URL = "http://hdl.handle.net/10281/7830",
URL = "http://boa.unimib.it/bitstream/10281/7830/1/phd_unimib_032791.pdf",
language = "eng",
size = "144 pages",
abstract = "Clustering is one of the most common machines learning
technique, which has been widely applied in genomics,
proteomics and more generally in Life Sciences. In
particular, clustering is an unsupervised technique
that, based on geometric concepts like distance or
similarity, partitions objects into groups, such that
objects with similar characteristics are clustered
together and dissimilar objects are in different
clusters. In many domains where clustering is applied,
some background knowledge is available in different
forms: labelled data (specifying the category to which
an instance belongs); complementary information about
'true' similarity between pairs of objects or about the
relationships structure present in the input data; user
preferences (for example specifying whether two
instances should be in same or different clusters). In
particular, in many real-world applications like
biological data processing, social network analysis and
text mining, data do not exist in isolation, but a rich
structure of relationships subsists between them. A
simple example can be viewed in biological domain,
where there are al lot of relationships between genes
and proteins based on many experimental conditions.
Another example, maybe common, is the Web search domain
where there are relations between documents and words
in a text or web pages, search queries and web users.
Our research is focused on how this background
knowledge can be incorporated into traditional
clustering algorithms to optimise the process of
pattern discovery (clustering) between instances.",
abstract = "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.",
notes = "NCI60, Saccharomyces Genome Database, Oral
anticoagulation therapy Also known as
\cite{10281_7830}",
}
@InProceedings{Giot:2010:ICPR,
author = "Romain Giot and Baptiste Hemery and Christophe
Rosenberger",
title = "Low Cost and Usable Multimodal Biometric System Based
on Keystroke Dynamics and 2{D} Face Recognition",
booktitle = "20th International Conference on Pattern Recognition
(ICPR 2010)",
year = "2010",
month = "23-26 " # aug,
pages = "1128--1131",
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/ICPR.2010.282",
ISSN = "1051-4651",
notes = "GREYC Lab., Univ. of CAEN, Caen, France Also known as
\cite{5595872}",
}
@InProceedings{WSEAS_644_Gir,
author = "Ra{\'u}l Gir{\'a}ldez and Roberto Ruiz",
title = "Applying Genetic Programming to obtain Separation
Surfaces",
address = "Puerto De La Cruz, Tenerife, Spain",
year = "2001",
month = feb # "~11-15",
booktitle = "WSEAS NNA-FSFS-EC 2001",
pages = "paper ID number 644",
organisation = "The World Scientific and Engineering Academy and
Society (WSEAS)",
keywords = "genetic algorithms, genetic programming,
Classification, Dynamical systems",
notes = "www.wseas.com/2001.xls",
}
@TechReport{Giraldi:2004:0403003,
author = "Gilson A. Giraldi and Renato Portugal and Ricardo N.
Thess",
title = "Genetic Algorithms and Quantum Computation",
institution = "National Laboratory for Scientific Computing,
Petropolis, RJ, Brazil",
year = "2004",
number = "0403003",
keywords = "genetic algorithms, genetic programming, Quantum
Computing, Evolutionary Strategies",
URL = "http://arxiv.org/PS_cache/cs/pdf/0403/0403003.pdf",
URL = "http://arxiv.org/pdf/cs.NE/0403003",
abstract = "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.",
size = "27 pages",
}
@InProceedings{conf/synasc/GirdeaC07,
author = "Marta Girdea and Liviu Ciortuz",
title = "A Hybrid Genetic Programming and Boosting Technique
for Learning Kernel Functions from Training Data",
booktitle = "Proceedings of the Ninth International Symposium on
Symbolic and Numeric Algorithms for Scientific
Computing, SYNASC 2007",
year = "2007",
editor = "Viorel Negru and Tudor Jebelean and Dana Petcu and
Daniela Zaharie",
pages = "395--402",
address = "Timisoara, Romania",
month = sep # " 26-29",
publisher = "IEEE Computer Society",
keywords = "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",
isbn13 = "978-0-7695-3078-9",
doi = "doi:10.1109/SYNASC.2007.71",
size = "8 pages",
abstract = "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.",
notes = "'Alexandru loan Cuza' Univ. of Iasi, Iasi",
bibdate = "2008-11-28",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/synasc/synasc2007.html#GirdeaC07",
}
@InProceedings{conf/eurogp/GirginP08,
title = "Feature Discovery in Reinforcement Learning Using
Genetic Programming",
author = "Sertan Girgin and Philippe Preux",
bibdate = "2008-04-15",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#GirginP08",
booktitle = "Proceedings of the 11th European Conference on Genetic
Programming, EuroGP 2008",
address = "Naples",
month = "26-28 " # mar,
publisher = "Springer",
year = "2008",
volume = "4971",
editor = "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",
isbn13 = "978-3-540-78670-2",
pages = "218--229",
series = "Lecture Notes in Computer Science",
doi = "doi:10.1007/978-3-540-78671-9_19",
keywords = "genetic algorithms, genetic programming",
notes = "See also http://hal.inria.fr/inria-00187997/en/
Part of \cite{conf/eurogp/2008} EuroGP'2008 held in
conjunction with EvoCOP2008, EvoBIO2008 and
EvoWorkshops2008",
}
@Article{Giustolisi:2004:JH,
author = "Orazio Giustolisi",
title = "Using genetic programming to determine {Chezy}
resistance coefficient in corrugated channels",
journal = "Journal of Hydroinformatics",
year = "2004",
volume = "6",
number = "3",
pages = "157--173",
month = jul,
keywords = "genetic algorithms, genetic programming, evolutionary
strategies, data mining, corrugated pipes",
ISSN = "1464-7141",
URL = "http://www.iwaponline.com/jh/006/0157/0060157.pdf",
size = "17 pages",
abstract = "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.",
notes = "Morris' wake interference. Cadim",
}
@Article{Giustolisi:2006:JH,
author = "Orazio Giustolisi and Dragan A. Savic",
title = "A symbolic data-driven technique based on evolutionary
polynomial regression",
journal = "Journal of Hydroinformatics",
year = "2006",
volume = "8",
number = "3",
pages = "207--222",
keywords = "genetic algorithms, genetic programming, EPR, Chezy
resistance coefficient, Colebrook-White formula,
data-driven modelling, evolutionary computing,
regression",
ISSN = "1464-7141",
URL = "http://www.iwaponline.com/jh/008/0207/0080207.pdf",
doi = "doi:10.2166/hydro.2006.020",
size = "16 pages",
abstract = "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.",
}
@Article{Giustolisi:2009:JH,
author = "O. Giustolisi and D. A. Savic",
title = "Advances in data-driven analyses and modelling using
{EPR}-{MOGA}",
journal = "Journal of Hydroinformatics",
year = "2009",
volume = "11",
number = "3",
pages = "225--236",
keywords = "genetic algorithms, genetic programming, data-driven
modelling, evolutionary computing, groundwater
resources, multiobjective optimization, symbolic
regression",
ISSN = "1464-7141",
URL = "http://www.iwaponline.com/jh/011/0225/0110225.pdf",
doi = "doi:10.2166/hydro.2009.017",
size = "12 pages",
abstract = "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.",
notes = "Brindisi, multi objective, ANN",
}
@Article{Gladwin:2011:pimed,
author = "D. Gladwin and Paul Stewart and Jill Stewart",
title = "A novel genetic programming approach to the design of
engine control systems for the voltage stabilisation of
hybrid electric vehicle generator outputs",
journal = "Proceedings of the Institute of Mechanical Engineers
Part D - Automobile Engineering",
year = "2011",
volume = "225",
number = "10",
pages = "1334--1346",
month = oct,
keywords = "genetic algorithms, genetic programming, electronic
and electrical engineering",
ISSN = "0954-4070",
doi = "doi:10.1177/0954407011407414",
size = "13 pages",
publisher = "Institute of Mechanical Engineers",
abstract = "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.",
bibsource = "OAI-PMH server at eprints.lincoln.ac.uk",
oai = "oai:eprints.lincoln.ac.uk:4352",
type = "PeerReviewed",
URL = "http://eprints.lincoln.ac.uk/4352/",
notes = "http://www.uk.sagepub.com/journals/Journal202018",
}
@Article{Gladwin:2011:ijsysc,
author = "Dan Gladwin and Paul Stewart and Jill Stewart",
title = "Internal combustion engine control for series hybrid
electric vehicles by parallel and distributed genetic
programming/multiobjective genetic algorithms",
journal = "International Journal of Systems Science",
volume = "42",
number = "2",
year = "2011",
pages = "249--261",
note = "Computational Intelligence for Modelling and Control
of Advanced Automotive Drivetrains",
keywords = "genetic algorithms, genetic programming, automotive,
model-reference control, time-delay, hybrid vehicles,
parallel and distributed evolutionary computation,
mechanical systems, PID control, distrubed
evolutionary",
ISSN = "0020-7721",
doi = "doi:10.1080/00207720903144479",
bibsource = "DBLP, http://dblp.uni-trier.de",
URL = "http://eprints.lincoln.ac.uk/3986/",
size = "13 pages",
abstract = "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.",
oai = "oai:eprints.lincoln.ac.uk:3986",
}
@MastersThesis{Glaholt:mastersthesis,
author = "William Edward Glaholt",
title = "{GP}-Lab: The Genetic Programming Laboratory",
school = "Computer Science, California State University,
Sacramento",
year = "2004",
type = "Masters of Science",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.theglaholts.net/gplab/GPLab-ThesisDoc%20Final.pdf",
size = "136 pages",
abstract = "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.",
notes = "Approved by: Dr. Du Zhang, Advisor and Committee Chair
W. Scott Gordon, Associate Professor",
}
@InProceedings{Glaholt:2004:ICTAI,
author = "William E. Glaholt and Du Zhang",
title = "{GP}-Lab: the Genetic Programming Laboratory",
booktitle = "16th IEEE International Conference on Tools with
Artificial Intelligence, 2004. ICTAI 2004",
year = "2004",
pages = "388--395",
address = "Boca Raton, FL, USA",
month = "15-17 " # nov,
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming",
ISSN = "1082-3409",
ISBN = "0-7695-2236-X",
doi = "doi:10.1109/ICTAI.2004.66",
abstract = "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) \cite{Kramer: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.",
}
@InCollection{gleason:2000:TCDDGAGP,
author = "Sean Gleason",
title = "Tuning and Creation of Discrete Differentiators using
Genetic Algorithms and Genetic Programming",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2000",
year = "2000",
editor = "John R. Koza",
pages = "160--169",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "part of \cite{koza:2000:gagp}",
}
@InProceedings{glickman:1998:ea:edsa,
author = "Matthew Glickman and Katia Sycara",
title = "Evolutionary Algorithms: Exploring the Dynamics of
Self-Adaptation",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "762--769",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "evolutionary programming",
ISBN = "1-55860-548-7",
notes = "GP-98",
}
@InProceedings{glickman:1999:EGBLICE,
author = "Matthew R. Glickman and Katia Sycara",
title = "Evolution of Goal-Directed Behavior from Limited
Information in a Complex Environment",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1281--1288",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "artificial life, adaptive behavior and agents",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-015.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-015.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{globus:1998:amduet,
author = "Al Globus and John Lawton and Todd Wipke",
title = "Automatic molecular design using evolutionary
techniques",
booktitle = "The Sixth Foresight Conference on Molecular
Nanotechnology",
year = "1998",
editor = "Al Globus and Deepak Srivastava",
address = "Westin Hotel in Santa Clara, CA, USA",
month = nov # " 12-15, 1998",
organisation = "Foresight Institute",
keywords = "genetic algorithms, genetic programming, ring
crossover, graphs, drugs",
URL = "http://www.foresight.org/Conferences/MNT6/Papers/Globus/index.html",
URL = "http://www.nas.nasa.gov/News/Techreports/1999/PDF/nas-99-005.pdf",
URL = "http://www.nas.nasa.gov/Research/Reports/Techreports/1999/nas-99-005.html",
abstract = "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.",
notes = "http://www.foresight.org/Conferences/MNT6/index.html",
}
@Article{globus:1999:Nano,
title = "Automatic molecular design using evolutionary
techniques",
author = "Al Globus and John Lawton and Todd Wipke",
journal = "Nanotechnology",
volume = "10",
number = "3",
month = sep,
year = "1999",
pages = "290--299",
URL = "http://ej.iop.org/links/20/wT4K9Gv4ZjM1zl3weq3M6Q/na9312.pdf",
URL = "http://www.foresight.org/conference/MNT6/Papers/Globus/index.html",
URL = "http://alglobus.net/NASAwork/papers/Nanotechnology98/paper.html",
URL = "http://people.nas.nasa.gov/~globus/home.html",
doi = "doi:10.1088/0957-4484/10/3/312",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
}
@InProceedings{globus:2000:jgac,
title = "JavaGenes and Condor: Cycle-Scavenging Genetic
Algorithms",
author = "Al Globus and Eric Langhirt and Miron Livny and
Ravishankar Ramamurthy and Marvin Solomon and Steve
Traugott",
booktitle = "Java Grande 2000, sponsored by ACM SIGPLAN",
address = "San Francisco, California",
month = "3-4 " # jun,
year = "2000",
URL = "http://www.cs.wisc.edu/condor/doc/javagenes.pdf",
URL = "http://people.nas.nasa.gov/~globus/papers/JavaGrande2000/JavaGrandePaper.html",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
}
@InProceedings{globus:2001:GECCO,
title = "Graph Crossover",
author = "Al Globus and John Lawton and Todd Wipke",
pages = "761",
year = "2001",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2001)",
editor = "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",
address = "San Francisco, California, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "7-11 " # jul,
keywords = "genetic algorithms, genetic programming, Poster,
graphs, crossover, molecules, drug, design",
ISBN = "1-55860-774-9",
URL = "http://people.nas.nasa.gov/~globus/home.html",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d04.pdf",
notes = "A joint meeting of the tenth International Conference
on Genetic Algorithms (ICGA-2001) and the sixth Annual
Genetic Programming Conference (GP-2001) Part of
\cite{spector:2001:GECCO} see
\cite{globus:2001:GECCOtr}",
}
@Misc{globus:2001:GECCOtr,
author = "Al Globus and Sean Atsatt and John Lawton and Todd
Wipke",
title = "Graph Crossover",
howpublished = "www",
year = "2000",
month = "5 " # may,
keywords = "genetic algorithms, genetic programming",
URL = "http://people.nas.nasa.gov/~globus/papers/JavaGenes2/JavaGenesPaper.html",
abstract = "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.",
notes = "see \cite{globus:2001:GECCO}",
}
@TechReport{globus:t1cpu,
title = "Towards 100,000 {CPU} Cycle-Scavenging by Genetic
Algorithms",
author = "Al Globus",
institution = "CSC at NASA Ames Research Center",
number = "NAS-0-011",
month = oct,
year = "2001",
URL = "http://people.nas.nasa.gov/~globus/papers/Cycle-ScavengingGA/paper.html",
keywords = "genetic algorithms",
abstract = "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.",
}
@InProceedings{globus:jem2001,
title = "JavaGenes: Evolving Molecular Force Field Parameters",
author = "Al Globus and Charles Bauschlicher and Sandra Johan
and Deepak Srivastava",
booktitle = "Ninth Foresight Conference on Molecular
Nanotechnology",
month = "9-11 " # nov,
year = "2001",
address = "Santa Clara, California",
URL = "http://www.foresight.org/Conferences/MNT9/Abstracts/Globus/index.html",
URL = "http://people.nas.nasa.gov/~globus/home.html",
keywords = "genetic algorithms",
}
@Misc{globus:2002:suppercomputer,
author = "Al Globus and Madhu Menon and Deepak Srivastava",
title = "Enabling Computational Nanotechnology through
JavaGenes in a Cycle Scavenging Environment",
howpublished = "www",
year = "2002",
month = jul,
keywords = "genetic algorithms, Condor, Java, distributed",
URL = "http://people.nas.nasa.gov/~globus/papers/JavaGenesSupercomputing2002/finalVersion.pdf",
notes = "Available in MS Word, pdf and html; the pdf and html
versions have problems caused by bugs in the MS
conversion software..",
}
@Article{globus:jem,
title = "JavaGenes: Evolving Molecular Force Field Parameters
with Genetic Algorithm",
author = "Al Globus and Madhu Menon and Deepak Srivastava",
journal = "Computer Modeling in Engineering and Science",
volume = "3",
number = "5",
pages = "557--574",
year = "2002",
URL = "http://people.nas.nasa.gov/~globus/home.html",
keywords = "genetic algorithms",
}
@InProceedings{globus:seof,
title = "Scheduling Earth Observing Fleets Using Evolutionary
Algorithms: Problem Description and Approach",
author = "Al Globus and James Crawford and Jason Lohn and Robert
Morris",
booktitle = "Proceedings of the 3rd International NASA Workshop on
Planning and Scheduling for Space",
address = "Houston, Texas",
month = oct # " 27-29",
year = "2002",
URL = "http://people.nas.nasa.gov/~globus/home.html",
keywords = "genetic algorithms",
}
@InProceedings{globus:emf,
title = "Evolving Molecular Force Field Parameters for Si and
Ge",
author = "Al Globus and Ecleamus Ricks and Madhu Menon and
Deepak Srivastava",
booktitle = "Proceedings of the 2003 Nanotechnology Conference and
Trade Show",
month = feb # " 23-27",
year = "2003",
address = "San Francisco, California, U.S.A.",
URL = "http://people.nas.nasa.gov/~globus/home.html",
keywords = "genetic algorithms",
}
@InProceedings{globus:seo,
title = "Scheduling Earth Observing Satellites with
Evolutionary Algorithms",
author = "Al Globus and James Crawford and Jason Lohn and Anna
Pryor",
booktitle = "International Conference on Space Mission Challenges
for Information Technology (SMC-IT)",
address = "Pasadena, CA, USA",
month = jul,
year = "2003",
URL = "http://people.nas.nasa.gov/~globus/home.html",
keywords = "genetic algorithms",
}
@Article{Gobet:2005:SJP,
author = "Fernand Gobet and Amanda Parker",
title = "Evolving structure-function mappings in cognitive
neuroscience using genetic programming",
journal = "Swiss Journal of Psychology",
year = "2005",
volume = "64",
number = "4",
pages = "231--239",
month = dec,
keywords = "genetic algorithms, genetic programming",
ISSN = "1421-0185",
abstract = "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)",
}
@InProceedings{Gockel:1997:GAsctg,
author = "Nicole Gockel and Martin Keim and Rolf Drechsler and
Bernd Becker",
title = "A Genetic Algorithm for Sequential Circuit Test
Generation based on Symbolic Fault Simulation",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "Genetic Algorithms",
pages = "363--369",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
notes = "GP-97",
}
@Unpublished{Gockel:1997:lheavsr,
author = "Nicole Gockel and Rolf Drechsler and Bernd Becker",
title = "Learning Heuristics by Evolutionary Algorithms with
Variable Size Representation",
note = "Position paper at the Workshop on Evolutionary
Computation with Variable Size Representation at
ICGA-97",
month = "20 " # jul,
year = "1997",
address = "East Lansing, MI, USA",
keywords = "genetic algorithms, Evolvable Hardware, variable size
representation",
size = "3 pages",
}
@InProceedings{Goertzel:2006:CEC,
author = "Ben Goertzel and Cassio Pennachin and Lucio {de Souza
Coelho} and Mauricio Mudado",
title = "Identifying Complex Biological Interactions based on
Categorical Gene Expression Data",
booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary
Computation",
year = "2006",
editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and
Simon M. Lucas",
pages = "5583--5590",
address = "Vancouver",
month = "6-21 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, poster",
ISBN = "0-7803-9487-9",
URL = "http://www.biomind.com/docs/WCCI_EC_feb06_06_fixed_v2.pdf",
size = "8 pages",
abstract = "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.",
notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
the IEE.
IEEE Catalog Number: 06TH8846D",
}
@Article{Goertzel:2006:P,
author = "Benjamin N Goertzel and Cassio Pennachin and Lucio {de
Souza Coelho} and Elizabeth M Maloney and James F Jones
and Brian Gurbaxani",
title = "Allostatic load is associated with symptoms in chronic
fatigue syndrome patients",
journal = "Pharmacogenomics",
year = "2006",
volume = "7",
number = "3",
pages = "485--494",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.2217/14622416.7.3.485",
URL = "http://www.futuremedicine.com/doi/abs/10.2217/14622416.7.3.485",
abstract = "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.",
}
@InProceedings{Goh:2000:GECCO,
author = "Gerard Kian-Meng Goh and James A. Foster",
title = "Evolving Molecules for Drug Design Using Genetic
Algorithms via Molecular Trees",
pages = "27--33",
year = "2000",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2000)",
editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz
and Lee Spector and Ian Parmee and Hans-Georg Beyer",
address = "Las Vegas, Nevada, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "10-12 " # jul,
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-708-0",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/GA141.pdf",
notes = "A joint meeting of the ninth International Conference
on Genetic Algorithms (ICGA-2000) and the fifth Annual
Genetic Programming Conference (GP-2000) Part of
\cite{whitley:2000:GECCO}",
}
@InProceedings{goh:2001:gadsacpc,
author = "C. Goh and Y. Li",
title = "{GA} Automated Design and Synthesis of Analog Circuits
with Practical Constrains",
booktitle = "Proceedings of the 2001 Congress on Evolutionary
Computation CEC2001",
year = "2001",
pages = "170--177",
address = "COEX, World Trade Center, 159 Samseong-dong,
Gangnam-gu, Seoul, Korea",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "27-30 " # may,
organisation = "IEEE Neural Network Council (NNC), Evolutionary
Programming Society (EPS), Institution of Electrical
Engineers (IEE)",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, CAD, Circuit
Synthesis, preferred value components, PSpice",
ISBN = "0-7803-6658-1",
notes = "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.",
}
@InProceedings{goldberg:1998:good,
author = "David E. Goldberg and Una-May O'Reilly",
title = "Where does the Good Stuff Go, and Why? How contextual
semantics influence program structure in simple genetic
programming",
booktitle = "Proceedings of the First European Workshop on Genetic
Programming",
year = "1998",
editor = "Wolfgang Banzhaf and Riccardo Poli and Marc Schoenauer
and Terence C. Fogarty",
volume = "1391",
series = "LNCS",
pages = "16--36",
address = "Paris",
publisher_address = "Berlin",
month = "14-15 " # apr,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-64360-5",
broken = "http://www.ai.mit.edu/people/unamay/papers/eurogp.final.ps",
URL = "http://citeseer.ist.psu.edu/96596.html",
size = "21 pages",
abstract = "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.",
notes = "EuroGP'98 Also presented at the Canadian AI-98
Workshop on Evolutionary Computation Schedule, 17 June
1998 Simon Fraser University Harbour Center, Canada",
}
@InProceedings{goldberg:1999:OGSH,
author = "David E. Goldberg and Siegfried Voessner",
title = "Optimizing Global-Local Search Hybrids",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "220--228",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-882.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-882.pdf",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{goldberg:1999:UTEGACP,
author = "David E. Goldberg",
title = "Using Time Efficiently: Genetic-Evolutionary
Algorithms and the Continuation Problem",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "212--219",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-881.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-881.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{goldfish:1996:nwfmGP,
author = "Andrew Goldfish",
title = "Noisy Wall-Following and Maze Navigation through
Genetic Programming",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "423",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
size = "1 page",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96",
}
@InProceedings{Goldman:2011:GECCOcomp,
author = "Brian W. Goldman and Daniel R. Tauritz",
title = "Self-configuring crossover",
booktitle = "GECCO 2011 1st workshop on evolutionary computation
for designing generic algorithms",
year = "2011",
editor = "Gisele L. Pappa and Alex A. Freitas and Jerry Swan and
John Woodward",
isbn13 = "978-1-4503-0690-4",
keywords = "genetic algorithms, genetic programming",
pages = "575--582",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001858.2002051",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{2002051} Distributed on CD-ROM at
GECCO-2011.
ACM Order Number 910112.",
}
@InProceedings{Golonek:2006:ISCAS,
author = "T. Golonek and D. Grzechca and J. Rutkowski",
title = "Application of genetic programming to edge detector
design",
booktitle = "Proceedings of the IEEE International Symposium on
Circuits and Systems, ISCAS 2006",
year = "2006",
month = "21-24 " # may,
publisher = "IEEE",
note = "4 pp, CD-ROM",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-9389-9",
doi = "doi:10.1109/ISCAS.2006.1693675",
abstract = "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.",
notes = "Inst. of Electron., Silesian Univ. of Technol.,
Gliwice, Poland",
}
@InProceedings{Golonek_2003_ECCTD,
author = "Tomasz Golonek and Jerzy Rutkowski",
title = "Application of Genetic Programming to Analog Fault
Decoder Design",
booktitle = "The 16th European Conference on Circuits Theory and
Design, ECCTD'03",
year = "2003",
address = "Electrical Engineering, AGH University of Science and
Technology, Krakow, Poland",
month = "1-4 " # sep,
organisation = "ECS, IEEE",
keywords = "genetic algorithms, genetic programming",
URL = "http://platforma.polsl.pl/rau3/mod/resource/view.php?id=1324",
URL = "http://platforma.polsl.pl/rau3/mod/resource/Appl.of_GP_to_AFD-ECCTD03.pdf",
size = "4 pages",
abstract = "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.",
notes = "http://ecctd03.zet.agh.edu.pl/docs/program.html",
}
@InProceedings{golovkin:1999:PXSAUGA,
author = "Igor E. Golovkin and Roberto C. Mancini and Sushil J.
Louis",
title = "Plasma {X}-ray Spectra Analysis Using Genetic
Algorithms",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1529--1534",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "real world applications",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-734b.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-734b.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{Golshan:2010:ISSPA,
author = "F. Golshan and K. Mohamadi",
title = "An intelligent watermarking algorithm based on Genetic
Programming",
booktitle = "10th International Conference on Information Sciences
Signal Processing and their Applications (ISSPA 2010)",
year = "2010",
month = may,
pages = "97--100",
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/ISSPA.2010.5605497",
notes = "Fac. of Electr. Eng., Karaj Islamic Azad Univ.,
Rajaeeshahr, Iran Also known as \cite{5605497}",
}
@InProceedings{golubski:1999:eNNsmGP,
author = "Wolfgang Golubski and Thomas Feuring",
title = "Evolving Neural Network Structures by Means of Genetic
Programming",
booktitle = "Genetic Programming, Proceedings of EuroGP'99",
year = "1999",
editor = "Riccardo Poli and Peter Nordin and William B. Langdon
and Terence C. Fogarty",
volume = "1598",
series = "LNCS",
pages = "211--220",
address = "Goteborg, Sweden",
publisher_address = "Berlin",
month = "26-27 " # may,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-65899-8",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1598&spage=211",
notes = "EuroGP'99, part of \cite{poli:1999:GP}",
}
@InProceedings{golubski:2002:EuroGP,
title = "New Results on Fuzzy Regression by Using Genetic
Programming",
author = "Wolfgang Golubski",
editor = "James A. Foster and Evelyne Lutton and Julian Miller
and Conor Ryan and Andrea G. B. Tettamanzi",
booktitle = "Genetic Programming, Proceedings of the 5th European
Conference, EuroGP 2002",
volume = "2278",
series = "LNCS",
pages = "308--315",
publisher = "Springer-Verlag",
address = "Kinsale, Ireland",
publisher_address = "Berlin",
month = "3-5 " # apr,
year = "2002",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-43378-3",
abstract = "In this paper we continue the work on symbolic fuzzy
regression problems. That means that we are interesting
in finding a fuzzy function f with best matches
given data pairs (xi,yi)
1<= i <= k 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.",
notes = "EuroGP'2002, part of \cite{lutton:2002:GP}",
}
@InProceedings{WSEAS_179_Golubski,
author = "Wolfgang Golubski",
title = "Distributed Genetic Programming for Regression
Analysis",
year = "2002",
month = may # "~12-16",
booktitle = "WSEAS IMCCAS-ISA-SOSM and MEM-MCP",
address = "Cancun, Mexico",
keywords = "genetic algorithms, genetic programming, Distributed
Genetic Programming, Symbolic Regression,
Master-Worker",
}
@InProceedings{WSEAS_177_Golubski,
author = "Wolfgang Golubski",
title = "Regression Analysis on Uncertain Data",
year = "2002",
month = may # "~12-16",
booktitle = "WSEAS IMCCAS-ISA-SOSM and MEM-MCP",
address = "Cancun, Mexico",
keywords = "genetic algorithms, genetic programming, Regression
Analysis, Genetic Programming, Fuzzy Numbers,
Evolutionary Algorithm, Fuzzy Application",
}
@InProceedings{Golubski:2002:GPP,
author = "Wolfgang Golubski",
title = "Genetic Programming: {A} Parallel Approach",
booktitle = "Soft-Ware 2002: Computing in an Imperfect World :
First International Conference",
volume = "2311",
pages = "166--173",
year = "2002",
CODEN = "LNCSD9",
ISSN = "0302-9743",
bibdate = "Tue Sep 10 19:09:25 MDT 2002",
URL = "http://link.springer-ny.com/link/service/series/0558/bibs/2311/23110166.htm",
URL = "http://link.springer-ny.com/link/service/series/0558/papers/2311/23110166.pdf",
acknowledgement = ack-nhfb,
editor = "D. Bustard and W. Liu and R. Sterritt",
series = "Lecture Notes in Computer Science",
address = "Belfast, Northern Ireland",
month = "8-10 " # apr,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
}
@Article{Gomez-Pulido:2011:GPEM,
author = "Juan A. Gomez-Pulido and Miguel A. Vega-Rodriguez and
Juan M. Sanchez-Perez and Silvio Priem-Mendes and Vitor
Carreira",
title = "Accelerating floating-point fitness functions in
evolutionary algorithms: a {FPGA}-{CPU}-{GPU}
performance comparison",
journal = "Genetic Programming and Evolvable Machines",
year = "2012",
volume = "12",
number = "4",
pages = "403--427",
month = dec,
keywords = "genetic algorithms, evolvable hardware, EHW,
Evolutionary algorithms, Fitness, Reconfigurable
circuits, GPU, Floating-Point, Performance,
Parallelism",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-011-9137-2",
size = "25 pages",
abstract = "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.",
notes = "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.",
}
@Article{gomi:2003:GPEM,
author = "Takashi Gomi",
title = "Book Review: {Evolutionary} Robotics: the Biology,
Intelligence, and Technology of Self-Organizing
Machines",
journal = "Genetic Programming and Evolvable Machines",
year = "2003",
volume = "4",
number = "1",
pages = "95--98",
month = mar,
keywords = "genetic algorithms, genetic programming, evolvable
hardware, robot",
ISSN = "1389-2576",
doi = "doi:10.1023/A:1021829228076",
abstract = "Review of ISBN:0-262-14070-5 MIT press Authors:
Stefano Nolfi and Dario Floreano",
notes = "Article ID: 5113075",
}
@Article{Goncalves:2010:JIDM,
title = "Automatic Selection of Training Examples for a Record
Deduplication Method Based on Genetic Programming",
author = "Gabriel Silva Goncalves and Moises G. {de Carvalho}
and Alberto H. F. Laender and Marcos Andre Goncalves",
journal = "Journal of Information and Data Management",
year = "2010",
number = "2",
volume = "1",
pages = "213--228",
month = jun,
keywords = "genetic algorithms, genetic programming, replica
identification, artificial intelligence",
ISSN = "2178-7107",
URL = "http://seer.lcc.ufmg.br/index.php/jidm/article/view/59",
size = "16 pages",
bibdate = "2010-11-03",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/jidm/jidm1.html#GoncalvesCLG10",
abstract = "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",
notes = "An official publication of the Brazilian Computer
Society Special Interest Group on Databases",
}
@InProceedings{goncalves:2012:EuroGP,
author = "Ivo Goncalves and Sara Silva and Joana B. Melo and
Joao M. B. Carreiras",
title = "Random Sampling Technique for Overfitting Control in
Genetic Programming",
booktitle = "Proceedings of the 15th European Conference on Genetic
Programming, EuroGP 2012",
year = "2012",
month = "11-13 " # apr,
editor = "Alberto Moraglio and Sara Silva and Krzysztof Krawiec
and Penousal Machado and Carlos Cotta",
series = "LNCS",
volume = "7244",
publisher = "Springer Verlag",
address = "Malaga, Spain",
pages = "218--229",
organisation = "EvoStar",
isbn13 = "978-3-642-29138-8",
doi = "doi:10.1007/978-3-642-29139-5_19",
keywords = "genetic algorithms, genetic programming, Over fitting,
Generalisation",
abstract = "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.",
notes = "Part of \cite{Moraglio:2012:GP} EuroGP'2012 held in
conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012
and EvoApplications2012",
}
@InProceedings{Gonzales:2007:cec,
author = "Eloy Gonzales and Karla Taboada and Kaoru Shimada and
Shingo Mabu and Kotaro Hirasawa and Jinglu Hu",
title = "Class Association Rule Mining for Large and Dense
Databases with Parallel Processing of Genetic Network
Programming",
booktitle = "2007 IEEE Congress on Evolutionary Computation",
year = "2007",
editor = "Dipti Srinivasan and Lipo Wang",
pages = "4615--4622",
address = "Singapore",
month = "25-28 " # sep,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "1-4244-1340-0",
file = "1045.pdf",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and
the IET.
IEEE Catalog Number: 07TH8963C",
}
@InProceedings{Gonzales:2010:gecco,
author = "Eloy Gonzales and Shingo Mabu and Karla Taboada and
Kotaro Hirasawa and Kaoru Shimada",
title = "Pruning association rules using statistics and genetic
relation algoritm",
booktitle = "GECCO '10: Proceedings of the 12th annual conference
on Genetic and evolutionary computation",
year = "2010",
editor = "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",
isbn13 = "978-1-4503-0072-8",
pages = "419--420",
keywords = "genetic algorithms, genetic programming, Evolution
strategies and evolutionary programming, Poster",
month = "7-11 " # jul,
organisation = "SIGEVO",
address = "Portland, Oregon, USA",
doi = "doi:10.1145/1830483.1830562",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{1830562} GECCO-2010 A joint
meeting of the nineteenth international conference on
genetic algorithms (ICGA-2010) and the fifteenth annual
genetic programming conference (GP-2010)",
}
@InProceedings{DBLP:conf/pdp/GonzalezV07,
author = "Daniel Lombrana Gonzalez and Francisco {Fernandez de
Vega}",
title = "On the Intrinsic Fault-Tolerance Nature of Parallel
Genetic Programming",
booktitle = "15th Euromicro Conference on Parallel, Distributed and
Network-based Processing",
year = "2007",
editor = "Pasqua D'Ambra and Mario R. Guarracino",
pages = "450--458",
bibsource = "DBLP, http://dblp.uni-trier.de",
address = "Naples",
month = "7-9 " # feb,
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming, fault
tolerance, parallel genetic programming",
ISBN = "0-7695-2784-1",
ISSN = "1066-6192",
doi = "doi:/10.1109/PDP.2007.56",
abstract = "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.",
notes = "PDP 2007 http://www.na.icar.cnr.it/~pdp2007",
}
@InProceedings{1277302,
author = "Daniel Lombrana Gonzalez and Francisco {Fernandez de
Vega}",
title = "Dynamic populations and length evolution: key factors
for analyzing fault tolerance on parallel genetic
programming",
booktitle = "GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation",
year = "2007",
editor = "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",
volume = "2",
isbn13 = "978-1-59593-697-4",
pages = "1752--1752",
address = "London",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1752.pdf",
doi = "doi:10.1145/1276958.1277302",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
month = "7-11 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming: Poster,
management, measurement, parallel and distributed
evolutionary algorithm, reliability, size evolution,
bloat",
size = "1 page",
abstract = "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.",
notes = "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 \cite{DBLP:conf/pdp/GonzalezV07}",
}
@InProceedings{Gonzalez:2007:cec,
author = "Daniel Lombrana Gonzalez and Francisco {Fernandez de
Vega}",
title = "Analyzing Fault Tolerance on Parallel Genetic
Programming by Means of Dynamic-Size Populations",
booktitle = "2007 IEEE Congress on Evolutionary Computation",
year = "2007",
editor = "Dipti Srinivasan and Lipo Wang",
pages = "4392--4398",
address = "Singapore",
month = "25-28 " # sep,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "1-4244-1340-0",
file = "1666.pdf",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1109/CEC.2007.4425045",
abstract = "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.",
notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and
the IET.
IEEE Catalog Number: 07TH8963C",
}
@InProceedings{Gonzalez:2008:ibergrid,
author = "Daniel Lombrana Gonzalez and Francisco {Fernandez de
Vega} and L. Trujillo and G. Olague and M. Cardenas and
L. Araujo and P. Castillo and K. Sharman and A. Silva",
title = "Interpreted Applications within {BOINC}
Infrastructure",
booktitle = "IBERGRID 2nd Iberian Grid Infrastructure Conference
Proceedings",
year = "2008",
editor = "Fernando Silva and Gaspar Barreira and Ligia Ribeiro",
pages = "261--272",
address = "Porto, Portugal",
publisher_address = "Oleiros (La Coruna), Spain",
month = "12-14 " # may,
publisher = "netbiblo.com",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-84-9745-288-5",
notes = "IBERGRID, ECJ 42 runs 1 week. 41 PCs. R script
(required extension via virtualisation of BOINC
framework). IAP. Java.",
}
@InProceedings{Gonzalez:2009:PDP,
author = "Daniel Lombrana Gonzalez and Francisco {Fernandez de
Vega} and Leonardo Trujillo and Gustavo Olague and
Lourdes Araujo and Pedro Castillo and Juan Julian
Merelo and Ken Sharman",
title = "Increasing {GP} Computing Power for Free via Desktop
{GRID} Computing and Virtualization",
booktitle = "17th Euromicro International Conference on Parallel,
Distributed and Network-based Processing",
year = "2009",
month = "18-20 " # feb,
pages = "419--423",
address = "Weimar, Germany",
isbn13 = "978-0-7695-3544-9",
keywords = "genetic algorithms, genetic programming, BOINC
framework, GP source code, desktop grid computing,
evolutionary algorithms, genetic programming computing
power, volunteer computing, grid computing, software
engineering",
doi = "doi:10.1109/PDP.2009.25",
ISSN = "1066-6192",
abstract = "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).",
notes = "Also known as \cite{4912963}",
}
@InProceedings{Gonzalez:2009:BADS,
author = "Daniel Lombrana Gonzalez and Francisco {Fernandez de
Vega} and Henri Casanova",
title = "Characterizing fault tolerance in genetic
programming",
booktitle = "BADS '09: Proceedings of the 2009 workshop on
Bio-inspired algorithms for distributed systems",
year = "2009",
editor = "Gianluigi Folino and Natalio Krasnogor and Carlo
Mastroianni and Franco Zambonelli",
pages = "1--10",
address = "Barcelona, Spain",
publisher_address = "New York, NY, USA",
month = jun # " 15-19",
publisher = "ACM",
keywords = "genetic algorithms, genetic programming,
Fault-tolerance, parallel genetic programming, desktop
grids",
isbn13 = "978-1-60558-584-0",
URL = "http://navet.ics.hawaii.edu/~casanova/homepage/papers/lombrana_bads2007.pdf",
doi = "doi:10.1145/1555284.1555286",
size = "10 pages",
abstract = "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.",
notes = "Also known as \cite{1555286} even-5-parity,
11-multiplexor",
}
@PhdThesis{LombranaGonzalez:thesis,
author = "D. Daniel {Lombrana Gonzalez}",
title = "Programacion genetica tolerante a fallos: despliegue
de programacion genetica sobre computacion grid de
sobremesa",
school = "Universidad de Extremadura",
year = "2010",
address = "Spain",
keywords = "genetic algorithms, genetic programming",
URL = "https://www.educacion.es/teseo/imprimirFicheroTesis.do?fichero=16774#2010lombrprogr.pdf",
URL = "http://dialnet.unirioja.es/servlet/tesis?codigo=21131",
size = "161 pages",
abstract = "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.",
notes = "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",
}
@Article{Gonzalez:2010:FGCS,
author = "Daniel {Lombrana Gonzalez} and Francisco {Fernandez de
Vega} and Henri Casanova",
title = "Characterizing fault tolerance in genetic
programming",
journal = "Future Generation Computer Systems",
year = "2010",
volume = "26",
number = "6",
pages = "847--856",
month = jun,
keywords = "genetic algorithms, genetic programming, Fault
tolerance, Parallel genetic programming, Desktop
grids",
ISSN = "0167-739X",
URL = "http://www.sciencedirect.com/science/article/B6V06-4YDT3S4-2/2/0a9075d8d9c6905e388ad608f0c81e79",
doi = "doi:10.1016/j.future.2010.02.006",
size = "10 pages",
abstract = "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.",
notes = "5.1.1. Even parity 5 5.1.2. 11-bit multiplexer",
}
@Article{gonzalez:2003:GPEM,
author = "Fabio A. Gonzalez and Dipankar Dasgupta",
title = "Anomaly Detection Using Real-Valued Negative
Selection",
journal = "Genetic Programming and Evolvable Machines",
year = "2003",
volume = "4",
number = "4",
pages = "383--403",
month = dec,
keywords = "artificial immune systems, anomaly detection, negative
selection, matching rule, self-organizing maps",
ISSN = "1389-2576",
doi = "doi:10.1023/A:1026195112518",
abstract = "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.",
notes = "Special issue on artificial immune systems Article ID:
5144849",
}
@InProceedings{DBLP:conf/gecco/GonzalezH09,
author = "Gerardo Gonzalez and Dean F. Hougen",
title = "Elitism, fitness, and growth",
booktitle = "GECCO '09: Proceedings of the 11th Annual conference
on Genetic and evolutionary computation",
year = "2009",
editor = "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",
pages = "1851--1852",
address = "Montreal",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "8-12 " # jul,
organisation = "SigEvo",
keywords = "genetic algorithms, genetic programming, Poster",
isbn13 = "978-1-60558-325-9",
bibsource = "DBLP, http://dblp.uni-trier.de",
doi = "doi:10.1145/1569901.1570199",
abstract = "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.",
notes = "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.",
}
@InProceedings{GonzalezMunoz:2005:RVK,
author = "David {Gonzalez Munoz} and Oscar Gustafsson and Lars
Wanhammar",
title = "Evolution of filter order equations for linear-phase
{FIR} filters using gene expression programming",
booktitle = "RVK 2005 RadioVetenskap och Kommunikation",
year = "2005",
pages = "679--682",
address = "Linkoping, Sweden",
month = "14-16 " # jun,
organisation = "FOI",
keywords = "genetic algorithms, genetic programming, gene
expression programming",
URL = "http://www.es.isy.liu.se/publications/papers_and_reports/2005/RVK05_oscarg_FIRorder.pdf",
abstract = "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.",
notes = "http://www.rvk05.foi.se/ P18T
http://www.rvk05.foi.se/Sessions_final.html
Linkoping University, Linkoping, Sweden
",
}
@InProceedings{GonzalezPadilla:2010:CERMA,
author = "Omar Alfrego Gonzalez Padilla and Felix Francisco
Ramos Corchado and Jean-Paul Bartes",
title = "Genetic Programming for Task Selection in Dialogue
Systems",
booktitle = "Electronics, Robotics and Automotive Mechanics
Conference (CERMA), 2010",
year = "2010",
month = "28 " # sep # "-1 " # oct,
pages = "180--184",
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/CERMA.2010.30",
notes = "Also known as \cite{5692333}",
}
@InProceedings{Gonzalez-Pardo:2011:AoGEAtREI,
title = "Analysis of Grammatical Evolution Approaches to
Regular Expression Induction",
author = "Antonio Gonzalez-Pardo and David Camacho",
pages = "632--639",
booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary
Computation",
year = "2011",
editor = "Alice E. Smith",
month = "5-8 " # jun,
address = "New Orleans, USA",
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, Data mining",
abstract = "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.",
notes = "CEC2011 sponsored by the IEEE Computational
Intelligence Society, and previously sponsored by the
EPS and the IET.",
}
@InProceedings{goodacre:1999:ERDEC,
author = "R. Goodacre and B. Shann and R. J. Gilbert and E. M.
Timmins and A. C. McGovern and B. K. Alsberg and N. A.
Logan and D. B. Kell",
title = "The characterisation of Bacillus species from {PyMS}
and {FT IR} data",
booktitle = "Proceedings of the 1997 ERDEC Scientific Conference on
Chemical and Biological Defense Research",
year = "1997",
number = "ERDEC-SP-063",
address = "Aberdeen Proving Ground",
keywords = "genetic algorithms, genetic programming",
}
@Article{goodacre:1999:dcvbcppmsGP,
author = "R. Goodacre and R. J. Gilbert",
title = "The detection of caffeine in a variety of beverages
using Curie-point pyrolysis mass spectrometry and
genetic programming",
journal = "The Analyst",
year = "1999",
volume = "124",
pages = "1069--1074",
keywords = "genetic algorithms, genetic programming",
URL = "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",
abstract = "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.",
}
@Article{goodacre:2000:ddabmbscppmsftis,
author = "Royston Goodacre and Beverley Shann and Richard J.
Gilbert and Eadaoin M. Timmins and Aoife C. McGovern
and Bjorn K. Alsberg and Douglas B. Kell and Niall A.
Logan",
title = "The detection of the dipicolinic acid biomarker in
Bacillus spores using Curie-point pyrolysis mass
spectrometry and Fourier-transform infrared
spectroscopy",
journal = "Analytical Chemistry",
year = "2000",
volume = "72",
number = "1",
pages = "119--127",
month = "1 " # jan,
publisher = "American Chamical Society",
keywords = "genetic algorithms, genetic programming",
URL = "http://pubs.acs.org/cgi-bin/article.cgi/ancham/2000/72/i01/html/ac990661i.html",
doi = "doi:10.1021/ac990661i",
abstract = "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.",
notes = "
PMID: 10655643",
}
@InCollection{Goodacre:2003:MP13,
author = "R. Goodacre and D. B. Kell",
title = "Evolutionary Computation for the Interpretation of
Metabolomic Data",
booktitle = "Metabolic Profiling: Its Role in Biomarker Discovery
and Gene Function Analysis",
publisher = "Kluwer Academic Publishers",
year = "2003",
editor = "George G. Harrigan and Royston Goodacre",
chapter = "13",
address = "Boston, USA",
month = jan,
keywords = "genetic algorithms, genetic programming",
ISBN = "1-4020-7370-4",
notes = "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",
}
@Article{goodacre:2003:cdupx,
author = "Royston Goodacre and Emma V. York and James K. Heald
and Ian M. Scott",
title = "Chemometric discrimination of unfractionated plant
extracts analyzed by electrospray mass spectrometry",
journal = "Phytochemistry",
year = "2003",
volume = "62",
number = "6",
pages = "859--863",
month = mar,
keywords = "genetic algorithms, genetic programming, Pharbitis
nil, Convolvulaceae, Japanese Morning Glory,
Electrospray ionization mass spectrometry, Neural
networks, Metabolic fingerprinting",
URL = "http://www.sciencedirect.com/science/article/B6TH7-47WBXD4-7/2/91ff09f988be54824c55a1cb596f7839",
doi = "doi:10.1016/S0031-9422(02)00718-5",
abstract = "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.",
notes = "GMax-Bio, Plant Metabolomics",
}
@Article{Goodacre:2003:VS,
author = "Royston Goodacre",
title = "Explanatory analysis of spectroscopic data using
machine learning of simple, interpretable rules",
journal = "Vibrational Spectroscopy",
year = "2003",
volume = "32",
pages = "33--45",
number = "1",
month = "5 " # aug,
note = "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",
keywords = "genetic algorithms, genetic programming, Artificial
neural networks, ANN, FT-IR",
ISSN = "0924-2031",
URL = "http://www.biospec.net/learning/Metab06/Goodacre-FTIRmaps.pdf",
URL = "http://www.sciencedirect.com/science/article/B6THW-48XJP5P-2/2/64840c1f311b856106e124993425ab92",
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.147.8811",
doi = "doi:10.1016/S0924-2031(03)00045-6",
language = "en",
oai = "oai:CiteSeerXPSU:10.1.1.147.8811",
abstract = "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.",
owner = "wlangdon",
}
@Article{goodacre:2004:TB,
author = "Royston Goodacre and Seetharaman Vaidyanathan and
Warwick B. Dunn and George G. Harrigan and Douglas B.
Kell",
title = "Metabolomics by numbers: acquiring and understanding
global metabolite data",
journal = "Trends in Biotechnology",
year = "2004",
volume = "22",
number = "5",
pages = "245--252",
month = "1 " # may,
keywords = "genetic algorithms, genetic programming, ILP",
URL = "http://dbkgroup.org/Papers/trends%20in%20biotechnology_22_(245).pdf",
doi = "doi:10.1016/j.tibtech.2004.03.007",
abstract = "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.",
notes = "many topics covered not just GP",
}
@Article{Goodacre:2007:m,
author = "Royston Goodacre and David Broadhurst and Age K.
Smilde and Bruce S. Kristal and J. David Baker and
Richard Beger and Conrad Bessant and Susan Connor and
Giorgio Capuani and Andrew Craig and Tim Ebbels and
Douglas B. Kell and Cesare Manetti and Jack Newton and
Giovanni Paternostro and Ray Somorjai and Michael
Sjostrom and Johan Trygg and Florian Wulfert",
title = "Proposed minimum reporting standards for data analysis
in metabolomics",
journal = "Metabolomics",
year = "2007",
volume = "3",
pages = "231--241",
keywords = "genetic algorithms, genetic programming, Chemometrics,
Multivariate, Megavariate Unsupervised learning,
Supervised learning, Informatics Bioinformatics,
Statistics, Biostatistics",
URL = "http://dbkgroup.org/Papers/goodacre_MSIdataanalysis07.pdf",
doi = "doi:10.1007/s11306-007-0081-3",
size = "11 pages",
abstract = "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.",
notes = "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",
}
@Proceedings{goodman:2001:GECCOlb,
title = "Late Breaking Papers at the 2001 Genetic and
Evolutionary Computation Conference",
year = "2001",
editor = "Erik Goodman",
address = "San Francisco, California, USA",
month = "7-11 " # jul,
size = "pages",
}
@InProceedings{GARAGe02-01-01,
author = "Erik D. Goodman and Kisung Seo and Ronald C. Rosenberg
and Zhun Fan and Jianjun Hu and Baihai Zhang",
title = "Automated Design Methodology for Mechatronic Systems
Using Bond Graphs and Genetic Programming",
booktitle = "Proceedings 2002 NSF Design, Service and Manufacturing
Grantees and Research Conference",
year = "2002",
pages = "206--221",
address = "San Juan, Puerto Rico",
month = jan,
organization = "National Science Foundation",
publisher = "National Science Foundation",
keywords = "genetic algorithms, genetic programming",
URL = "http://garage.cse.msu.edu/papers/GARAGe02-01-01.pdf",
size = "16 pages",
abstract = "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.",
}
@Article{goodman:2004:GPEM,
author = "Erik D. Goodman",
title = "A Word from the Chair of {ISGEC}",
journal = "Genetic Programming and Evolvable Machines",
year = "2004",
volume = "5",
number = "1",
pages = "9",
month = mar,
ISSN = "1389-2576",
doi = "doi:10.1023/B:GENP.0000017052.83908.eb",
notes = "Chair of the Executive Board, International Society
for Genetic and Evolutionary Computation Article ID:
5264732",
}
@InProceedings{Gopalakrishnan:2010:ANNIE,
author = "Kasthurirangan Gopalakrishnan and Halil Ceylan and
Sunghwan Kim and Siddhartha K. Khaitan",
title = "Natural Selection of Asphalt Mix Stiffness Predictive
Models with Genetic Programming",
booktitle = "ANNIE 2010, Intelligent Engineering Systems through
Artificial Neural Networks",
year = "2010",
editor = "Cihan H. Dagli",
volume = "20",
pages = "paper 48",
address = "St. Louis, Mo, USA",
month = nov # " 1-3",
organisation = "Smart Engineering Systems Laboratory, Systems
Engineering Graduate Programs, Missouri University of
Science and Technology, 600 W. 14th St., Rolla, MO
65409 USA",
publisher = "ASME",
keywords = "genetic algorithms, genetic programming",
isbn13 = "9780791859599",
doi = "doi:10.1115/1.859599.paper48",
abstract = "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.",
notes = "http://annie.mst.edu/conference_schedule/ConferenceSchedule.html
ASME Order Number: 859599",
}
@InProceedings{gordillo:1997:ocipGPpa,
author = "F. Gordillo and A. Bernal",
title = "Optimal Control of an Inverted Pendulum Using Genetic
Programming: Practical Aspects",
booktitle = "Artificial Neural Nets and Genetic Algorithms:
Proceedings of the International Conference,
ICANNGA97",
year = "1997",
editor = "George D. Smith and Nigel C. Steele and Rudolf F.
Albrecht",
address = "University of East Anglia, Norwich, UK",
publisher = "Springer-Verlag",
note = "published in 1998",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-211-83087-1",
notes = "http://www.sys.uea.ac.uk/Research/ResGroups/MAG/ICANNGA97/papers_frame.html",
}
@InProceedings{gordillo:1999:ATSDGCA,
author = "Francisco Gordillo and Ismael Alcala and Javier
Aracil",
title = "A Tool for Solving Differential Games with
Co-evolutionary Algorithms",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1535--1542",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "real world applications",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-775.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-775.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InCollection{gordon:1994:usni,
author = "Benjamin M. Gordon",
title = "Exploring the Underlying Structure of Natural Images
Through Genetic Programming",
booktitle = "Genetic Algorithms at Stanford 1994",
year = "1994",
editor = "John R. Koza",
pages = "49--56",
address = "Stanford, California, 94305-3079 USA",
month = dec,
organisation = "Stanford University",
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming, MSE, pixels",
ISBN = "0-18-187263-3",
notes = "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",
}
@Article{Gordon:2006:IS,
title = "Adaptive Web Search: Evolving a Program That Finds
Information",
author = "Michael Gordon and Weiguo (Patrick) Fan and Praveen
Pathak",
journal = "IEEE Intelligent Systems",
year = "2006",
volume = "21",
number = "5",
pages = "72--77",
month = sep # "-" # oct,
keywords = "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",
ISSN = "1541-1672",
doi = "doi:10.1109/MIS.2006.86",
size = "6 pages",
abstract = "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",
notes = "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.",
}
@InProceedings{gordon:1999:TGAMPST,
author = "V. Scott Gordon and Rebecca Pirie and Adam Wachter and
Scottie Sharp",
title = "Terrain-Based Genetic Algorithm ({TBGA}): Modeling
Parameter Space as Terrain",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "229--235",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/tbga.pdf",
URL = "http://ecs.csus.edu/~gordonvs/papers/tbga.pdf",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@Article{gordon:2001:GPEM,
author = "Timothy G. W. Gordon",
title = "Book Review: {Hardware} evolution: automatic design of
electronic circuits in reconfigurable hardware by
artificial evolution",
journal = "Genetic Programming and Evolvable Machines",
year = "2001",
volume = "2",
number = "4",
pages = "409--411",
month = dec,
keywords = "genetic algorithms, evolvable hardware",
ISSN = "1389-2576",
doi = "doi:10.1023/A:1012930922211",
notes = "Book review of ISBN: 3-540-76253-1 Author: Adrian
Thompson Publisher: Springer-Verlag London Ltd.
1998.
Article ID: 386364",
}
@InProceedings{gordon:2005:EH,
author = "Timothy G. W. Gordon and Peter J. Bentley",
title = "Development Brings Scalability to Hardware Evolution",
booktitle = "Proceedings of the 2005 NASA/DoD Conference on
Evolvable Hardware",
year = "2005",
editor = "Jason Lohn and David Gwaltney and Gregory Hornby and
Ricardo Zebulum and Didier Keymeulen and Adrian
Stoica",
pages = "272--279",
address = "Washington, DC, USA",
month = "29 " # jun # "-1 " # jul,
publisher = "IEEE Press",
publisher_address = "IEEE Service Center 445 Hoes Lane Asia P.O. Box
1331 Piscataway, NJ 08855-1331",
organisation = "NASA, DoD",
keywords = "genetic algorithms, genetic programming, EHW",
ISBN = "0-7695-2399-4",
URL = "http://www.cs.ucl.ac.uk/staff/t.gordon/gordont_scalability.pdf",
doi = "doi:10.1109/EH.2005.18",
size = "8 pages",
abstract = "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.",
notes = "EH2005 IEEE Computer Society Order Number P2399",
}
@PhdThesis{tgordon,
author = "Timothy Glennie Wilson Gordon",
title = "Exploiting Development to Enhance the Scalability of
Hardware Evolution",
school = "University College, London",
year = "2005",
month = jul,
keywords = "genetic algorithms, genetic programming, EHW",
URL = "http://www.bcs.org/upload/pdf/tgordon.pdf",
size = "302 pages",
abstract = "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.",
notes = "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.",
}
@InProceedings{gorges-schleuter:1999:AALSES,
author = "Martina Gorges-Schleuter",
title = "An Analysis of Local Selection in Evolution
Strategies",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "847--854",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "evolution strategies and evolutionary programming",
ISBN = "1-55860-611-4",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@Article{Gosling:2012:GPEM,
author = "Timothy Gosling",
title = "Moshe Sipper: Evolved to Win",
journal = "Genetic Programming and Evolvable Machines",
year = "2012",
volume = "13",
number = "2",
pages = "269--270",
month = jun,
note = "Book review",
keywords = "genetic algorithms, genetic programming",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-012-9157-6",
affiliation = "The Creative Assembly, Horsham, England",
}
@InProceedings{1277011,
author = "Stanley Phillips Gotshall and Terence Soule",
title = "Stochastic training of a biologically plausible
spino-neuromuscular system model",
booktitle = "GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation",
year = "2007",
editor = "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",
volume = "1",
isbn13 = "978-1-59593-697-4",
pages = "253--260",
address = "London",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p253.pdf",
doi = "doi:10.1145/1276958.1277011",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
month = "7-11 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "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",
abstract = "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.",
notes = "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",
}
@Article{Gotshall:2007:GPEM,
author = "Stanley Gotshall and Kathy Browder and Jessica Sampson
and Terence Soule and Richard Wells",
title = "Stochastic optimization of a biologically plausible
spino-neuromuscular system model",
journal = "Genetic Programming and Evolvable Machines",
year = "2007",
volume = "8",
number = "4",
pages = "355--380",
month = dec,
note = "special issue on medical applications of Genetic and
Evolutionary Computation",
keywords = "genetic algorithms, Biological neural networks,
Particle swarm optimisers, PSO, Breeding swarm
optimisers",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-007-9044-8",
abstract = "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.",
notes = "See Erratum \cite{Gotshall:2011:GPEM}",
}
@Article{Gotshall:2011:GPEM,
author = "Stanley Gotshall and Kathy Browder and Jessica Sampson
and Terence Soule and Richard Wells",
title = "Erratum to: Stochastic optimization of a biologically
plausible spino-neuromuscular system model {A}
comparison with human subjects",
journal = "Genetic Programming and Evolvable Machines",
year = "2011",
volume = "12",
number = "1",
pages = "87--88",
month = mar,
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-010-9108-z",
size = "2 pages",
abstract = "The on line version of the original article can be
found under doi:10.1007/s10710-007-9044-8.",
notes = "Fig 11, Equation 5 and Sec 4.4 in
\cite{Gotshall:2007:GPEM}",
affiliation = "Department of Computer Science, University of Idaho,
Moscow, ID USA",
}
@InProceedings{gottlieb:1999:EAMKPRBFR,
author = "Jens Gottlieb",
title = "Evolutionary Algorithms for Multidimensional Knapsack
Problems: the Relevance of the Boundary f the Feasible
Region",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "787",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems, poster
papers",
ISBN = "1-55860-611-4",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@Misc{gounares:2001:patent,
author = "Alexander Gounares and Prakash Sikchi",
title = "Adaptive problem solving method and apparatus
utilizing evolutionary computation techniques",
howpublished = "U.S. Patent",
year = "2001",
month = "28 " # aug,
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "6,282,527 Assignee: Microsoft Corporation (Redmond,
WA)",
}
@InProceedings{graae:2000:svhrGP,
author = "Cristopher T. M. Graae and Peter Nordin and Mats
Nordahl",
title = "Stereoscopic Vision for a Humanoid Robot Using Genetic
Programming",
booktitle = "Real-World Applications of Evolutionary Computing",
year = "2000",
editor = "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",
volume = "1803",
series = "LNCS",
pages = "12--21",
address = "Edinburgh",
publisher_address = "Berlin",
month = "17 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-67353-9",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1803&spage=12",
notes = "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",
}
@InCollection{Graf:Banzhaf:EA95,
author = "Jeanine Graf and Wolfgang Banzhaf",
title = "Interactive Evolution for Simulated Natural
Evolution",
booktitle = "Artificial Evolution",
publisher = "Springer Verlag",
year = "1996",
editor = "Jean-Marc Alliot and Evelyne Lutton and Edmund Ronald
and Marc Schoenauer and Dominique Snyers",
volume = "1063",
series = "LNCS",
pages = "259--272",
keywords = "genetic algorithms, genetic programming, Growth,
Paleontology, Evolutionary Algorithms, Simulation of
Natural Evolution",
isbn13 = "978-3-540-61108-0",
doi = "doi:10.1007/3-540-61108-8_43",
size = "14 pages",
abstract = "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.",
notes = "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",
affiliation = "Informatik Centrum Dortmund (ICD) 44227 Dortmund
Germany 44227 Dortmund Germany",
}
@InProceedings{Graff:2008:eurogp,
title = "Practical Model of Genetic Programming's Performance
on Rational Symbolic Regression Problems",
author = "Mario Graff and Riccardo Poli",
bibdate = "2008-04-15",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#GraffP08",
booktitle = "Proceedings of the 11th European Conference on Genetic
Programming, EuroGP 2008",
address = "Naples",
month = "26-28 " # mar,
publisher = "Springer",
year = "2008",
volume = "4971",
editor = "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",
isbn13 = "978-3-540-78670-2",
pages = "122--133",
series = "Lecture Notes in Computer Science",
doi = "doi:10.1007/978-3-540-78671-9_11",
keywords = "genetic algorithms, genetic programming",
notes = "Also known as \cite{conf/eurogp/GraffP08} Least Angle
Regression (LAR). p151 {"}Angle between GP systems{"}.
System used by Koza \cite{koza:book} and TinyGP
\cite{poli08:fieldguide}. neato.
Part of \cite{conf/eurogp/2008} EuroGP'2008 held in
conjunction with EvoCOP2008, EvoBIO2008 and
EvoWorkshops2008",
}
@InProceedings{Graff:2009:eurogp,
author = "Mario Graff and Riccardo Poli",
title = "Automatic Creation of Taxonomies of Genetic
Programming Systems",
booktitle = "Proceedings of the 12th European Conference on Genetic
Programming, EuroGP 2009",
year = "2009",
editor = "Leonardo Vanneschi and Steven Gustafson and Alberto
Moraglio and Ivanoe {De Falco} and Marc Ebner",
volume = "5481",
series = "LNCS",
pages = "145--158",
address = "Tuebingen",
month = apr # " 15-17",
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-01180-1",
doi = "doi:10.1007/978-3-642-01181-8_13",
size = "14 pages",
abstract = "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.",
notes = "Part of \cite{conf/eurogp/2009} EuroGP'2009 held in
conjunction with EvoCOP2009, EvoBIO2009 and
EvoWorkshops2009",
}
@Article{Graff20101254,
author = "Mario Graff and Riccardo Poli",
title = "Practical performance models of algorithms in
evolutionary program induction and other domains",
journal = "Artificial Intelligence",
volume = "174",
number = "15",
pages = "1254--1276",
year = "2010",
ISSN = "0004-3702",
doi = "doi:10.1016/j.artint.2010.07.005",
URL = "http://www.sciencedirect.com/science/article/B6TYF-50KWG15-1/2/3fb87252c46b990fe9a47f5dbd261a82",
keywords = "genetic algorithms, genetic programming, Evolution
algorithms, Program induction, Performance prediction,
Algorithm taxonomies, Algorithm selection problem",
abstract = "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.",
}
@InProceedings{graff:2011:EuroGP,
author = "Mario Graff and Riccardo Poli",
title = "Performance Models for Evolutionary Program Induction
Algorithms based on Problem Difficulty Indicators",
booktitle = "Proceedings of the 14th European Conference on Genetic
Programming, EuroGP 2011",
year = "2011",
month = "27-29 " # apr,
editor = "Sara Silva and James A. Foster and Miguel Nicolau and
Mario Giacobini and Penousal Machado",
series = "LNCS",
volume = "6621",
publisher = "Springer Verlag",
address = "Turin, Italy",
pages = "118--129",
organisation = "EvoStar",
keywords = "genetic algorithms, genetic programming, cartesian
genetic programming",
isbn13 = "978-3-642-20406-7",
doi = "doi:10.1007/978-3-642-20407-4_11",
abstract = "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.",
notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in
conjunction with EvoCOP2011 EvoBIO2011 and
EvoApplications2011",
}
@InProceedings{graham:1998:opdidcGA,
author = "Jonathan M. Graham",
title = "Optimal Placement of Distributed Iterrelated Data
Components using Genetic Algorithms",
booktitle = "Late Breaking Papers at the Genetic Programming 1998
Conference",
year = "1998",
editor = "John R. Koza",
pages = "52--58",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "Stanford, CA, USA",
month = "22-25 " # jul,
publisher = "Stanford University Bookstore",
keywords = "genetic algorithms, OX, EM",
notes = "GP-98LB
",
}
@InProceedings{Graham:2009:eurogp,
author = "Lee Graham and Rob Cattral and Franz Oppacher",
title = "Beneficial Preadaptation in the Evolution of a 2{D}
Agent Control System with Genetic Programming",
booktitle = "Proceedings of the 12th European Conference on Genetic
Programming, EuroGP 2009",
year = "2009",
editor = "Leonardo Vanneschi and Steven Gustafson and Alberto
Moraglio and Ivanoe {De Falco} and Marc Ebner",
volume = "5481",
series = "LNCS",
pages = "303--314",
address = "Tuebingen",
month = apr # " 15-17",
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, poster",
isbn13 = "978-3-642-01180-1",
doi = "doi:10.1007/978-3-642-01181-8_26",
notes = "Part of \cite{conf/eurogp/2009} EuroGP'2009 held in
conjunction with EvoCOP2009, EvoBIO2009 and
EvoWorkshops2009",
}
@Article{graham-rowe:1999:elvis,
author = "Duncan Graham-Rowe",
title = "Elvis Lives",
journal = "New Scientist",
year = "1999",
month = "21 " # aug,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.newscientist.com/ns/19990821/newsstory4.html",
size = "2 pages",
abstract = "Description of Peter Nordin humanoid robot Elvis",
}
@Article{graham-rowe:2001:egp,
author = "Duncan Graham-Rowe",
title = "Evolve or die",
journal = "New Scientist",
year = "2001",
month = "27 " # oct,
keywords = "genetic algorithms, genetic programming, enzyme
genetic programming",
URL = "http://www.newscientist.com/hottopics/tech/article.jsp?id=23142200&sub=Computing",
size = "1 page",
abstract = "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.",
notes = "Michael A Lones",
}
@Article{graham-rowe:2002:radio,
author = "Duncan Graham-Rowe",
title = "Radio emerges from the electronic soup",
journal = "New Scientist",
year = "2002",
month = "13 " # aug,
keywords = "genetic algorithms, evolvable hardware",
URL = "http://www.newscientist.com/news/news.jsp?id=ns99992732",
size = "1 page",
abstract = "A self-organising electronic circuit has stunned
engineers by turning itself into a radio receiver.",
notes = "Paul Layzell and Jon Bird at the University of
Sussex",
}
@Article{graham-rowe:2005:complearn,
author = "Duncan Graham-Rowe",
title = "Google's search for meaning",
journal = "New Scientist",
year = "2005",
volume = "2484",
pages = "21",
month = "29 " # jan,
keywords = "genetic algorithms, genetic programming, complearn",
URL = "http://www.newscientist.com/channel/info-tech/mg18524846.100",
size = "1 page",
abstract = "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.",
notes = "Paul Vitanyi and Rudi Cilibrasi at the www.CWI.nl
\cite{cs.CL/0412098}
see
http://homepages.cwi.nl/~paulv/lectures/google-lecture.pdf",
}
@Proceedings{Grahl:2006:GECCO:lbp,
title = "Late breaking papers at Genetic and Evolutionary
Computation Conference {(GECCO'2006)}",
year = "2006",
month = "8-12 " # jul,
editor = "J{\"{o}}rn Grahl",
address = "Seattle, WA, USA",
keywords = "genetic algorithms, genetic programming, MOO, PSO, NN,
LCS",
notes = "Distributed on CD-ROM at GECCO-2006",
}
@InProceedings{grand:1997:creatures,
author = "Stephen Grand and Dave Cliff and Anil Malhotra",
title = "Creatures: Artificial Life Autonmous Software Agents
for Home Entertainment",
booktitle = "The First International Conference on Autonomous
Agents (Agents '97)",
year = "1997",
editor = "W. Lewis Johnson",
pages = "22--29",
address = "Marina del Rey, California, USA",
publisher_address = "1515 Broadway, New York, NY 10036, USA",
month = feb # " 5-8",
organisation = "ACM SIGART",
publisher = "ACM Press",
keywords = "Arificial Life",
ISBN = "0-89791-877-0",
notes = "http://www.isi.edu/isd/AA97/info.html",
}
@MastersThesis{grant:msc,
author = "Michael S. Grant",
title = "An Investigation into Genetic Programming",
school = "Department of Computer Science and Applied
Mathematics, Aston University",
year = "1996",
address = "Birmingham, UK",
month = sep,
email = "michael.grant@bbc.co.uk",
email = "gp@michael-grant.me.uk",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.michael-grant.me.uk/msc.zip broken",
size = "150 pages",
abstract = "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.",
}
@PhdThesis{grant:phd,
author = "Michael Sean Grant",
title = "An Investigation into the Suitability of Genetic
Programming for Computing Visibility Areas for Sensor
Planning",
school = "Department of Computing and Electrical Engineering,
Heriot-Watt University",
year = "2000",
address = "Riccarton, Edinburgh EH14 4AS, United Kingdom",
month = may,
email = "gp@michael-grant.me.uk",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.michael-grant.me.uk/phd.zip broken",
size = "293 pages",
abstract = "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.",
}
@TechReport{gray:1996:ssi,
author = "G. J. Gray and Yun Li and D. J. Murray-Smith and K. C.
Sharman",
title = "Structural System Identification Using Genetic
Programming and a Block Diagram Oriented Simulation
Tool",
institution = "Department of Electronics and Electrical Engineering,
University of Glasgow",
year = "1996",
type = "Technical Report",
number = "CSC-96003",
address = "Glasgow, G12 8QQ, U.K.",
month = "13 " # jun,
note = "Submitted to: Electronics Letters",
keywords = "genetic algorithms, genetic programming, system
identification, nonlinear mathematical modelling,
SIMULINK",
URL = "http://www.mech.gla.ac.uk/Research/Control/Publications/Reports/csc96003.ps",
abstract = "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",
notes = "See \cite{gray:1996:ssi2}
",
}
@Article{gray:1996:ssi2,
author = "Gary J. Gray and Yun Li and D. J. Murray-Smith and K.
C. Sharman",
title = "Structural system identification using genetic
programming and a block diagram oriented simulation
tool",
journal = "Electronics Letters",
year = "1996",
volume = "32",
number = "15",
pages = "1422--1424",
month = "18 " # jul,
keywords = "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",
ISSN = "0013-5194",
URL = "http://ieeexplore.ieee.org/iel1/2220/11173/00511160.pdf?isNumber=11173",
abstract = "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.",
notes = "See also \cite{gray:1996:ssi} SIMULINK, MATLAB.
Numerical parameters optimised using combination of
Nelder simplex minimisation and simulated annealing.
A.P.Fraser's gpc++.",
}
@InProceedings{gray:1996:nmsti,
author = "Gary J. Gray and David J. Murray-Smith and Yun Li and
Ken C. Sharman",
title = "Nonlinear Model Structure Identification Using Genetic
Programming",
booktitle = "Late Breaking Papers at the Genetic Programming 1996
Conference Stanford University July 28-31, 1996",
year = "1996",
editor = "John R. Koza",
pages = "32--37",
address = "Stanford University, CA, USA",
publisher_address = "Stanford University, Stanford, California
94305-3079, USA",
month = "28--31 " # jul,
publisher = "Stanford Bookstore",
ISBN = "0-18-201031-7",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.mech.gla.ac.uk/Research/Control/Publications/Reports/csc96006.ps",
URL = "http://citeseer.ist.psu.edu/60878.html",
abstract = "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.",
notes = "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",
}
@InProceedings{Gray:1997:ISMM,
author = "Gary J. Gray and David J. Murray-Smith and Yun Li and
Ken C. Sharman",
title = "Nonlinear Structural System Identification Using
Genetic Programming",
booktitle = "Proceedings of Second International Symposium on
Mathematical modelling",
year = "1997",
editor = "Inge Troch and Felix Breitenecker",
number = "11",
series = "ARGESIM Report Series",
pages = "301--306",
address = "Technical University Vienna, Austria",
month = "5-7 " # feb,
organisation = "IMACS/IFAC",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-901608-11-7",
notes = "http://web.iti.upv.es/~ken/kenpubs.html
http://polaris.dit.upm.es/~jpuente/ifac/newsletter497/mathmod.html",
}
@InProceedings{gray:1997:,
author = "G. J. Gray and T. Weinbrenner and D. J. Murray-Smith
and Y. Li and K. C. Sharman",
title = "Issues in Nonlinear Model Structure Identification
Using Genetic Programming",
booktitle = "Second International Conference on Genetic Algorithms
in Engineering Systems: Innovations and Applications,
GALESIA",
year = "1997",
editor = "Ali Zalzala",
pages = "308--313",
address = "University of Strathclyde, Glasgow, UK",
publisher_address = "Savoy Place, London WC2R 0BL, UK",
month = "1-4 " # sep,
publisher = "Institution of Electrical Engineers",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-85296-693-8",
URL = "http://scitation.aip.org/getpdf/servlet/GetPDFServlet?filetype=pdf&id=IEECPS0019970CP446000308000001&idtype=cvips&prog=normal",
doi = "doi:10.1049/cp:19971198",
size = "6 page",
abstract = "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.",
notes = "GALESIA'97",
}
@Article{Gray:1998:CEP,
author = "Gary J. Gray and David J. Murray-Smith and Yun Li and
Ken C. Sharman and Thomas Weinbrenner",
title = "Nonlinear model structure identification using genetic
programming",
journal = "Control Engineering Practice",
volume = "6",
pages = "1341--1352",
year = "1998",
number = "11",
keywords = "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",
URL = "http://www.sciencedirect.com/science/article/B6V2H-3W1GPR8-4/1/047d9c74e28a6a1a117a3ed9a6d6c409",
abstract = "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.",
}
@InProceedings{gray:1996:GPcbtNMR,
author = "H. F. Gray and R. J. Maxwell and I. Martinez-Perez and
C. Arus and S. Cerdan",
title = "Genetic Programming for Classification of Brain
Tumours from Nuclear Magnetic Resonance Biopsy
Spectra",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "424",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
size = "1 page",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96",
}
@InProceedings{Gray:1997:GPmcMRS,
author = "H. F. Gray and R. J. Maxwell",
title = "Genetic Programming for Multi-class Classification of
Magnetic Resonance Spectroscopy Data",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "137",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/Gray_1997_GPmcMRS.pdf",
size = "1 page",
notes = "GP-97",
}
@InProceedings{gray:1997:GPcmd,
author = "Helen Gray",
title = "Genetic Programming for Classification of Medical
Data",
booktitle = "Late Breaking Papers at the 1997 Genetic Programming
Conference",
year = "1997",
editor = "John R. Koza",
pages = "291",
address = "Stanford University, CA, USA",
publisher_address = "Stanford University, Stanford, California,
94305-3079, USA",
month = "13--16 " # jul,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-206995-8",
notes = "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",
}
@Article{gray:1998:GPcfs:aNMRshbtb,
author = "Helen F. Gray and Ross J. Maxwell and Irene
Martinez-Perez and Carles Arus and Sebastian Cerdan",
title = "Genetic programming for classification and feature
selection: analysis of {1H} nuclear magnetic resonance
spectra from human brain tumour biopsies",
journal = "NMR Biomedicine",
year = "1998",
volume = "11",
number = "4-5",
pages = "217--224",
month = jun # "-" # aug,
keywords = "genetic algorithms, genetic programming, brain tumour,
artificial intelligence, classification, feature
selection",
doi = "doi:10.1002/(SICI)1099-1492(199806/08)11:4/5<217::AID-NBM512>3.0.CO;2-4",
abstract = "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.",
notes = "PMID: 9719576, UI: 98384081 Computer Science
Department, Arhus University, Denmark.",
}
@InProceedings{Gray:2000:GPO,
author = "Helen Frances Gray and Ross James Maxwell",
title = "Genetic Programming Optimisation of Nuclear Magnetic
Resonance Pulse Shapes",
booktitle = "Medical Data Analysis: First International Symposium,
ISMDA 2000, Proceedings",
year = "2000",
editor = "R. W. Brause and E. Hanisch",
volume = "1933",
series = "Lecture Notes in Computer Science",
pages = "242--??",
address = "Frankfurt, Germany",
publisher_address = "Heidelberg",
month = sep,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
URL = "http://link.springer-ny.com/link/service/series/0558/bibs/1933/19330242.htm",
URL = "http://link.springer-ny.com/link/service/series/0558/papers/1933/19330242.pdf",
CODEN = "LNCSD9",
ISSN = "0302-9743",
bibdate = "Tue Sep 10 19:08:54 MDT 2002",
acknowledgement = ack-nhfb,
abstract = "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.",
}
@InProceedings{Greeff:1997:eemmps,
author = "D. J. Greeff and C. Aldrich",
title = "Evolution of Empirical Models for Metallurgical
Process Systems",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "138",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/Greeff_1997_eemmps.pdf",
size = "1 page",
notes = "GP-97",
}
@Article{Greeff:1998:CCE,
author = "D. J Greeff and C. Aldrich",
title = "Empirical modelling of chemical process systems with
evolutionary programming",
journal = "Computers \& Chemical Engineering",
year = "1998",
volume = "22",
pages = "995--1005",
number = "7-8",
abstract = "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.",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6TFT-3TKV02R-F/2/30657596f48ca16571ac48098a948833",
keywords = "genetic algorithms, genetic programming, empirical
modelling",
doi = "doi:10.1016/S0098-1354(97)00271-8",
}
@InProceedings{greene:1998:dasdd,
author = "Buster Greene",
title = "A Deterministic Analysis of Stationary
Diploid/Dominance",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "770--776",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "evolutionary programming",
ISBN = "1-55860-548-7",
notes = "GP-98",
}
@InProceedings{Greene:2008:gecco,
author = "Casey S. Greene and Bill C. White and Jason H. Moore",
title = "Using expert knowledge in initialization for
genome-wide analysis of epistasis using genetic
programming",
booktitle = "GECCO '08: Proceedings of the 10th annual conference
on Genetic and evolutionary computation",
year = "2008",
editor = "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",
isbn13 = "978-1-60558-130-9",
pages = "351--352",
address = "Atlanta, GA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p351.pdf",
doi = "doi:10.1145/1389095.1389158",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "12-16 " # jul,
abstract = "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.",
keywords = "genetic algorithms, genetic programming, expert
knowledge, genetic analysis, Initialisation,
Bioinformatics, computational biology: Poster, TuRF,
Relief, SNP, MDR, SDA",
notes = "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
\cite{1389158}
Comparison of three ways of loading problem inputs
(10000+) into initial population to predict clinical
end point (death). Artificial datasets.",
}
@Article{Greene:2008:sigevo,
author = "Casey S. Greene and Jason H. Moore",
title = "Human Genetics Using {GP}",
journal = "SIGEVOlution",
year = "2008",
volume = "3",
number = "2",
month = "Summer",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.sigevolution.org/issues/pdf/SIGEVOlution200802.pdf",
}
@InProceedings{Greene:2009:cec,
author = "Casey S. Greene and Jeff Kiralis and Jason H. Moore",
title = "Nature-Inspired Algorithms for the Genetic Analysis of
Epistasis in Common Human Diseases: Theoretical
Assessment of Wrapper vs. Filter Approaches",
booktitle = "2009 IEEE Congress on Evolutionary Computation",
year = "2009",
editor = "Andy Tyrrell",
pages = "800--807",
address = "Trondheim, Norway",
month = "18-21 " # may,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-2959-2",
file = "P153.pdf",
doi = "doi:10.1109/CEC.2009.4983027",
abstract = "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.",
keywords = "genetic algorithms, genetic programming",
notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and
the IET. IEEE Catalog Number: CFP09ICE-CDR",
}
@InProceedings{Greene:2009:cec2,
author = "Casey S. Greene and Bill C. White and Jason H. Moore",
title = "Sensible Initialization Using Expert Knowledge for
Genome-Wide Analysis of Epistasis Using Genetic
Programming",
booktitle = "2009 IEEE Congress on Evolutionary Computation",
year = "2009",
editor = "Andy Tyrrell",
pages = "1289--1296",
address = "Trondheim, Norway",
month = "18-21 " # may,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-2959-2",
file = "P152.pdf",
doi = "doi:10.1109/CEC.2009.4983093",
abstract = "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.",
keywords = "genetic algorithms, genetic programming",
notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and
the IET. IEEE Catalog Number: CFP09ICE-CDR",
}
@PhdThesis{greene:thesis,
author = "Francis Manwell Greene",
title = "Genetic Synthesis of Signal Processing Networks
Utilizing Diploid/Dominance",
school = "Department of Electrical Engineering. University of
Washington",
year = "1997",
address = "Seattle, USA",
month = "6 " # mar,
keywords = "genetic algorithms, genetic programming",
URL = "https://digital.lib.washington.edu/dspace/handle/1773/4915/browse?rpp=20&etal=-1&type=title&starts_with=G&order=ASC&sort_by=1",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/fgPhdDissertation.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/fgDissertation.pdf",
size = "183 pages",
abstract = "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.",
notes = "Supervisior Dr. Alistair Holden. fgDissertation.pdf is
Dissertation Proposal (July 29, 2001)",
}
@InProceedings{Greene:2000:GECCO,
author = "William A. Greene",
title = "A Non-Linear Schema Theorem for Genetic Algorithms",
pages = "189--194",
year = "2000",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2000)",
editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz
and Lee Spector and Ian Parmee and Hans-Georg Beyer",
address = "Las Vegas, Nevada, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "10-12 " # jul,
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-708-0",
URL = "http://www.cs.uno.edu/People/Faculty/bill/NonLinSchemaTheorem-GECCO-2000.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/GA068.pdf",
notes = "A joint meeting of the ninth International Conference
on Genetic Algorithms (ICGA-2000) and the fifth Annual
Genetic Programming Conference (GP-2000) Part of
\cite{whitley:2000:GECCO}",
}
@InProceedings{greene:2001:NBAGA,
author = "William A. Greene",
title = "Non-Linear Bit Arrangements in Genetic Algorithms",
booktitle = "2001 Genetic and Evolutionary Computation Conference
Late Breaking Papers",
year = "2001",
editor = "Erik D. Goodman",
pages = "138--144",
address = "San Francisco, California, USA",
month = "9-11 " # jul,
keywords = "genetic algorithms, poster",
URL = "http://www.cs.uno.edu/People/Faculty/bill/NonLinBits-GECCO-2001-lateBreakPaper.pdf",
abstract = "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.",
notes = "GECCO-2001LB. Two dimensional grid chromosome, three_D
cubes, complete binary tree. Follows up
\cite{Greene: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 \cite{greene:2001:GECCO}.",
}
@InProceedings{greene:sdi:gecco2004,
author = "William A. Greene",
title = "Schema Disruption in Chromosomes That Are Structured
as Binary Trees",
booktitle = "Genetic and Evolutionary Computation -- GECCO-2004,
Part I",
year = "2004",
editor = "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",
series = "Lecture Notes in Computer Science",
pages = "1197--1207",
address = "Seattle, WA, USA",
publisher_address = "Heidelberg",
month = "26-30 " # jun,
organisation = "ISGEC",
publisher = "Springer-Verlag",
volume = "3102",
ISBN = "3-540-22344-4",
ISSN = "0302-9743",
URL = "http://www.cs.uno.edu/People/Faculty/bill/Schema-disruption-binary-trees-GECCO-2004.pdf",
URL = "http://link.springer.de/link/service/series/0558/bibs/3102/31021197.htm",
size = "11",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "GECCO-2004 A joint meeting of the thirteenth
international conference on genetic algorithms
(ICGA-2004) and the ninth annual genetic programming
conference (GP-2004)",
}
@InProceedings{conf/prib/GreeneWM07,
author = "Casey S. Greene and Bill C. White and Jason H. Moore",
title = "An Expert Knowledge-Guided Mutation Operator for
Genome-Wide Genetic Analysis Using Genetic
Programming",
booktitle = "Proceedings of the second IAPR International Workshop
Pattern Recognition in Bioinformatics, PRIB 2007",
year = "2007",
editor = "Jagath C. Rajapakse and Bertil Schmidt and L. Gwenn
Volkert",
volume = "4774",
series = "Lecture Notes in Computer Science",
pages = "30--40",
address = "Singapore",
month = oct # " 1-2",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, TuRF",
isbn13 = "978-3-540-75285-1",
doi = "doi:10.1007/978-3-540-75286-8_4",
size = "11 page",
abstract = "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.",
bibdate = "2007-09-20",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/prib/prib2007.html#GreeneWM07",
}
@InCollection{Greene:2009:GPTP,
author = "Casey S. Greene and Douglas P. Hill and Jason H.
Moore",
title = "Environmental Sensing of Expert Knowledge in a
Computational Evolution System for Complex Problem
Solving in Human Genetics",
booktitle = "Genetic Programming Theory and Practice {VII}",
year = "2009",
editor = "Rick L. Riolo and Una-May O'Reilly and Trent
McConaghy",
series = "Genetic and Evolutionary Computation",
address = "Ann Arbor",
month = "14-16 " # may,
publisher = "Springer",
chapter = "2",
pages = "19--36",
keywords = "genetic algorithms, genetic programming, Genetic
Epidemiology, Symbolic Discriminant Analysis,
Epistasis",
notes = "part of \cite{Riolo:2009:GPTP}",
}
@InCollection{greenfield:2000:ECAPHE,
author = "Aaron Greenfield",
title = "Evolution of Communication Among Prey in a Hostile
Environment",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2000",
year = "2000",
editor = "John R. Koza",
pages = "170--179",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "part of \cite{koza:2000:gagp}",
}
@InProceedings{greenwold:2000:AGG,
author = "Simon M. Greenwold",
title = "{AGENCY} {GP}: Genetic programming for architectural
design",
booktitle = "Graduate Student Workshop",
year = "2000",
editor = "Conor Ryan and Una-May O'Reilly and William B.
Langdon",
pages = "273--276",
address = "Las Vegas, Nevada, USA",
month = "8 " # jul,
keywords = "genetic algorithms, genetic programming",
notes = "GECCO-2000WKS Part of \cite{wu:2000:GECCOWKS}",
}
@InProceedings{Greenwood:1997:chaosES,
author = "Garrison W. Greenwood",
title = "Experimental Observation of Chaos in Evolution
Strategies",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "evolutionary programming and evolution strategies",
pages = "439--444",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
notes = "GP-97",
}
@Article{greenwood:2001:bicm,
author = "Garrison W. Greenwood",
title = "Book Review: {Bio-Inspired} Computing Machines:
Towards Novel Computational Architectures",
journal = "Genetic Programming and Evolvable Machines",
year = "2001",
volume = "2",
number = "1",
pages = "75--78",
month = mar,
keywords = "genetic algorithms, genetic programming, evolutionary
programming, evolution strategies, evolvable hardware,
FPGA, L-Systems",
ISSN = "1389-2576",
doi = "doi:10.1023/A:1010022700219",
notes = "review of \cite{mange:1998:bicm} Article ID: 319814",
}
@Unpublished{grefenstette:1997:vivposn,
author = "John Grefenstette and Kenneth {De Jong} and Connie
Ramsey and Annie Wu",
title = "The Virtual Virus Project",
note = "Position paper at the Workshop on Evolutionary
Computation with Variable Size Representation at
ICGA-97",
month = "20 " # jul,
year = "1997",
address = "East Lansing, MI, USA",
keywords = "genetic algorithms, variable size representation",
size = "1 page",
}
@InProceedings{gregory:1998:GAoddq,
author = "Michael Gregory",
title = "Genetic Algorithm Optimisation of Distributed Database
Queries",
booktitle = "Proceedings of the 1998 IEEE World Congress on
Computational Intelligence",
year = "1998",
pages = "271--276",
address = "Anchorage, Alaska, USA",
month = "5-9 " # may,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-4869-9",
file = "c047.pdf",
size = "6 pages",
abstract = "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.",
notes = "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
World Congress on Computational Intelligence",
}
@InProceedings{Griffioen:2008:cec,
author = "A. R. Griffioen and S. K. Smit and A. E. Eiben",
title = "Learning Benefits Evolution if Sex Gives Pleasure",
booktitle = "2008 IEEE World Congress on Computational
Intelligence",
year = "2008",
editor = "Jun Wang",
address = "Hong Kong",
month = "1-6 " # jun,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-1823-7",
file = "EC0492.pdf",
URL = "http://www.cs.vu.nl/~gusz/papers/2008-CEC-Griffioen-Smit-Eiben.pdf",
abstract = "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.",
keywords = "genetic algorithms, genetic programming",
notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
EPS and the IET.",
}
@InProceedings{grimbleby:1995:,
author = "J. B. Grimbleby",
title = "An automatic Analogue Network Synthesis using Genetic
Algorithms",
booktitle = "First International Conference on Genetic Algorithms
in Engineering Systems: Innovations and Applications,
GALESIA",
year = "1995",
editor = "A. M. S. Zalzala",
volume = "414",
pages = "53--58",
address = "Sheffield, UK",
publisher_address = "London, UK",
month = "12-14 " # sep,
publisher = "IEE",
keywords = "genetic algorithms, genetic programming, analogue
network synthesis, frequency-domain, linear networks,
time-domain, analogue circuits, circuit CAD, circuit
optimisation, linear network synthesis",
ISBN = "0-85296-650-4",
doi = "doi:10.1049/cp:19951024",
size = "6 pages",
abstract = "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",
notes = "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]",
}
@InProceedings{grimes:1995:gtprtm,
author = "C. A. Grimes",
title = "Application of Genetic Techniques to the Planning of
Railway Track Maintenance Work",
booktitle = "First International Conference on Genetic Algorithms
in Engineering Systems: Innovations and Applications,
GALESIA",
year = "1995",
editor = "A. M. S. Zalzala",
volume = "414",
pages = "467--472",
address = "Sheffield, UK",
publisher_address = "London, UK",
month = "12-14 " # sep,
publisher = "IEE",
keywords = "genetic algorithms, genetic programming, scheduling,
maintenance, PC-MARPAS",
ISBN = "0-85296-650-4",
URL = "http://scitation.aip.org/getpdf/servlet/GetPDFServlet?filetype=pdf&id=IEECPS0019950CP414000467000001&idtype=cvips&prog=normal",
doi = "doi:10.1049/cp:19951093",
abstract = "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).",
notes = "12--14 September 1995, Halifax Hall, University of
Sheffield, UK see also
http://www.iee.org.uk/LSboard/Conf/program/galprog.htm
",
}
@PhdThesis{oai:ufu.br:295,
title = "Regress{\~a}o simb{\'o}lica via programa{\c c}{\~a}o
gen{\'e}tica: um estudo de caso com modelagem
geof{\'i}sica",
author = "Alexandre Grings",
year = "2006",
type = "Tese ou Dissertacao Eletronica",
school = "Biblioteca Digital da Universidade Federal de
Uberl{\^a}ndia",
address = "Brazil",
month = "24 " # feb,
bibsource = "OAI-PMH server at oai.ibict.br",
contributor = "Ant{\^o}nio Eduardo Costa Pereira and Joao Bosco da
Mota Alves and M{\'a}rcia Aparecida Fernandes",
format = "PDF",
language = "PT",
oai = "oai:ufu.br:295",
rights = "Liberar o conte{\'u}do dos arquivos para acesso
p{\'u}blico",
keywords = "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",
URL = "http://www.bdtd.ufu.br//tde_busca/arquivo.php?codArquivo=550.pdf",
size = "133 pages",
abstract = "A regress{\~a}o simb{\'o}lica, que consiste na
manipula{\c c}{\~a}o de express{\~o}es matem{\'a}ticas
para descobertade fun{\c c}{\~o}es que descrevam um
conjunto de dados, foi uma tarefa exclusivamente
humanaat{\'e} pouco tempo atr{\'a}s. Recentemente,
foram desenvolvidas v{\'a}rias t{\'e}cnicas
computacionais paraautomatizar a regress{\~a}o
simb{\'o}lica. Uma dessas t{\'e}cnicas {\'e} a
programa{\c c}{\~a}o gen{\'e}tica, uma sub{\'a}reada
computa{\c c}{\~a}o evolutiva que usa analogia {\`a}
teoria da evolu{\c c}{\~a}o de Darwin e id{\'e}ias do
campoda Gen{\'e}tica para desenvolver um grupo de
programas de computador na busca por solu{\c c}{\~o}es
atarefas computacionais. O presente trabalho visa a
testar as capacidades de regress{\~a}o simb{\'o}licada
programa{\c c}{\~a}o gen{\'e}tica com objetivo de
verificar sua viabilidade como ferramenta paraa
pesquisa de um problema geof{\'i}sico. Esse problema
diz respeito a fen{\^o}menos que ocorremna ionosfera, a
regi{\~a}o da atmosfera ionizada pela a{\c c}{\~a}o dos
raios solares, que desempenham umpapel fundamental para
as telecomunica{\c c}{\~o}es. No intercurso dessa
tentativa, faz-se o uso deduas implementa{\c c}{\~o}es
tradicionais de programa{\c c}{\~a}o gen{\'e}tica e de
uma variante, chamada programa{\c c}{\~a}oda
express{\~a}o g{\^e}nica. Problemas como o sistema
estudado demandam muito tempode processamento e
mem{\'o}ria, desse modo, o trabalho culmina com uma
implementa{\c c}{\~a}o distribu{\'i}dade programa{\c
c}{\~a}o gen{\'e}tica 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.",
notes = "in Portuguese",
}
@Article{gritz:1995:GPafm,
author = "L. Gritz and J. K. Hahn",
title = "Genetic Programming for Articulated Figure Motion",
journal = "Journal of Visualization and Computer Animation",
year = "1995",
volume = "6",
number = "3",
pages = "129--142",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.icg.seas.gwu.edu/Publications/gpafm.ps",
abstract = "
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.",
notes = "
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/
",
}
@InProceedings{Gritz:1997:GPec3da,
author = "Larry Gritz and James K. Hahn",
title = "Genetic Programming Evolution of Controllers for 3-{D}
Character Animation",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "139--146",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
URL = "http://www.icg.seas.gwu.edu/Publications/gpec-gp97.ps",
size = "8 pages",
abstract = "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.",
notes = "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",
}
@PhdThesis{gritz:dissertation,
author = "Larry Israel Gritz",
title = "Evolutionary Controller Synthesis for 3-{D} Character
Animation",
school = "The George Washington University",
year = "1999",
address = "Washington, DC, USA",
month = "15 " # may,
keywords = "genetic algorithms, genetic programming, computer
animation",
URL = "http://www.icg.seas.gwu.edu/Publications/gritzdissert.ps.gz",
URL = "http://www.seas.gwu.edu/~graphics/papers/gritzdissert.html",
size = "113 pages",
abstract = "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.",
}
@InProceedings{DBLP:conf/mfcs/Gronemeier04,
author = "Andre Gronemeier",
title = "Approximating {Boolean} Functions by {OBDD}s",
booktitle = "29th Symposium on Mathematical Foundations of Computer
Science MFCS 2004",
year = "2004",
editor = "Jir\'{\i} Fiala and V{\'a}clav Koubek and Jan
Kratochv\'{\i}l",
series = "Lecture Notes in Computer Science",
volume = "3153",
pages = "251--262",
address = "Prague, Czech Republic",
month = aug # " 22-27",
publisher = "Springer",
bibsource = "DBLP, http://dblp.uni-trier.de",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-22823-3",
URL = "http://ls2-www.cs.uni-dortmund.de/~gronemeier/publications/obdd-approx-mfcs.pdf",
URL = "http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3153&spage=251",
size = "16 pages",
abstract = "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.",
notes = "replaced by \cite{Gronemeier:2007:DAM}",
}
@Article{Gronemeier:2007:DAM,
author = "Andre Gronemeier",
title = "Approximating {Boolean} functions by {OBDD}",
journal = "Discrete Applied Mathematics",
year = "2007",
volume = "155",
number = "2",
pages = "194--209",
month = "15 " # jan,
note = "29th Symposium on Mathematical Foundations of Computer
Science MFCS 2004",
keywords = "genetic algorithms, genetic programming, OBDD,
Communication complexity, Approximation",
doi = "doi:10.1016/j.dam.2006.04.037",
abstract = "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.",
notes = "replaces \cite{DBLP:conf/mfcs/Gronemeier04}",
}
@InProceedings{gronroos:1999:ACSMENN,
author = "Marko Gronroos",
title = "A Comparison of Some Methods for Evolving Neural
Networks",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1442",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "artificial life, adaptive behavior and agents, poster
papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-006.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-006.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InCollection{gros:2003:GENN,
author = "Charles-Henri Gros",
title = "Genetic Evolution of Neural Networks",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2003",
year = "2003",
editor = "John R. Koza",
pages = "68--74",
address = "Stanford, California, 94305-3079 USA",
month = "4 " # dec,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.genetic-programming.org/sp2003/Gros.pdf",
notes = "part of \cite{koza:2003:gagp}",
}
@InProceedings{conf/iwinac/GrosanAH05,
title = "{MEPIDS}: Multi-Expression Programming for Intrusion
Detection System",
author = "Crina Grosan and Ajith Abraham and Sang-Yong Han",
year = "2005",
booktitle = "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",
editor = "Jose Mira and Jose R. Alvarez",
volume = "3562",
series = "Lecture Notes in Computer Science",
pages = "163--172",
address = "Las Palmas, Canary Islands, Spain",
publisher_address = "Berlin / Heidelberg",
month = jun # " 15-18",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
bibdate = "2005-06-15",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/iwinac/iwinac2005-2.html#GrosanAH05",
ISBN = "3-540-26319-5",
ISSN = "0302-9743",
URL = "http://www.cs.ubbcluj.ro/~cgrosan/iwinac05.pdf",
doi = "doi:10.1007/11499305_17",
size = "10 pages",
abstract = "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.",
}
@InProceedings{grosan-stock,
author = "Crina Grosan and Ajith Abraham and Vitorino Ramos and
Sang Yong Han",
title = "Stock Market Prediction Using Multi Expression
Programming",
booktitle = "ALEA-05, Workshop on Artificial Life and Evolutionary
Algorithms at EPIA'05 - Proc. of the 12th Portuguese
Conference on Artificial Intelligence",
year = "2005",
editor = "C. Bento and A. Cardoso and G. Dias",
pages = "73--78",
address = "Covilha, Portugal",
keywords = "genetic algorithms, genetic programming, Stock Market
Prediction, Multi Expression Programming, Nasdaq-100,
CNX NIFTY stock index",
URL = "http://www.cs.ubbcluj.ro/~cgrosan/alea.pdf",
URL = "http://alfa.ist.utl.pt/~cvrm/staff/vramos/Vramos-EPIA05.pdf",
size = "6 pages",
abstract = "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.",
notes = "
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.",
}
@InProceedings{grosan:2005:HIS,
author = "C. Grosan and A. Abraham",
title = "Ensemble of genetic programming models for designing
reactive power controllers",
booktitle = "Fifth International Conference on Hybrid Intelligent
Systems, HIS-05",
year = "2005",
month = "6-9 " # nov,
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1109/ICHIS.2005.36",
abstract = "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.",
}
@InCollection{grosan:2006:GSP,
author = "Crina Grosan and Ajith Abraham",
title = "Stock Market Modeling Using Genetic Programming
Ensembles",
year = "2006",
booktitle = "Genetic Systems Programming: Theory and Experiences",
pages = "133--148",
volume = "13",
series = "Studies in Computational Intelligence",
editor = "Nadia Nedjah and Ajith Abraham and Luiza {de Macedo
Mourelle}",
publisher = "Springer",
address = "Germany",
note = "Forthcoming",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-29849-5",
URL = "http://www.cs.ubbcluj.ro/~cgrosan/stock-chapter.pdf",
abstract = "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.",
notes = "http://www.springer.com/sgw/cda/frontpage/0,11855,5-146-22-92733168-0,00.html",
size = "17 pages",
}
@InProceedings{Grosman2001663,
author = "Benjamin Grosman and Daniel R. Lewin",
title = "{MPC} using nonlinear models generated by genetic
programming",
editor = "Rafiqul Gani and Sten Bay Jorgensen",
booktitle = "European Symposium on Computer Aided Process
Engineering - 11, 34th European Symposium of the
Working Party on Computer Aided Process Engineering",
publisher = "Elsevier",
year = "2001",
volume = "9",
pages = "663--668",
series = "Computer Aided Chemical Engineering",
address = "Kolding, Denmark",
month = may # " 27-30",
keywords = "genetic algorithms, genetic programming",
isbn13 = "0-444-5070904",
ISSN = "1570-7946",
doi = "doi:10.1016/S1570-7946(01)80105-X",
URL = "http://www.sciencedirect.com/science/article/B8G5G-4P40D5J-3R/2/96212e409c54e5c4c1781f7f1780816e",
notes = "ESCAPE-11",
}
@Article{Grosman:2002:CCE,
author = "Benyamin Grosman and Daniel R. Lewin",
title = "Automated nonlinear model predictive control using
genetic programming",
journal = "Computers \& Chemical Engineering",
year = "2002",
volume = "26",
pages = "631--640",
number = "4-5",
owner = "wlangdon",
keywords = "genetic algorithms, genetic programming, Empirical
process modeling, Nonlinear model predictive control",
ISSN = "0098-1354",
URL = "http://www.sciencedirect.com/science/article/B6TFT-44YWM6B-B/2/b0dbb5bfa3d6c3d92f1904e01e559d3f",
doi = "doi:10.1016/S0098-1354(01)00780-3",
abstract = "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.",
}
@Article{Grosman:2004:CCE,
author = "Benyamin Grosman and Daniel R. Lewin",
title = "Adaptive genetic programming for steady-state process
modeling",
journal = "Computers \& Chemical Engineering",
year = "2004",
volume = "28",
pages = "2779--2790",
number = "12",
abstract = "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.",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6TFT-4DMW22F-1/2/3e0d065d49ca47901dac832951154da0",
month = "15 " # nov,
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1016/j.compchemeng.2004.09.001",
}
@Article{Grosman:2005:tSM,
title = "Yield enhancement in photolithography through
model-based process control: average mode control",
author = "Benyamin Grosman and Sivan Lachman-Shalem and Raaya
Swissa and D. R. Lewin",
journal = "IEEE Transactions on Semiconductor Manufacturing",
year = "2005",
volume = "18",
number = "1",
pages = "86--93",
month = feb,
keywords = "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",
doi = "doi:10.1109/TSM.2004.836654",
ISSN = "0894-6507",
abstract = "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.",
}
@InProceedings{Grosman:2006:iscacsd,
author = "B. Grosman and D. R. Lewin",
title = "Lyapunov-based Stability Analysis Automated by Genetic
Programming",
booktitle = "IEEE International Symposium on Computer-Aided Control
Systems Design, 2006",
year = "2006",
pages = "766--771",
address = "Munich, Germany",
month = "4-6 " # oct,
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-9797-5",
doi = "doi:10.1109/CACSD.2006.285474",
abstract = "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",
notes = "Dept. of Chem. Eng., Technion-Israel Inst. of
Technol., Haifa",
}
@PhdThesis{Grosman:thesis,
author = "Benyamin Grosman",
title = "Stability Analysis of Nonlinear Control Systems Using
Genetic Programming",
school = "Department of Chemical Engineering, Technion",
year = "2008",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.graduate.technion.ac.il/Theses/Abstracts.asp?Id=24203",
size = "pages",
abstract = "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.",
notes = "Supervisor: Prof. Lewin Daniel",
}
@Article{Grosman2009252,
author = "Benyamin Grosman and Daniel R. Lewin",
title = "Lyapunov-based stability analysis automated by genetic
programming",
journal = "Automatica",
volume = "45",
number = "1",
pages = "252--256",
year = "2009",
ISSN = "0005-1098",
doi = "DOI:10.1016/j.automatica.2008.07.014",
URL = "http://www.sciencedirect.com/science/article/B6V21-4V402MR-3/2/500948c7466e5824a72a3930c046e8aa",
keywords = "genetic algorithms, genetic programming, Lyapunov
stability",
abstract = "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.",
}
@InProceedings{gross:2002:gecco,
author = "R. Gross and K. Albrecht and W. Kantschik and W.
Banzhaf",
title = "Evolving Chess Playing Programs",
booktitle = "GECCO 2002: Proceedings of the Genetic and
Evolutionary Computation Conference",
editor = "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",
year = "2002",
pages = "740--747",
address = "New York",
publisher_address = "San Francisco, CA 94104, USA",
month = "9-13 " # jul,
publisher = "Morgan Kaufmann Publishers",
keywords = "genetic algorithms, genetic programming, chess,
distributed computing, evolution strategies",
ISBN = "1-55860-878-8",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/GP121.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/GP121.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf",
notes = "GECCO-2002. A joint meeting of the eleventh
International Conference on Genetic Algorithms
(ICGA-2002) and the seventh Annual Genetic Programming
Conference (GP-2002)",
}
@TechReport{Gruau:1992:cegNN,
author = "F. Gruau",
title = "Cellular encoding of Genetic Neural Networks",
institution = "Laboratoire de l'Informatique du Parallilisme. Ecole
Normale Supirieure de Lyon",
year = "1992",
type = "Technical report",
number = "92-21",
address = "France",
keywords = "genetic algorithms, genetic programming",
broken = "ftp://lip.ens-lyon.fr/pub/Rapports/RR/RR92/RR92-21.ps.Z",
}
@InProceedings{Gruau92,
author = "Frederic Gruau",
title = "Genetic Synthesis of {Boolean} Neural Networks with a
Cell Rewriting Developmental Process",
booktitle = "Proceedings of the Workshop on Combinations of Genetic
Algorithms and Neural Networks (COGANN92)",
editor = "J. D. Schaffer and D. Whitley",
publisher = "The IEEE Computer Society Press",
pages = "55--74",
year = "1992",
keywords = "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",
doi = "doi:10.1109/COGANN.1992.273948",
abstract = "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",
}
@Article{Gruau93,
author = "Frederic Gruau",
editor = "Simon Lucas",
title = "Cellular encoding as a graph grammar",
journal = "IEE Colloquium on Grammatical Inference: Theory,
Applications and Alternatives",
volume = "(Digest No.092)",
pages = "17/1--10",
publisher = "IEE",
address = "London",
month = "22-23 " # apr,
year = "1993",
keywords = "genetic algorithm connectionism neural networks
cogann",
abstract = "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.",
}
@InProceedings{icga93:gruau,
author = "Frederic Gruau",
title = "Genetic Synthesis of Modular Neural Networks",
year = "1993",
booktitle = "Proceedings of the 5th International Conference on
Genetic Algorithms, ICGA-93",
editor = "Stephanie Forrest",
publisher = "Morgan Kaufmann",
pages = "318--325",
month = "17-21 " # jul,
address = "University of Illinois at Urbana-Champaign",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/icga93_gruau.pdf",
size = "8 pages",
notes = "
",
}
@PhdThesis{Gruau:1994:thesis,
author = "F. Gruau",
title = "Neural Network Synthesis using Cellular Encoding and
the Genetic Algorithm.",
school = "Laboratoire de l'Informatique du Parallilisme, Ecole
Normale Supirieure de Lyon",
year = "1994",
address = "France",
keywords = "genetic algorithms, genetic programming",
URL = "ftp://ftp.ens-lyon.fr/pub/LIP/Rapports/PhD/PhD1994/PhD1994-01-E.ps.Z",
URL = "ftp://ftp.ens-lyon.fr/pub/LIP/Rapports/PhD/PhD1994/PhD1994-01-F.ps.Z",
size = "151 pages",
abstract = "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.",
notes = "
",
}
@InCollection{kinnear:gruau,
title = "Genetic micro programming of Neural Networks",
author = "Frederic Gruau",
booktitle = "Advances in Genetic Programming",
publisher = "MIT Press",
editor = "Kenneth E. {Kinnear, Jr.}",
year = "1994",
pages = "495--518",
keywords = "genetic algorithms, genetic programming",
chapter = "24",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262111888",
size = "25 pages",
}
@TechReport{Gruau:1993:ceNNile,
author = "F. Gruau and D. Whitley",
title = "The cellular development of neural networks: The
interaction of learning and evolution",
institution = "Laboratoire de l'Informatique du Parallilisme, Ecole
Normale Supirieure de Lyon",
year = "1993",
type = "Technical report",
number = "93-04",
address = "France",
keywords = "genetic algorithms, genetic programming",
broken = "ftp://lip.ens-lyon.fr/pub/Rapports/RR/RR93/RR93-04.ps.Z",
}
@Article{Gruau:1993:alcdp,
author = "F. Gruau and D. Whitley",
title = "Adding learning to the cellular development process: a
comparative study",
journal = "Evolutionary Computation",
year = "1993",
volume = "1",
number = "3",
pages = "213--233",
doi = "doi:10.1162/evco.1993.1.3.213",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
}
@InProceedings{gruau:1995:plad,
author = "Frederic Gruau and Darrell Whitley",
title = "A Programming Language for Artificial Development",
booktitle = "Evolutionary Programming {IV} Proceedings of the
Fourth Annual Conference on Evolutionary Programming",
year = "1995",
editor = "John Robert McDonnell and Robert G. Reynolds and David
B. Fogel",
pages = "415--434",
address = "San Diego, CA, USA",
month = "1-3 " # mar,
publisher = "MIT Press",
keywords = "genetic algorithms, Neural Networks, parellel
architectures",
ISBN = "0-262-13317-2",
size = "20 pages",
notes = "EP-95, Extension of cellular encoding. Says can build
neural network that can emulate any functional language
(eg SISAL).",
}
@Article{gruau:1995:admnn,
author = "Frederic Gruau",
title = "Automatic Definition of Modular Neural Networks",
journal = "Adaptive Behaviour",
year = "1995",
volume = "3",
number = "2",
pages = "151--183",
keywords = "genetic algorithms, genetic programming, animats,
cellular encoding, modularity, locomotion, automatic
definition of neural subnetworks",
URL = "http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/654/http:zSzzSzwww.cwi.nlzSz~gruauzSzgruauzSzAB.pdf/gruau95automatic.pdf",
URL = "http://citeseer.ist.psu.edu/gruau95automatic.html",
abstract = "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.",
notes = "ANN for controlling six legged robot locomotion,
http://www.isab.org/journal/adap3_2.php",
}
@InCollection{gruau:1996:aigp2,
author = "Frederic Gruau",
title = "On Using Syntactic Constraints with Genetic
Programming",
booktitle = "Advances in Genetic Programming 2",
publisher = "MIT Press",
year = "1996",
editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}",
pages = "377--394",
chapter = "19",
address = "Cambridge, MA, USA",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-262-01158-1",
URL = "http://cisnet.mit.edu/Advances-in-Genetic-Programming/394",
}
@InProceedings{gruau:1996:ceVdeGNN,
author = "Frederic Gruau and Darrell Whitley and Larry Pyeatt",
title = "A Comparison between Cellular Encoding and Direct
Encoding for Genetic Neural Networks",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "81--89",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
URL = "http://www.cs.colostate.edu/~genitor/1996/gp96.ps.gz",
size = "9 pages",
abstract = "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...",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "See also \cite{HeidrichMeisner2009152}. GP-96",
}
@TechReport{gruau:1996:ceier,
author = "Frederic Gruau and Kameel Quatramaran",
title = "Cellular Encoding for Interactive Evolutionary
Robotics",
institution = "School of Cognitive and Computing Sciences, University
of Sussex",
year = "1996",
type = "Cognitive Science Research Paper",
number = "425",
address = "Falmer, Brighton, Sussex, UK",
month = jul,
keywords = "genetic algorithms, genetic programming",
URL = "ftp://ftp.cogs.susx.ac.uk/pub/reports/csrp/csrp425.ps.Z",
URL = "http://www.cogs.susx.ac.uk/cgi-bin/htmlcogsreps?csrp425",
abstract = "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.",
size = "23 pages",
}
@InCollection{Gruau:EA95,
author = "Frederic Gruau",
title = "Modular Genetic Neural Networks for Six-Legged
Locomotion",
booktitle = "Artificial Evolution",
publisher = "Springer Verlag",
year = "1996",
editor = "Jean-Marc Alliot and Evelyne Lutton and Edmund Ronald
and Marc Schoenauer and Dominique Snyers",
volume = "1063",
series = "LNCS",
pages = "201--219",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-540-61108-0",
doi = "doi:10.1007/3-540-61108-8_39",
size = "19 pages",
abstract = "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.",
notes = "Selected papers from two conferences: Evolution
Artificielle 94 and Evolution Artificielle 95
http://www.cmap.polytechnique.fr/www.eark/ea95.html",
affiliation = "Stanford University Psychology Department 94305 Palo
Alto CA 94305 Palo Alto CA",
}
@InProceedings{blob_computing2004,
author = "Frederic Gruau and Yves Lhuillier and Philippe Reitz
and Olivier Temam",
title = "Blob Computing",
booktitle = "Computing Frontiers",
year = "2004",
pages = "125--139",
organisation = "ACM",
publisher = "SIGMicro",
keywords = "genetic algorithms, genetic programming",
URL = "http://blob.lri.fr/publication/2004-model-blob-machine.pdf",
abstract = "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.",
notes = "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",
}
@MastersThesis{IMM2005-03650,
author = "Soren Grubov and Rasmus Hartvig",
title = "{AI} in Computer games",
year = "2005",
school = "Informatics and Mathematical Modelling, Technical
University of Denmark, {DTU}",
address = "Richard Petersens Plads, Building 321, {DK-}2800 Kgs.
Lyngby",
note = "Supervisor: Thomas Bolander \& Hans Bruun",
URL = "http://www2.imm.dtu.dk/pubdb/p.php?3650",
abstract = "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.",
keywords = "genetic algorithms, genetic programming",
}
@Article{Grunwald2009195,
author = "S. Grunwald",
title = "Multi-criteria characterization of recent digital soil
mapping and modeling approaches",
journal = "Geoderma",
volume = "152",
number = "3-4",
pages = "195--207",
year = "2009",
ISSN = "0016-7061",
doi = "doi:10.1016/j.geoderma.2009.06.003",
URL = "http://www.sciencedirect.com/science/article/B6V67-4WSG2WJ-1/2/af92060815439203d2999e4ace2ae786",
keywords = "genetic algorithms, genetic programming, Digital soil
mapping, Digital soil modelling, Pedometrics,
Quantitative methods, Soils",
abstract = "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.",
notes = "survey",
}
@InProceedings{guerra-salcedo:1998:gsfss,
author = "Cesar Guerra-Salcedo and Darrell Whitley",
title = "Genetic Search for Feature Subset Selection: {A}
Comparison Between {CHC} and {GENESIS}",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "504--509",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms",
notes = "SGA-98",
}
@InProceedings{guerra-salcedo:1999:GAFSEC,
author = "Cesar Guerra-Salcedo and Darrell Whitley",
title = "Genetic Approach to Feature Selection for Ensemble
Creation",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "236--243",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems, data
mining",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/Guerra-Salcedo_gecco99c.pdf",
URL = "http://www.cs.colostate.edu/~genitor/1999/gecco99c.pdf",
abstract = "boosting and bagging",
notes = "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.
",
}
@InProceedings{guigue:1999:SALGA,
author = "Alexis Guigue and Sofiane Oussedik and Daniel
Delahaye",
title = "Sequencing Aircraft Landings by Genetic Algorithms",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "788",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems, poster
papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-880.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-880.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{ppdp10-synthesis,
author = "Sumit Gulwani",
title = "Dimensions in program synthesis",
booktitle = "Proceedings of the 12th international ACM SIGPLAN
symposium on Principles and practice of declarative
programming",
year = "2010",
pages = "13--24",
address = "Hagenberg, Austria",
month = oct,
publisher = "ACM",
note = "Invited talk",
keywords = "genetic algorithms, genetic programming, Deductive
Synthesis, Inductive Synthesis, Programming by
Examples, Programming by Demonstration, SAT Solving,
SMT Solving, Machine Learning, Probabilistic Inference,
Belief Propagation",
acmid = "1836091",
isbn13 = "978-1-4503-0132-9",
URL = "http://research.microsoft.com/en-us/um/people/sumitg/pubs/ppdp10-synthesis.pdf",
doi = "doi:10.1145/1836089.1836091",
size = "12 pages",
abstract = "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.",
notes = "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 \cite{5770924}",
}
@Article{journals/tsmc/GuoJN05,
title = "Feature generation using genetic programming with
application to fault classification",
author = "Hong Guo and Lindsay B. Jack and Asoke K. Nandi",
journal = "IEEE Transactions on Systems, Man, and Cybernetics,
Part B",
year = "2005",
number = "1",
volume = "35",
pages = "89--99",
month = feb,
bibdate = "2006-01-23",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/tsmc/tsmcb35.html#GuoJN05",
keywords = "genetic algorithms, genetic programming",
ISSN = "1083-4419",
doi = "doi:10.1109/TSMCB.2004.841426",
size = "11 pages",
abstract = "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.",
}
@Article{GN:PR:06,
title = "Breast cancer diagnosis using genetic programming
generated feature",
author = "Hong Guo and Asoke K. Nandi",
journal = "Pattern Recognition",
year = "2006",
volume = "39",
number = "5",
pages = "980--987",
month = may,
keywords = "genetic algorithms, genetic programming, Feature
extraction, Fisher discriminant analysis, Pattern
recognition",
doi = "doi:10.1016/j.patcog.2005.10.001",
size = "8 pages",
abstract = "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.",
}
@InProceedings{conf/biostec/GuoZN08,
title = "Breast Cancer Detection using Genetic Programming",
author = "Hong Guo and Qing Zhang and Asoke K. Nandi",
bibdate = "2008-04-10",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/biostec/biosignals2008-2.html#GuoZN08",
booktitle = "Proceedings of the First International Conference on
Biomedical Electronics and Devices, BIOSIGNALS 2008",
publisher = "INSTICC - Institute for Systems and Technologies of
Information, Control and Communication",
year = "2008",
editor = "Pedro Encarna{\c c}{\~a}o and Ant{\'o}nio Veloso",
isbn13 = "978-989-8111-18-0",
pages = "334--341",
volume = "2",
address = "Funchal, Madeira, Portugal",
month = jan # " 28-31",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "http://www.biosignals.biostec.org/Abstracts/2008/BIOSIGNALS_2008_Abstracts.htm",
}
@Article{Guo201110425,
author = "Ling Guo and Daniel Rivero and Julian Dorado and
Cristian R. Munteanu and Alejandro Pazos",
title = "Automatic feature extraction using genetic
programming: An application to epileptic {EEG}
classification",
journal = "Expert Systems with Applications",
volume = "38",
number = "8",
pages = "10425--10436",
year = "2011",
ISSN = "0957-4174",
doi = "doi:10.1016/j.eswa.2011.02.118",
URL = "http://www.sciencedirect.com/science/article/B6V03-5265S7J-6/2/7bccfdf0fc39adebbc6851a1c6c408a3",
keywords = "genetic algorithms, genetic programming, Feature
extraction, K-nearest neighbour classifier (KNN),
Discrete wavelet transform (DWT), Epilepsy, EEG
classification",
abstract = "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.",
}
@InProceedings{DBLP:conf/gecco/GuoB09,
author = "Pei Fang Guo and Prabir Bhattacharya",
title = "An evolutionary approach to feature function
generation in application to biomedical image
patterns",
booktitle = "GECCO '09: Proceedings of the 11th Annual conference
on Genetic and evolutionary computation",
year = "2009",
editor = "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",
pages = "1883--1884",
address = "Montreal",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "8-12 " # jul,
organisation = "SigEvo",
keywords = "genetic algorithms, genetic programming, Poster",
isbn13 = "978-1-60558-325-9",
bibsource = "DBLP, http://dblp.uni-trier.de",
doi = "doi:10.1145/1569901.1570216",
abstract = "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.",
notes = "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.",
}
@InProceedings{Guo:2009:SMC,
author = "Pei-Fang Guo and Prabir Bhattacharya and Nawwaf
Kharma",
title = "An efficient image pattern recognition system using an
evolutionary search strategy",
booktitle = "IEEE International Conference on Systems, Man and
Cybernetics, SMC 2009",
year = "2009",
month = oct,
pages = "599--604",
address = "San Antonio, Texas, USA",
publisher = "IEEE",
keywords = "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",
ISSN = "1062-922X",
doi = "doi:10.1109/ICSMC.2009.5346614",
size = "6 pages",
abstract = "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.",
notes = "Also known as \cite{5346614}",
}
@InProceedings{Guo:2010:CCECE,
author = "Pei-Fang Guo and Prabir Bhattacharya and Nawwaf
Kharma",
title = "Automated synthesis of feature functions for pattern
detection",
booktitle = "23rd Canadian Conference on Electrical and Computer
Engineering (CCECE), 2010",
year = "2010",
month = "2-5 " # may,
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/CCECE.2010.5575224",
ISSN = "0840-7789",
notes = "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 \cite{5575224}",
}
@InCollection{gupta:2000:CGGUGP,
author = "Binod Gupta",
title = "Context-Free Grammar Generation Using Genetic
Programming",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2000",
year = "2000",
editor = "John R. Koza",
pages = "180--187",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "part of \cite{koza:2000:gagp}",
}
@InProceedings{GuptaR08,
author = "Nirmal Kumar Gupta and Mukesh Kumar Rohil",
title = "Using Genetic Algorithm for Unit Testing of Object
Oriented Software",
booktitle = "Proceedings of the 1st International Conference on
Emerging Trends in Engineering and Technology (ICETET
'08)",
year = "2008",
pages = "308--313",
month = jul,
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming,
object-oriented methods, program testing, object
oriented software unit testing, test case generation",
bibsource = "http://www.sebase.org/sbse/publications/repository.html",
doi = "doi:10.1109/ICETET.2008.137",
size = "6 pages",
abstract = "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.",
notes = "Also known as \cite{4579916}
Java, HTMLparser",
}
@InCollection{gurganious:1999:ABWEUGA,
author = "Darryl Gurganious",
title = "Adaptive Beamformer Weight Estimation Using Genetic
Algorithms",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1999",
year = "1999",
editor = "John R. Koza",
pages = "49--57",
address = "Stanford, California, 94305-3079 USA",
month = "15 " # mar,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms",
notes = "part of \cite{koza:1999:GAGPs}",
}
@Article{Gusel:2005:MT,
author = "Leo Gusel and Miran Brezocnik",
title = "Genetic modeling of electrical conductivity of formed
material",
journal = "Materials and technology",
year = "2005",
volume = "39",
number = "4",
pages = "107--111",
email = "mbrezocnik@uni-mb.si",
keywords = "genetic algorithms, genetic programming, copper
alloys, electrical conductivity, cold forming,
modelling, genetsko programiranje, modeliranje, hladno
preoblikovanje, elektricna prevodnost, bakrove
zlitine",
ISSN = "1580-2949",
URL = "http://www.imt.si/materiali-tehnologije/",
URL = "http://ctklj.ctk.uni-lj.si/kovine/izvodi/mit054/gusel.pdf",
size = "5 pages",
abstract = "In the paper a genetic programming method for
efficient determination of accurate models for the
change of electrical conductivity of cold formed alloy
CuCrZr was presented. The main characteristic of
genetic programming method, which is one of
evolutionary methods for modelling, 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. 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 models results and regression models results
concerning the experimental results has showed that
genetic models are much more precise and more varied
then regression model. The variety of genetic models
allows us, concerning the demands, to decide for an
optimal genetic model for mathematical description and
prediction of change of electrical conductivity in the
frame of experimental environment.",
abstract = "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.",
}
@Article{Gusel:2006:CMS,
author = "Leo Gusel and Miran Brezocnik",
title = "Modeling of impact toughness of cold formed material
by genetic programming",
journal = "Computational Materials Science",
year = "2006",
volume = "37",
number = "4",
pages = "476--482",
month = oct,
email = "mbrezocnik@uni-mb.si",
keywords = "genetic algorithms, genetic programming, evolutionary
computing, metal forming, modelling, impact toughness,
copper alloy",
ISSN = "0927-0256",
doi = "doi:10.1016/j.commatsci.2005.11.007",
abstract = "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.",
}
@InProceedings{gustafson:2000:GAK,
author = "Steven M. Gustafson and William H. Hsu",
title = "Genetic programming for strategy learning in soccer
playing agents: {A} {KDD}-based architecture",
booktitle = "Graduate Student Workshop",
year = "2000",
editor = "Conor Ryan and Una-May O'Reilly and William B.
Langdon",
pages = "277--280",
address = "Las Vegas, Nevada, USA",
month = "8 " # jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.nott.ac.uk/~smg/research/publications/gecco-2000.ps",
URL = "http://www.cs.nott.ac.uk/~smg/research/publications/gecco-2000.pdf",
notes = "GECCO-2000WKS Part of \cite{wu:2000:GECCOWKS}",
}
@InProceedings{gustafson:2001:EuroGP,
author = "Steven M. Gustafson and William H. Hsu",
title = "Layered Learning in Genetic Programming for a
Co-operative Robot Soccer Problem",
booktitle = "Genetic Programming, Proceedings of EuroGP'2001",
year = "2001",
editor = "Julian F. Miller and Marco Tomassini and Pier Luca
Lanzi and Conor Ryan and Andrea G. B. Tettamanzi and
William B. Langdon",
volume = "2038",
series = "LNCS",
pages = "291--301",
address = "Lake Como, Italy",
publisher_address = "Berlin",
month = "18-20 " # apr,
organisation = "EvoNET",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, Layered
Learning, Hierarchical abstractions, Robot soccer,
Robots, Multiagent systems",
ISBN = "3-540-41899-7",
URL = "http://www.cs.nott.ac.uk/~smg/research/publications/eurogp-2001.ps",
URL = "http://www.cs.nott.ac.uk/~smg/research/publications/eurogp-2001.pdf",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2038&spage=291",
size = "11 pages",
abstract = "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.
",
notes = "EuroGP'2001, part of miller:2001:gp. See also
\cite{gustafson:mastersthesis}",
}
@MastersThesis{gustafson:mastersthesis,
author = "Steven M. Gustafson",
title = "Layered learning in genetic programming for a
co-operative robot soccer problem",
school = "Kansas State University",
year = "2000",
address = "Manhattan, KS, USA",
month = dec,
email = "smg@cs.nott.ac.uk",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.nott.ac.uk/~smg/research/publications/msthesis-2000.ps",
URL = "http://www.cs.nott.ac.uk/~smg/research/publications/msthesis-2000.pdf",
URL = "http://citeseer.ist.psu.edu/450396.html",
abstract = "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.",
notes = "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. \cite{hsu: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.
\cite{hsu: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.
\cite{gustafson:2001:EuroGP}",
}
@InProceedings{gustafson:2002:EuroGP,
title = "A Puzzle to Challenge Genetic Programming",
author = "Edmund Burke and Steven Gustafson and Graham Kendall",
editor = "James A. Foster and Evelyne Lutton and Julian Miller
and Conor Ryan and Andrea G. B. Tettamanzi",
booktitle = "Genetic Programming, Proceedings of the 5th European
Conference, EuroGP 2002",
volume = "2278",
series = "LNCS",
pages = "238--247",
publisher = "Springer-Verlag",
address = "Kinsale, Ireland",
publisher_address = "Berlin",
month = "3-5 " # apr,
year = "2002",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-43378-3",
URL = "http://www.cs.nott.ac.uk/~smg/research/publications/eurogp-2002.ps",
URL = "http://www.cs.nott.ac.uk/~smg/research/publications/eurogp-2002.pdf",
URL = "http://link.springer-ny.com/link/service/series/0558/bibs/2278/22780238.htm",
URL = "http://link.springer-ny.com/link/service/series/0558/papers/2278/22780238.pdf",
abstract = "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.",
notes = "EuroGP'2002, part of \cite{lutton:2002:GP} Best
poster",
}
@InProceedings{gustafson:2003:iidigpbaaoteop,
author = "Edmund K. Burke and Steven Gustafson and Graham
Kendall and Natalio Krasnogor",
title = "Is increased diversity in genetic programming
beneficial? An analysis of the effects on performance",
booktitle = "Proceedings of the 2003 Congress on Evolutionary
Computation CEC2003",
editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass
and Kay Chen Tan and Bob McKay and Daryl Essam and Tom
Gedeon",
pages = "1398--1405",
year = "2003",
publisher = "IEEE Press",
address = "Canberra",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "8-12 " # dec,
organisation = "IEEE Neural Network Council (NNC), Engineers Australia
(IEAust), Evolutionary Programming Society (EPS),
Institution of Electrical Engineers (IEE)",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-7804-0",
abstract = "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.",
notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust,
the EPS, and the IEE.",
}
@InProceedings{gustafson:2004:eurogp,
author = "Steven Gustafson and Edmund K. Burke and Graham
Kendall",
title = "Sampling of Unique Structures and Behaviours in
Genetic Programming",
booktitle = "Genetic Programming 7th European Conference, EuroGP
2004, Proceedings",
year = "2004",
editor = "Maarten Keijzer and Una-May O'Reilly and Simon M.
Lucas and Ernesto Costa and Terence Soule",
volume = "3003",
series = "LNCS",
pages = "279--288",
address = "Coimbra, Portugal",
publisher_address = "Berlin",
month = "5-7 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-21346-5",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=279",
URL = "http://www.cs.nott.ac.uk/~smg/research/publications/eurogp-sampling-2004.ps",
URL = "http://www.cs.nott.ac.uk/~smg/research/publications/eurogp-sampling-2004.pdf",
abstract = "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.",
notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in
conjunction with EvoCOP2004 and EvoWorkshops2004",
}
@PhdThesis{gustafson:2004:phdthesis,
author = "Steven Gustafson",
title = "An Analysis of Diversity in Genetic Programming",
school = "School of Computer Science and Information Technology,
University of Nottingham",
year = "2004",
month = feb,
address = "Nottingham, England",
keywords = "genetic algorithms, genetic programming",
size = "170 pages",
URL = "http://www.cs.nott.ac.uk/~smg/research/publications/phdthesis-gustafson.pdf",
URL = "http://www.cs.nott.ac.uk/~smg/research/publications/phdthesis-gustafson.ps",
URL = "http://www.cs.nott.ac.uk/~smg/research/publications/phdthesis-gustafson.ps.gz",
URL = "http://www.gustafsonresearch.com/thesis_html/",
abstract = "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.",
}
@Article{gustafson:2004:GPEM,
author = "Steven Gustafson and Aniko Ekart and Edmund Burke and
Graham Kendall",
title = "Problem Difficulty and Code Growth in Genetic
Programming",
journal = "Genetic Programming and Evolvable Machines",
year = "2004",
volume = "5",
number = "3",
pages = "271--290",
month = sep,
keywords = "genetic algorithms, genetic programming, population
diversity, code growth, problem difficulty",
ISSN = "1389-2576",
URL = "http://www.gustafsonresearch.com/research/publications/gustafson-gpem2004.pdf",
doi = "doi:10.1023/B:GENP.0000030194.98244.e3",
size = "20 pages",
abstract = "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.",
notes = "Article ID: 5272970",
}
@Article{gustafson:2004:IEEE,
author = "Edmund K. Burke and Steven Gustafson and Graham
Kendall",
title = "Diversity in Genetic Programming: An Analysis of
Measures and Correlation with Fitness",
journal = "IEEE Transactions on Evolutionary Computation",
publisher = "IEEE Press",
year = "2004",
volume = "8",
number = "1",
pages = "47--62",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.nott.ac.uk/~smg/research/publications/gustafson-ieee2004-preprint.pdf",
URL = "http://www.cs.nott.ac.uk/~smg/research/publications/gustafson-ieee2004-preprint.ps",
}
@InProceedings{eurogp:GustafsonV05,
author = "Steven Gustafson and Leonardo Vanneschi",
editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre
Collet and Jano I. {van Hemert} and Marco Tomassini",
title = "Operator-Based Distance for Genetic Programming:
Subtree Crossover Distance",
booktitle = "Proceedings of the 8th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3447",
year = "2005",
address = "Lausanne, Switzerland",
month = "30 " # mar # " - 1 " # apr,
organisation = "EvoNet",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-25436-6",
pages = "178--189",
URL = "http://www.cs.nott.ac.uk/~smg/research/publications/eurogp2005-gustafson-vanneschi.ps",
URL = "http://www.cs.nott.ac.uk/~smg/research/publications/eurogp2005-gustafson-vanneschi.pdf",
URL = "http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=178",
doi = "doi:10.1007/b107383",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in
conjunction with EvoCOP2005 and EvoWorkshops2005",
}
@InProceedings{eurogp:GustafsonBK05,
author = "Steven Gustafson and Edmund K. Burke and Natalio
Krasnogor",
editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre
Collet and Jano I. {van Hemert} and Marco Tomassini",
title = "The Tree-String Problem: An Artificial Domain for
Structure and Content Search",
booktitle = "Proceedings of the 8th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3447",
year = "2005",
address = "Lausanne, Switzerland",
month = "30 " # mar # " - 1 " # apr,
organisation = "EvoNet",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-25436-6",
pages = "215--226",
URL = "http://www.cs.nott.ac.uk/~smg/research/publications/eurogp2005-gustafson-etal.ps",
URL = "http://www.cs.nott.ac.uk/~smg/research/publications/eurogp2005-gustafson-etal.pdf",
URL = "http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=215",
doi = "doi:10.1007/b107383",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in
conjunction with EvoCOP2005 and EvoWorkshops2005",
}
@InProceedings{gustafson:2005:CEC,
author = "Steven Gustafson and Edmund K. Burke and Natalio
Krasnogor",
title = "On Improving Genetic Programming for Symbolic
Regression",
booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary
Computation",
year = "2005",
editor = "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",
volume = "1",
pages = "912--919",
address = "Edinburgh, UK",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "2-5 " # sep,
organisation = "IEEE Computational Intelligence Society, Institution
of Electrical Engineers (IEE), Evolutionary Programming
Society (EPS)",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-9363-5",
abstract = "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.",
notes = "CEC2005 - A joint meeting of the IEEE, the IEE, and
the EPS.",
}
@Article{Gustafson:2006:JPDC,
author = "Steven Gustafson and Edmund K. Burke",
title = "The Speciating Island Model: An alternative parallel
evolutionary algorithm",
journal = "Journal of Parallel and Distributed Computing",
year = "2006",
volume = "66",
number = "8",
pages = "1025--1036",
month = aug,
note = "Parallel Bioinspired Algorithms",
keywords = "genetic algorithms, genetic programming, Parallel
evolutionary algorithms, Islands",
doi = "doi:10.1016/j.jpdc.2006.04.017",
abstract = "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.",
}
@Article{Gustafson:2008:TEC,
title = "Crossover-Based Tree Distance in Genetic Programming",
author = "Steven Gustafson and Leonardo Vanneschi",
journal = "IEEE Transactions on Evolutionary Computation",
year = "2008",
month = aug,
volume = "12",
number = "4",
pages = "506--524",
keywords = "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",
ISSN = "1089-778X",
doi = "doi:10.1109/TEVC.2008.915993",
size = "19 pages",
abstract = "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).",
notes = "also known as \cite{4459225}",
}
@Article{Guven:2008:JIDE,
author = "Aytac Guven and Mustafa Gunal",
title = "Genetic Programming Approach for Prediction of Local
Scour Downstream of Hydraulic Structures",
journal = "Journal of Irrigation and Drainage Engineering",
year = "2008",
volume = "134",
number = "2",
pages = "241--249",
month = mar # "/" # apr,
publisher = "American Society of Civil Engineers",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1061/(ASCE)0733-9437(2008)134:2(241)",
abstract = "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.",
notes = "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.",
}
@Article{Guven:2008:clean,
author = "Aytac Guven and Ali Aytek and M. Ishak Yuce and
Hafzullah Aksoy",
title = "Genetic Programming-Based Empirical Model for Daily
Reference Evapotranspiration Estimation",
journal = "CLEAN - Soil, Air, Water",
year = "2008",
volume = "36",
number = "10-11",
pages = "905--912",
keywords = "genetic algorithms, genetic programming,
Evapotranspiration Artificial intelligence, Gene
expression programming",
doi = "DOI:10.1002/clen.200800009",
abstract = "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.",
notes = "Acta hydrochimica et hydrobiologica
Correspondence to Ali Aytek, Gaziantep University,
Department of Civil Engineering, Hydraulics Division,
Gaziantep, Turkey",
}
@Article{Guven:2009:JESS,
author = "Aytac Guven",
title = "Linear genetic programming for time-series modelling
of daily flow rate",
journal = "Journal of Earth System Science",
year = "2009",
volume = "118",
number = "2",
pages = "137--146",
month = apr,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, neural
networks, daily flows, flow forecasting",
ISSN = "0253-4126",
URL = "http://www.ias.ac.in/jess/apr2009/137.pdf",
size = "10 pages",
abstract = "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.",
notes = "Civil Engineering Department, Gaziantep University,
27310 Gaziantep, Turkey.",
}
@Article{Guven:2009:JHE,
author = "Aytac Guven and Ali Aytek",
title = "New Approach for Stage-Discharge Relationship:
Gene-Expression Programming",
journal = "Journal of Hydrologic Engineering",
year = "2009",
volume = "14",
number = "8",
pages = "812--820",
month = aug,
keywords = "genetic algorithms, genetic programming, gene
expression programming",
ISSN = "1084-0699",
doi = "doi:10.1061/(ASCE)HE.1943-5584.0000044",
abstract = "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.",
}
@Article{Guven2009985,
author = "Aytac Guven and H. Md. Azamathulla and N. A. Zakaria",
title = "Linear genetic programming for prediction of circular
pile scour",
journal = "Ocean Engineering",
volume = "36",
number = "12-13",
pages = "985--991",
year = "2009",
ISSN = "0029-8018",
doi = "doi:10.1016/j.oceaneng.2009.05.010",
URL = "http://www.sciencedirect.com/science/article/B6V4F-4WCTX10-3/2/805df81deb25d8c99465f876a03fc1e5",
keywords = "genetic algorithms, genetic programming, Scour,
Neuro-fuzzy, Circular pile, Regression",
abstract = "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.",
}
@Article{Guven:2011:WRM,
author = "Aytac Guven and Ozgur Kisi",
title = "Estimation of Suspended Sediment Yield in Natural
Rivers Using Machine-coded Linear Genetic Programming",
journal = "Water Resources Management",
year = "2011",
volume = "25",
number = "2",
pages = "691--704",
month = jan,
keywords = "genetic algorithms, genetic programming, gene
expression programming, Suspended sediment yield,
Modelling, Linear genetic programming, ANN, Neural
networks",
publisher = "Springer",
ISSN = "0920-4741",
doi = "doi:10.1007/s11269-010-9721-x",
size = "14 pages",
abstract = "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.",
affiliation = "Civil Engineering Department, Hydraulics Division,
Gaziantep University, 27310 Gaziantep, Turkey",
}
@Article{Guven:2011:IS,
author = "Aytac Guven and Ozgur Kisi",
title = "Daily pan evaporation modeling using linear genetic
programming technique",
journal = "Irrigation Science",
year = "2011",
volume = "29",
number = "2",
pages = "135--145",
keywords = "genetic algorithms, genetic programming, gene
expression programming",
ISSN = "0342-7188",
publisher = "Springer",
doi = "doi:10.1007/s00271-010-0225-5",
size = "11 pages",
abstract = "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.",
affiliation = "Civil Engineering Department, Hydraulics Division,
Gaziantep University, 27310 Gaziantep, Turkey",
}
@InCollection{guyaguler:2000:RPWTDRMP,
author = "Baris Guyaguler",
title = "Regression on Petroleum Well Test Data with the
Reservoir Model as a Parameter",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2000",
year = "2000",
editor = "John R. Koza",
pages = "188--197",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms",
notes = "part of \cite{koza:2000:gagp}",
}
@InProceedings{Haasdijk:2008:cec,
author = "E. Haasdijk and P. Vogt and A. E. Eiben",
title = "Social Learning in Population-Based Adaptive Systems",
booktitle = "2008 IEEE World Congress on Computational
Intelligence",
year = "2008",
editor = "Jun Wang",
address = "Hong Kong",
month = "1-6 " # jun,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-1823-7",
file = "EC0363.pdf",
abstract = "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.",
keywords = "genetic algorithms, genetic programming",
notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
EPS and the IET.",
}
@InCollection{haberman:1994:aa,
author = "Mike Haberman",
title = "Altrusitic Ants",
booktitle = "Artificial Life at Stanford 1994",
year = "1994",
editor = "John R. Koza",
pages = "34--43",
address = "Stanford, California, 94305-3079 USA",
month = jun,
organisation = "Stanford University",
publisher = "Stanford Bookstore",
keywords = "genetic algorithms",
ISBN = "0-18-182105-2",
notes = "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",
}
@InProceedings{hackworth:1999:IPARAGA,
author = "Tim Hackworth",
title = "India and Pakistan, a classic ``Richardson'' Arms
Race: {A} Genetic Algorithmic approach",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1543--1550",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "real world applications",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-700.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-700.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{hackworth:1999:GS,
author = "Tim Hackworth",
title = "Genetic algorithms; Some effects of redundancy in
chromosomes",
booktitle = "Late Breaking Papers at the 1999 Genetic and
Evolutionary Computation Conference",
year = "1999",
editor = "Scott Brave and Annie S. Wu",
pages = "99--106",
address = "Orlando, Florida, USA",
month = "13 " # jul,
keywords = "Genetic Algorithms",
notes = "GECCO-99LB",
}
@Article{Haddow:2011:GPEM,
author = "Pauline C. Haddow",
title = "Introduction: special issue on evolvable hardware
challenges",
journal = "Genetic Programming and Evolvable Machines",
year = "2011",
volume = "12",
number = "3",
pages = "181--182",
month = sep,
note = "EDITORIAL",
keywords = "genetic algorithms, evolvable hardware",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-011-9138-1",
size = "2 pages",
}
@Article{Haddow:2011:GPEM2,
author = "Pauline C. Haddow and Andy M. Tyrrell",
title = "Challenges of evolvable hardware: past, present and
the path to a promising future",
journal = "Genetic Programming and Evolvable Machines",
year = "2011",
volume = "12",
number = "3",
pages = "183--215",
month = sep,
keywords = "genetic algorithms, genetic programming, evolvable
hardware, EHW, Future technology, Scalability,
Computation medium, Review",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-011-9141-6",
size = "33 pages",
abstract = "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 evolution¡Çs 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.",
notes = "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",
}
@InProceedings{1274013,
author = "Fatima Zohra Hadjam and Claudio Moraga and Mohamed
Benmohamed",
title = "Cluster-based evolutionary design of digital circuits
using all improved multi-expression programming",
booktitle = "Late breaking paper at Genetic and Evolutionary
Computation Conference {(GECCO'2007)}",
year = "2007",
month = "7-11 " # jul,
editor = "Peter A. N. Bosman",
isbn13 = "978-1-59593-698-1",
pages = "2475--2482",
address = "London, United Kingdom",
keywords = "genetic algorithms, genetic programming, improved
multi-expression programming, islands model",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2475.pdf",
URL = "http://ls1-www.cs.uni-dortmund.de/pdf/Veroeffentlichungen/GECCO-2007.pdf",
doi = "doi:10.1145/1274000.1274013",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Distributed on CD-ROM at GECCO-2007 ACM Order No.
910071",
}
@InProceedings{Hadjam:2010:cec,
author = "Fatima Z. Hadjam and Claudio Moraga",
title = "Evolutionary design of reversible digital circuits
using {IMEP} the case of the even parity problem",
booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-6910-9",
abstract = "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.",
doi = "doi:10.1109/CEC.2010.5586252",
notes = "WCCI 2010. Also known as \cite{5586252}",
}
@Unpublished{hafner:1996:GGP,
author = "Christian Hafner and Juerg Froehlich and Hansueli
Gerber",
title = "Generalized Genetic Program",
note = "Submitted to the 'Evolutionary Computation' Journal",
year = "1996",
keywords = "genetic algorithms, genetic programming",
URL = "http://alphard.ethz.ch/gp.htm",
abstract = "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.",
notes = "postscript generated by MS word appears to be faulty.
GGP
See GPP manual
http://alphard.ethz.ch/Hafner/ggp/ggpmanu.htm",
size = "25 pages",
}
@InProceedings{hafner:1999:GFAUHEA,
author = "Christian Hafner and Jurg Frohlich",
title = "Generalized Function Analysis Using Hybrid
Evolutionary Algorithms",
booktitle = "Proceedings of the Congress on Evolutionary
Computation",
year = "1999",
editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc
Schoenauer and Xin Yao and Ali Zalzala",
volume = "1",
pages = "287--294",
address = "Mayflower Hotel, Washington D.C., USA",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "6-9 " # jul,
organisation = "Congress on Evolutionary Computation, IEEE / Neural
Networks Council, Evolutionary Programming Society,
Galesia, IEE",
publisher = "IEEE Press",
keywords = "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",
ISBN = "0-7803-5536-9 (softbound)",
ISBN = "0-7803-5537-7 (Microfiche)",
URL = "http://ieeexplore.ieee.org/iel5/6342/16952/00781938.pdf",
size = "8 pages",
abstract = "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.",
notes = "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.",
}
@InProceedings{hagedorn:2001:agpsppr,
author = "John G. Hagedorn and Judith E. Devaney",
title = "A Genetic Programming System with a Procedural Program
Representation",
booktitle = "2001 Genetic and Evolutionary Computation Conference
Late Breaking Papers",
year = "2001",
editor = "Erik D. Goodman",
pages = "152--159",
address = "San Francisco, California, USA",
month = "9-11 " # jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://math.nist.gov/mcsd/savg/papers/g2001.ps.gz",
notes = "GECCO-2001LB, NIST",
}
@InProceedings{hagiya:1998:tamc,
author = "Masami Hagiya",
title = "Towards Autonomous Molecular Computers",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "691--699",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "DNA Computing",
ISBN = "1-55860-548-7",
notes = "GP-98",
}
@InProceedings{haith:1999:CPS,
author = "Gary L. Haith and Silvano P. Colombano and Jason D.
Lohn and Dimitris Stassinopoulos",
title = "Coevolution for Problem Simplification",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "244--251",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-896.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-896.ps",
abstract = "predator-prey",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{Halaby:2010:ICEAC,
author = "A. Halaby and M. Awad and R. Khanna",
title = "Guided Search Space Genetic Programming for
identifying energy aware microarchitectural designs",
booktitle = "2010 International Conference on Energy Aware
Computing (ICEAC)",
year = "2010",
month = "16-18 " # dec,
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/ICEAC.2010.5702307",
notes = "fixed representation classifier. Electrical & Computer
Engineering, American University of Beirut, Beirut,
Lebanon. Also known as \cite{5702307}",
}
@Book{hall:1995:AIsd,
author = "Curt Hall and Paul Harmon",
title = "{AI} in Software Development: Genetic Programming,
Fuzzy Logic, and Neural Nets",
publisher = "cutter",
year = "1995",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cutter.com/itgroup/reports/aisoft.htm",
abstract = "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.",
notes = "lovering@cutter.com",
size = "45 pages",
}
@InCollection{hall:2004:GPTP,
author = "John M. Hall and Terence Soule",
title = "Does Genetic Programming Inherently Adopt Structured
Design Techniques?",
booktitle = "Genetic Programming Theory and Practice {II}",
year = "2004",
editor = "Una-May O'Reilly and Tina Yu and Rick L. Riolo and
Bill Worzel",
chapter = "10",
pages = "159--174",
address = "Ann Arbor",
month = "13-15 " # may,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, design,
function choice, root node",
ISBN = "0-387-23253-2",
URL = "http://www.cs.uidaho.edu/~tsoule/research/doesDesign.ps",
doi = "doi:10.1007/0-387-23254-0_10",
abstract = "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.",
notes = "part of \cite{oreilly: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.",
}
@Article{hamda:2002:IJAI,
author = "Hatem Hamda and Francois Jouve and Evelyne Lutton and
Marc Schoenauer and Michele Sebag",
title = "Compact Unstructured Representations for Evolutionary
Design",
journal = "International Journal of Applied Intelligence",
year = "2002",
volume = "16",
number = "2",
pages = "139--155",
note = "Special Issue on Creative Evolutionary Systems",
publisher = "Springer Netherlands",
keywords = "genetic algorithms, evolution strategies, Computer
Science",
ISSN = "0924-669X",
URL = "http://minimum.inria.fr/evo-lab/Publications/creative_soumis.ps.gz",
URL = "http://www.wkap.nl/prod/j/0924-669X",
doi = "doi:10.1023/A:1013666503249",
size = "29 pages",
abstract = "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.",
notes = "Bentely and Corne Special issue",
}
@InProceedings{hamel:2002:gecco,
author = "Lutz Hamel",
title = "Breeding Algebraic Structures---An Evolutionary
Approach To Inductive Equational Logic Programming",
booktitle = "GECCO 2002: Proceedings of the Genetic and
Evolutionary Computation Conference",
editor = "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",
year = "2002",
pages = "748--755",
address = "New York",
publisher_address = "San Francisco, CA 94104, USA",
month = "9-13 " # jul,
publisher = "Morgan Kaufmann Publishers",
keywords = "genetic algorithms, genetic programming, algebraic
specification, concept learning, equational logic,
inductive logic programming",
ur = "http://homepage.cs.uri.edu/faculty/hamel/pubs/gecco2002.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/GP034.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf",
ISBN = "1-55860-878-8",
notes = "GECCO-2002. A joint meeting of the eleventh
International Conference on Genetic Algorithms
(ICGA-2002) and the seventh Annual Genetic Programming
Conference (GP-2002)",
}
@InProceedings{Hamel:2007:AAIP,
author = "Lutz Hamel and Chi Shen",
title = "An Inductive Programming Approach to Algebraic
Specification",
booktitle = "Proceedings of the ECML 2007 Workshop on Approaches
and Applications of Inductive Programming (AAIP'07)",
year = "2007",
pages = "3--15",
address = "Warsaw",
month = "17-21 " # sep,
keywords = "genetic algorithms, genetic programming",
URL = "http://homepage.cs.uri.edu/faculty/hamel/pubs/aaip07-hamel.pdf",
URL = "http://www.ecmlpkdd2007.org/CD/workshops/AAIP/hamel_shen/hamel_shen.pdf",
size = "12 pages",
abstract = "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.",
notes = "Department of Computer Science and Statistics
University of Rhode Island Kingston, RI 02881, USA",
}
@Article{Hammell2010,
author = "Molly Hammell",
title = "Computational methods to identify mi{RNA} targets",
journal = "Seminars in Cell \& Developmental Biology",
year = "2010",
volume = "21",
number = "7",
pages = "738--744",
month = sep,
ISSN = "1084-9521",
doi = "doi:10.1016/j.semcdb.2010.01.004",
URL = "http://www.sciencedirect.com/science/article/B6WX0-4Y5GY3K-2/2/ee338722f9ce7b4b87a41bdd717fc22e",
keywords = "genetic algorithms, genetic programming, miRNA, miRNA
target prediction, Computational methods",
abstract = "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.",
notes = "survey",
}
@InProceedings{hampo:1992:new,
author = "Richard Hampo",
title = "Genetic Programming: {A} New Paradigm for Control and
Analysis",
booktitle = "Third ASME Symposium on Transportation Systems",
year = "1992",
pages = "155--163",
address = "Anaheim, California, USA",
month = "9--13 " # nov,
note = "Invited Paper at ASME Winter Annual Meeting",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/hampo_1992_new.pdf",
size = "5 pages",
}
@InProceedings{hampo:1992:cvs,
author = "R. J. Hampo and K. A. Marko",
title = "Application of Genetic Programming to Control of
Vehicle Systems",
booktitle = "Proceedings of the Intelligent Vehicles '92
Symposium",
year = "1992",
pages = "191--195",
address = "Detroit, Mi, USA",
month = jun # " 29 - " # jul # " 1",
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-0747-X",
doi = "doi:10.1109/IVS.1992.252255",
abstract = "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",
}
@Unpublished{hampo:1992:newford,
author = "R. J. Hampo",
title = "The Genetic Programming Paradigm: {A} New Tool for
Analysis and Control",
note = "Ford Proprietary",
month = "6 " # mar,
year = "1992",
keywords = "genetic algorithms, genetic programming",
notes = "Ford Technical Report SR-92-114",
}
@InProceedings{Hampo:1994:ICemdagGP,
author = "Richard J. Hampo and Bruce D. Bryant and Kenneth A.
Marko",
title = "{IC} Engine Misfire Detection Algorithm Generation
Using Genetic Programming",
booktitle = "EUFIT'94",
year = "1994",
pages = "1674--1678",
address = "Promenade 9, D-52076, Aachen, Germany",
month = "20--23 " # sep,
publisher = "ELITE-Foundation",
keywords = "genetic algorithms, genetic programming",
size = "5 pages",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/misfire-detection.PS.Z",
notes = "
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",
}
@InProceedings{hamza:ooc:gecco2004,
author = "Karim Hamza and Kazuhiro Saitou",
title = "Optimization of Constructive Solid Geometry Via a
Tree-Based Multi-objective Genetic Algorithm",
booktitle = "Genetic and Evolutionary Computation -- GECCO-2004,
Part II",
year = "2004",
editor = "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",
series = "Lecture Notes in Computer Science",
pages = "981--992",
address = "Seattle, WA, USA",
publisher_address = "Heidelberg",
month = "26-30 " # jun,
organisation = "ISGEC",
publisher = "Springer-Verlag",
volume = "3103",
ISBN = "3-540-22343-6",
ISSN = "0302-9743",
URL = "http://link.springer.de/link/service/series/0558/bibs/3103/31030981.htm",
size = "12",
keywords = "genetic algorithms, genetic programming",
notes = "GECCO-2004 A joint meeting of the thirteenth
international conference on genetic algorithms
(ICGA-2004) and the ninth annual genetic programming
conference (GP-2004)",
}
@InProceedings{Han:2006:WCICA,
author = "Pu Han and Shiliang Zhou and Dongfeng Wang",
title = "A Multi-objective Genetic Programming/ {NARMAX}
Approach to Chaotic Systems Identification",
booktitle = "The Sixth World Congress on Intelligent Control and
Automation, WCICA 2006",
year = "2006",
volume = "1",
pages = "1735--1739",
address = "Dalian",
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-4244-0332-4",
doi = "doi:10.1109/WCICA.2006.1712650",
abstract = "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",
notes = "Dept. of Autom., North China Electr. Power Univ.,
Baoding",
}
@InCollection{han:2000:GHSPGA,
author = "Todd Han",
title = "Generating Hard Satisfiability Problems with Genetic
Algorithms",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2000",
year = "2000",
editor = "John R. Koza",
pages = "198--205",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms",
notes = "part of \cite{koza:2000:gagp}",
}
@InProceedings{Hand:1997:gn,
author = "Charles Hand",
title = "Genetic Nets",
booktitle = "Late Breaking Papers at the 1997 Genetic Programming
Conference",
year = "1997",
editor = "John R. Koza",
address = "Stanford University, CA, USA",
publisher_address = "Stanford University, Stanford, California,
94305-3079, USA",
month = "13--16 " # jul,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-206995-8",
notes = "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",
}
@Misc{hand:1994:GPreview,
author = "David J. Hand",
title = "Evolutionary computation",
journal = "Statistics and Computing",
year = "1994",
volume = "4",
number = "2",
pages = "158",
month = jun,
note = "Book review of Koza's ``Genetic Programming''",
doi = "DOI:10.1007/BF00175359",
keywords = "genetic algorithms, genetic programming",
size = "0.7 pages",
ISSN = "0960-3174",
notes = "Special issue on Evolutionary Programming. Favourable
review of \cite{koza:book}",
}
@Article{hand:2003:GPEM,
author = "David J. Hand",
title = "Book Review: {Data} Mining and Knowledge Discovery
with Evolutionary Programs",
journal = "Genetic Programming and Evolvable Machines",
year = "2003",
volume = "4",
number = "3",
pages = "287--289",
month = sep,
keywords = "genetic algorithms",
ISSN = "1389-2576",
doi = "doi:10.1023/A:1025128524617",
notes = "Review of Alex A. Freitas' \cite{freitas:2002:book}
Article ID: 5141125",
}
@InProceedings{handa:1999:CGASDCSP,
author = "Hisashi Handa and Osamu Katai and Tadataka Konishi and
Mitsuru Baba",
title = "Coevolutionary Genetic Algorithms for Solving Dynamic
Constraint Satisfaction Problems",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "252--257",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-394.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-394.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{icga93:handley,
author = "Simon Handley",
title = "Automatic Learning of a Detector for alpha-helices in
Protein Sequences Via Genetic Programming",
year = "1993",
booktitle = "Proceedings of the 5th International Conference on
Genetic Algorithms, ICGA-93",
editor = "Stephanie Forrest",
publisher = "Morgan Kaufmann",
address = "University of Illinois at Urbana-Champaign",
month = "17-21 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "271--278",
size = "8 pages",
abstract = "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).",
URL = "http://www-leland.stanford.edu/~shandley/postscript/alpha-helices.ps.gz
broken",
notes = "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",
}
@InProceedings{Handley:1993:GPagplGP,
author = "Simon Handley",
title = "The genetic planner: The automatic generation of plans
for a mobile robot via genetic programming",
booktitle = "Proceedings of the Eighth IEEE International Symposium
on Intelligent Control",
year = "1993",
pages = "190--195",
address = "Chicago, USA",
month = aug,
organisation = "The IEEE Control System Society",
publisher = "IEEE",
keywords = "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",
doi = "doi:10.1109/ISIC.1993.397715",
size = "6 pages",
abstract = "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.",
notes = "Chicago, IL, USA
",
}
@InProceedings{Handley:1991:agplGPADF,
author = "S. Handley",
title = "The automatic generation of plans for a mobile robot
via genetic programming with automatically defined
functions",
booktitle = "Proceedings of the Fifth Workshop on Neural Networks:
An International Conference on Computational
Intelligence: Neural Networks, Fuzzy Systems,
Evolutionary Programming, and Virtual Reality",
year = "1991",
organisation = "The Society for Computer Simulation",
keywords = "genetic algorithms, genetic programming",
}
@InCollection{kinnear:handley,
title = "The Automatic Generations of Plans for a Mobile Robot
via Genetic Programming with Automatically Defined
Functions",
author = "Simon G. Handley",
booktitle = "Advances in Genetic Programming",
publisher = "MIT Press",
editor = "Kenneth E. {Kinnear, Jr.}",
year = "1994",
chapter = "18",
pages = "391--407",
keywords = "genetic algorithms, genetic programming",
size = "17 pages",
broken = "http://www-leland.stanford.edu/~shandley/postscript/kinnear.ps.gz",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262111888",
abstract = "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.",
notes = "
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)
",
}
@InProceedings{Handley:1994:DAGpcp,
author = "S. Handley",
title = "On the use of a directed acyclic graph to represent a
population of computer programs",
booktitle = "Proceedings of the 1994 IEEE World Congress on
Computational Intelligence",
year = "1994",
pages = "154--159",
volume = "1",
address = "Orlando, Florida, USA",
month = "27-29 " # jun,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, DAG",
doi = "doi:10.1109/ICEC.1994.350024",
size = "6 pages",
abstract = "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).",
notes = "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.",
}
@InProceedings{Handley:1994:alAHGP,
author = "S. Handley",
title = "Automated learning of a detector for the cores of
a-helices in protein sequences via genetic
programming",
booktitle = "Proceedings of the 1994 IEEE World Congress on
Computational Intelligence",
year = "1994",
volume = "1",
pages = "474--479",
address = "Orlando, Florida, USA",
month = "27-29 " # jun,
publisher = "IEEE Press",
broken = "http://www-leland.stanford.edu/~shandley/postscript/helix_segments_paper.ps.gz",
doi = "doi:10.1109/ICEC.1994.349904",
keywords = "genetic algorithms, genetic programming",
size = "6 pages",
abstract = "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).",
}
@InProceedings{handley:1994:solvent,
author = "Simon G. Handley",
title = "The prediction of the degree of exposure to solvent of
amino acid residues via genetic programming",
booktitle = "Second International Conference on Intelligent Systems
for Molecular Biology",
year = "1994",
address = "Stanford University, Stanford, CA, USA",
publisher = "AAAI Press",
keywords = "genetic algorithms, genetic programming",
broken = "http://www-leland.stanford.edu/~shandley/postscript/pburied.ps.gz",
URL = "http://www.aaai.org/Library/ISMB/ismb94contents.php",
abstract = "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.",
}
@InCollection{handley:1994:al,
author = "Simon G. Handley and Tod Klingler",
title = "Automated learning of a detector for a-helices in
protein sequences via genetic programming",
booktitle = "Artificial Life at Stanford 1993",
year = "1993",
editor = "John R. Koza",
address = "Stanford, California, 94305-3079 USA",
month = dec,
organisation = "Stanford University",
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-171957-6",
notes = "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",
}
@InProceedings{handley:1995:DNAsplice,
author = "Simon Handley",
title = "Predicting Whether or Not a 60-base {DNA} Sequence
Contains a Centrally-Located Splice Site Using Genetic
Programming",
booktitle = "Proceedings of the Workshop on Genetic Programming:
From Theory to Real-World Applications",
year = "1995",
editor = "Justinian P. Rosca",
pages = "98--103",
address = "Tahoe City, California, USA",
month = "9 " # jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/handley_1995_DNAsplice.pdf",
broken = "http://www-leland.stanford.edu/~shandley/postscript/splicej.ps.gz",
broken = "http://www-leland.stanford.edu/~shandley/postscript/ML95GPwkshp.ps.gz",
URL = "http://www.cs.rochester.edu/u/rosca/ml95.htm",
size = "6 pages",
abstract = "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.",
notes = "Pop size 64,000 part of \cite{rosca:1995:ml}",
}
@InProceedings{handley:1995:IorE,
author = "Simon Handley",
title = "Classifying Nucleic Acid Sub-Sequences as Introns or
Exons Using Genetic Programming",
booktitle = "Proceedings of the Third International Conference on
Intelligent Systems for Molecular Biology (ISMB-95)",
year = "1995",
editor = "Christopher Rawlins and Dominic Clark and Russ Altman
and Lawrence Hunter and Thomas Lengauer and Shoshana
Wodak",
pages = "162--169",
address = "Cambridge, UK",
publisher_address = "Menlo Park, CA, USA",
publisher = "AAAI Press",
keywords = "genetic algorithms, genetic programming",
broken = "http://www-leland.stanford.edu/~shandley/postscript/iep-ISMB.ps.gz",
URL = "http://www.aaai.org/Library/ISMB/ismb95contents.php",
abstract = "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).",
notes = "PMID: 7584433
",
}
@InProceedings{handley:1995:coliP,
author = "Simon Handley",
title = "Predicting Whether or not a Nucleic Acid Sequence is
an {E}. coli Promoter Region using Genetic
Programming",
booktitle = "Proceedings of the First International Symposium on
Intelligence in Neural and Biological Systems INBS-95",
year = "1995",
pages = "122--127",
address = "Herndon, Virginia, USA",
publisher_address = "Los Alamitos, California, USA",
month = "29-31 " # may,
organisation = "IEEE Comitteee on Pattern Analysis and Machine
Intelligence (PAMI)",
publisher = "IEEE Computer Society Press",
keywords = "genetic algorithms, genetic programming",
broken = "http://www-leland.stanford.edu/~shandley/postscript/postscript/INBS-camera-ready.ps.gz",
doi = "doi:10.1109/INBS.1995.404270",
abstract = "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.",
notes = "Pop size 32,000
",
}
@InProceedings{handley:1995:DNAspliceF,
author = "Simon Handley",
title = "Predicting Whether Or Not a 60-Base {DNA} Sequence
Contains a Centrally-Located Splice Site Using Genetic
Programming",
booktitle = "Working Notes for the AAAI Symposium on Genetic
Programming",
year = "1995",
editor = "E. V. Siegel and J. R. Koza",
pages = "17--22",
address = "MIT, Cambridge, MA, USA",
publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA",
month = "10--12 " # nov,
publisher = "AAAI",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.aaai.org/Papers/Symposia/Fall/1995/FS-95-01/FS95-01-003.pdf",
URL = "http://www.aaai.org/Library/Symposia/Fall/fs95-01.php",
size = "6 pages",
abstract = "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.",
notes = "AAAI-95f GP. Part of \cite{siegel:1995:aaai-fgp} {\em
Telephone:} 415-328-3123 {\em Fax:} 415-321-4457 {\em
email} info@aaai.org {\em URL:} http://www.aaai.org/",
}
@InProceedings{handley:1996:pdesaarGP,
author = "Simon Handley",
title = "The Prediction of the Degree of Exposure to Solvent of
Amino Acid Residues via Genetic Programming",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "297--300",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
size = "4 pages",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96",
}
@InProceedings{handley:1996:nfsssp,
author = "Simon Handley",
title = "A New Class of Function Sets for Solving Sequence
Problems",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "301--308",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
size = "8 pages",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96",
}
@PhdThesis{handley:thesis,
author = "Simon Handley",
title = "Automatically Discovering Solutions that Flexibly
Combine Iterative and non-Iterative Computations",
school = "Department of Computer Science, Stanford University",
year = "1998",
keywords = "genetic algorithms, genetic programming",
size = "pages",
}
@Article{oai:biomedcentral.com:1471-2105-8-23,
title = "Motif kernel generated by genetic programming improves
remote homology and fold detection",
author = "Tony Handstad and Arne J H Hestnes and Pal Saetrom",
journal = "BMC Bioinformatics",
year = "2007",
volume = "8",
number = "23",
month = jan # "~25",
publisher = "BioMed Central Ltd.",
bibsource = "OAI-PMH server at www.biomedcentral.com",
language = "en",
oai = "oai:biomedcentral.com:1471-2105-8-23",
rights = "Copyright 2007 H{\aa}ndstad et al; licensee BioMed
Central Ltd.",
keywords = "genetic algorithms, genetic programming, GPkernel,
SVM, MISD, boosting",
ISSN = "1471-2105",
URL = "http://www.biomedcentral.com/content/pdf/1471-2105-8-23.pdf",
URL = "http://www.biomedcentral.com/1471-2105/8/23",
doi = "doi:10.1186/1471-2105-8-23",
url_undergraduate_thesis = "http://www.diva-portal.org/diva/getDocument?urn_nbn_no_ntnu_diva-1030-1__fulltext.pdf",
size = "16 pages",
abstract = "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.",
notes = "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.",
}
@InCollection{hahn:1994:p-p,
author = "Mark S. Hanh",
title = "Simulating Evolution In a Kolmogorov Predator-Prey
Model With Genetic Extensions",
booktitle = "Artificial Life at Stanford 1994",
year = "1994",
editor = "John R. Koza",
pages = "44--53",
address = "Stanford, California, 94305-3079 USA",
month = jun,
organisation = "Stanford University",
publisher = "Stanford Bookstore",
keywords = "genetic algorithms",
ISBN = "0-18-182105-2",
notes = "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",
}
@InProceedings{Hanselmann:1996:Chemeca,
author = "K. Hanselmann and G. W. Barton and B. McKay and M. J.
Willis",
title = "Modelling a Transformer Oil Regeneration Process Using
Genetic Programming",
booktitle = "Chemeca 96: Excellence in Chemical Engineering;
Proceedings of the 24th Australian and New Zealand
Chemical Engineering Conference and Exhibition",
year = "1996",
editor = "Gordon Weiss",
number = "96/13",
series = "National conference publication",
pages = "9--84 [in volume 2]",
address = "Barton, ACT, Australia",
publisher_address = "Australia",
publisher = "Institution of Engineers",
keywords = "genetic algorithms, genetic programming, Data
processing, Neural networks (Computer science),
Mathematical models, Linear programming, Mathematical
models, Offshore oil industry, Electric insulators and
insulation, Oils",
ISBN = "0-85825-658-4",
URL = "http://search.informit.com.au/documentSummary;dn=894065266629714;res=IELENG",
abstract = "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.",
notes = "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",
}
@Article{Hansen:2003:GPEM,
author = "James V. Hansen",
title = "Genetic Programming Experiments with Standard and
Homologous Crossover Methods",
journal = "Genetic Programming and Evolvable Machines",
year = "2003",
volume = "4",
number = "1",
pages = "53--66",
month = mar,
keywords = "genetic algorithms, genetic programming, homologous
crossover, regression, classifications",
ISSN = "1389-2576",
doi = "doi:10.1023/A:1021825110329",
abstract = "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.",
notes = "Article ID: 5113072",
}
@Article{hansen:2004:COR,
author = "James V. Hansen",
title = "Genetic search methods in air traffic control",
journal = "Computers and Operations Research",
year = "2004",
volume = "31",
pages = "445--459",
keywords = "genetic algorithms, genetic programming, Aircraft
traffic control, Genetic search, Heuristics,
Scheduling",
number = "3",
URL = "http://www.sciencedirect.com/science/article/B6VC5-480622F-4/2/468055c77aed02e9629b07b8dc6b0dbe",
doi = "doi:10.1016/S0305-0548(02)00228-9",
abstract = "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.",
owner = "wlangdon",
}
@Article{Hansen:2006:DSS,
author = "James V. Hansen and Paul Benjamin Lowry and Rayman D.
Meservy and Daniel M. McDonald",
title = "Genetic programming for prevention of cyberterrorism
through dynamic and evolving intrusion detection",
journal = "Decision Support Systems",
year = "2007",
volume = "43",
number = "4",
pages = "1362--1374",
month = aug,
note = "Special Issue Clusters",
keywords = "genetic algorithms, genetic programming,
Cyberterrorism, Homologous crossover, Intrusion
detection, Pattern recognition, Information security",
doi = "doi:10.1016/j.dss.2006.04.004",
abstract = "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.",
}
@InProceedings{Hao:2006:ASPGP,
title = "Developmental evaluation in genetic programming: {A}
{TAG}-based framework",
author = "Tuan-Hao Hoang and Daryl Essam and R. I. McKay and
Xuan Hoai Nguyen",
booktitle = "Proceedings of the Third Asian-Pacific workshop on
Genetic Programming",
year = "2006",
editor = "The Long Pham and Hai Khoi Le and Xuan Hoai Nguyen",
pages = "86--97",
ISSN = "18590209",
address = "Military Technical Academy, Hanoi, VietNam",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/aspgp06/haodtag3p_new.pdf",
size = "12 pages",
abstract = "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.",
notes = "http://www.aspgp.org",
}
@InProceedings{hara:1999:EAADG,
author = "Akira Hara and Tomoharu Nagao",
title = "Emergence of the cooperative behavior using {ADG};
Automatically Defined Groups",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1039--1046",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-415.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@Article{Hardesty:2012:MITnews,
author = "Larry Hardesty",
title = "The mathematics of taste",
journal = "MIT news",
year = "2012",
month = jan # " 24",
keywords = "genetic algorithms, genetic programming",
URL = "http://web.mit.edu/newsoffice/2012/what-smells-good-0124.html",
size = "~1 page",
abstract = "By using 'genetic programming' to crossbreed
algorithms, researchers help flavour companies figure
out what their customers like.",
notes = "See \cite{Veeramachaneni:2012:GPEM}
MIT News Office 77 Massachusetts Avenue, Room 11-400,
Cambridge, MA 02139-4307, 617.253.2700",
}
@InProceedings{Harding:2003:eh,
author = "Simon Harding and Julian Francis Miller",
editor = "Jason Lohn and Ricardo Zebulum and James Steincamp and
Didier Keymeulen and Adrian Stoica and Michael I.
Ferguson",
month = "9-11 " # jul,
year = "2003",
title = "A Scalable Platform for Intrinsic Hardware and in
materio Evolution",
booktitle = "2003 {NASA/DoD} Conference on Evolvable Hardware",
pages = "221--224",
publisher = "IEEE Computer Society",
address = "Chicago, Illinois",
organisation = "NASA Ames Research Center",
publisher_address = "10662 Los Vaqueros Circle, P.O. Box 3014, Los
Alamitos, CA, 90720-1314, USA",
email = "s.l.harding@cs.bham.ac.uk",
ISBN = "0-7695-1977-6",
URL = "EHW http://ehw.jpl.nasa.gov",
notes = "EH2003 http://ic.arc.nasa.gov/projects/eh2003/",
}
@InProceedings{eurogp:HardingM05,
author = "Simon Harding and Julian F. Miller",
editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre
Collet and Jano I. {van Hemert} and Marco Tomassini",
title = "Evolution of Robot Controller Using Cartesian Genetic
Programming",
booktitle = "Proceedings of the 8th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3447",
year = "2005",
address = "Lausanne, Switzerland",
month = "30 " # mar # " - 1 " # apr,
organisation = "EvoNet",
keywords = "genetic algorithms, genetic programming, cartesian
genetic programming",
ISBN = "3-540-25436-6",
pages = "62--73",
URL = "http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=62",
doi = "doi:10.1007/b107383",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in
conjunction with EvoCOP2005 and EvoWorkshops2005",
}
@InProceedings{harding:2005:EH,
author = "Simon Harding and Julian F. Miller",
title = "Evolution In Materio : {A} Real-Time Robot Controller
in Liquid Crystal",
booktitle = "Proceedings of the 2005 NASA/DoD Conference on
Evolvable Hardware",
year = "2005",
editor = "Jason Lohn and David Gwaltney and Gregory Hornby and
Ricardo Zebulum and Didier Keymeulen and Adrian
Stoica",
pages = "229--238",
address = "Washington, DC, USA",
month = "29 " # jun # "-1 " # jul,
publisher = "IEEE Press",
publisher_address = "IEEE Service Center 445 Hoes Lane Asia P.O. Box
1331 Piscataway, NJ 08855-1331",
organisation = "NASA, DoD",
keywords = "genetic algorithms, genetic programming, EHW",
ISBN = "0-7695-2399-4",
doi = "doi:10.1109/EH.2005.22",
abstract = "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.",
notes = "EH2005 IEEE Computer Society Order Number P2399",
}
@InProceedings{eurogp07:harding,
author = "Simon Harding and Wolfgang Banzhaf",
title = "Fast genetic programming on {GPU}s",
editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and
Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar",
booktitle = "Proceedings of the 10th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "4445",
year = "2007",
address = "Valencia, Spain",
month = "11-13 " # apr,
pages = "90--101",
keywords = "genetic algorithms, genetic programming, Cartesian
genetic programming",
ISBN = "3-540-71602-5",
isbn13 = "978-3-540-71602-0",
doi = "doi:10.1007/978-3-540-71605-1_9",
abstract = "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.",
notes = "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
\cite{langdon:2005:CS}
Part of \cite{ebner:2007:GP} EuroGP'2007 held in
conjunction with EvoCOP2007, EvoBIO2007 and
EvoWorkshops2007",
}
@InProceedings{1277161,
author = "Simon L. Harding and Julian F. Miller and Wolfgang
Banzhaf",
title = "Self-modifying cartesian genetic programming",
booktitle = "GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation",
year = "2007",
editor = "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",
volume = "1",
isbn13 = "978-1-59593-697-4",
pages = "1021--1028",
address = "London",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1021.pdf",
doi = "doi:10.1145/1276958.1277161",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
month = "7-11 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, Cartesian
Genetic Programming, Generative and Developmental
Systems, evolution, self modification",
abstract = "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.",
notes = "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",
}
@InProceedings{10.1109/HPCS.2007.17,
author = "S. L. Harding and W. Banzhaf",
title = "Fast Genetic Programming and Artificial Developmental
Systems on {GPU}s",
booktitle = "21st International Symposium on High Performance
Computing Systems and Applications (HPCS'07)",
year = "2007",
pages = "2",
address = "Canada",
publisher = "IEEE Computer Society",
keywords = "genetic algorithms, genetic programming, GPU",
ISBN = "0-7695-2813-9",
doi = "doi:10.1109/HPCS.2007.17",
abstract = "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.",
}
@InProceedings{Harding:2008:cec,
author = "Simon Harding",
title = "Evolution of Image Filters on Graphics Processor Units
Using Cartesian Genetic Programming",
booktitle = "2008 IEEE World Congress on Computational
Intelligence",
year = "2008",
editor = "Jun Wang",
pages = "1921--1928",
address = "Hong Kong",
month = "1-6 " # jun,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-1823-7",
file = "EC0465.pdf",
doi = "doi:10.1109/CEC.2008.4631051",
abstract = "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.",
keywords = "genetic algorithms, genetic programming, Cartesian
Genetic Programming, GPU",
notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
EPS and the IET.",
}
@Article{harding_genetic_2008,
author = "S. Harding and W. Banzhaf",
title = "Genetic programming on {GPUs} for image processing",
journal = "International Journal of High Performance Systems
Architecture",
year = "2008",
volume = "1",
number = "4",
pages = "231--240",
keywords = "genetic algorithms, genetic programming, GPU, graphics
processing units, image filters, image processing,
parallel processing, reverse engineering",
ISSN = "1751-6528",
URL = "http://www.inderscience.com/search/index.php?action=record&rec_id=24207&prevQuery=&ps=10&m=or",
doi = "doi:10.1504/IJHPSA.2008.024207",
abstract = "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.",
notes = "IJHPSA",
}
@InProceedings{Harding:2009:eurogp,
author = "Simon Harding and Julian Miller and Wolfgang Banzhaf",
title = "Self Modifying Cartesian Genetic Programming:
Fibonacci, Squares, Regression and Summing",
booktitle = "Proceedings of the 12th European Conference on Genetic
Programming, EuroGP 2009",
year = "2009",
editor = "Leonardo Vanneschi and Steven Gustafson and Alberto
Moraglio and Ivanoe {De Falco} and Marc Ebner",
volume = "5481",
series = "LNCS",
pages = "133--144",
address = "Tuebingen",
month = apr # " 15-17",
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, cartesian
genetic programming, developmental systems",
isbn13 = "978-3-642-01180-1",
URL = "http://www.evolutioninmaterio.com/preprints/eurogp_smcgp_1.ps.pdf",
doi = "doi:10.1007/978-3-642-01181-8_12",
size = "12 pages",
abstract = "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.",
notes = "Part of \cite{conf/eurogp/2009} EuroGP'2009 held in
conjunction with EvoCOP2009, EvoBIO2009 and
EvoWorkshops2009",
}
@InProceedings{Harding:2009:cec,
author = "S. Harding and J. F. Miller and W. Banzhaf",
title = "Self Modifying Cartesian Genetic Programming: Parity",
booktitle = "2009 IEEE Congress on Evolutionary Computation",
year = "2009",
editor = "Andy Tyrrell",
pages = "285--292",
address = "Trondheim, Norway",
month = "18-21 " # may,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-2959-2",
file = "P128.pdf",
doi = "doi:10.1109/CEC.2009.4982960",
abstract = "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.",
keywords = "genetic algorithms, genetic programming, cartesian
genetic programming",
notes = "CEC 2009 - A joint meeting of the IEEE, the EPS and
the IET. IEEE Catalog Number: CFP09ICE-CDR",
}
@InProceedings{DBLP:conf/gecco/HardingMB09,
author = "Simon Harding and Julian Francis Miller and Wolfgang
Banzhaf",
title = "Evolution, development and learning using
self-modifying cartesian genetic programming",
booktitle = "GECCO '09: Proceedings of the 11th Annual conference
on Genetic and evolutionary computation",
year = "2009",
editor = "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",
pages = "699--706",
address = "Montreal",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "8-12 " # jul,
organisation = "SigEvo",
keywords = "genetic algorithms, genetic programming, cartesian
genetic programming",
isbn13 = "978-1-60558-325-9",
bibsource = "DBLP, http://dblp.uni-trier.de",
doi = "doi:10.1145/1569901.1569998",
abstract = "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?",
notes = "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.",
}
@InProceedings{hardinggpem2009,
author = "Simon L. Harding and Wolfgang Banzhaf",
title = "Distributed Genetic Programming on {GPU}s using
{CUDA}",
booktitle = "Workshop on Parallel Architectures and Bioinspired
Algorithms",
year = "2009",
editor = "Ignacio Hidalgo and Francisco Fernandez and Juan
Lanchares",
pages = "1--10",
address = "Raleigh, NC, USA",
month = "13 " # sep,
publisher = "Universidad Complutense de Madrid",
keywords = "genetic algorithms, genetic programming, GPU",
URL = "http://www.evolutioninmaterio.com/preprints/CudaParallelCompilePP.pdf",
abstract = "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.",
notes = "mono dot net. WPABA'09
http://bioinspired.dacya.ucm.es/doku.php?id=workshops",
}
@Article{Harding:2010:GPEM,
author = "Simon Harding and Julian F. Miller and Wolfgang
Banzhaf",
title = "Developments in Cartesian Genetic Programming:
self-modifying {CGP}",
journal = "Genetic Programming and Evolvable Machines",
year = "2010",
volume = "11",
number = "3/4",
pages = "397--439",
month = sep,
note = "Tenth Anniversary Issue: Progress in Genetic
Programming and Evolvable Machines",
keywords = "genetic algorithms, genetic programming, Cartesian
Genetic Programming, Developmental systems",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-010-9114-1",
size = "43 pages",
abstract = "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",
}
@InProceedings{Harding:2010:gecco,
author = "Simon Harding and Julian F. Miller and Wolfgang
Banzhaf",
title = "Self modifying cartesian genetic programming: finding
algorithms that calculate pi and e to arbitrary
precision",
booktitle = "GECCO '10: Proceedings of the 12th annual conference
on Genetic and evolutionary computation",
year = "2010",
editor = "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",
isbn13 = "978-1-4503-0072-8",
pages = "579--586",
keywords = "genetic algorithms, genetic programming, cartesian
genetic programming, Generative and developmental
systems",
month = "7-11 " # jul,
organisation = "SIGEVO",
address = "Portland, Oregon, USA",
doi = "doi:10.1145/1830483.1830591",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{1830591} GECCO-2010 A joint
meeting of the nineteenth international conference on
genetic algorithms (ICGA-2010) and the fifteenth annual
genetic programming conference (GP-2010)",
}
@InCollection{Harding:2010:GPTP,
author = "Simon Harding and Wolfgang Banzhaf and Julian F.
Miller",
title = "A Survey of Self Modifying Cartesian Genetic
Programming",
booktitle = "Genetic Programming Theory and Practice VIII",
year = "2010",
editor = "Rick Riolo and Trent McConaghy and Ekaterina
Vladislavleva",
series = "Genetic and Evolutionary Computation",
volume = "8",
address = "Ann Arbor, USA",
month = "20-22 " # may,
publisher = "Springer",
chapter = "6",
pages = "91--107",
keywords = "genetic algorithms, genetic programming, cartesian
genetic programming",
isbn13 = "978-1-4419-7746-5",
URL = "http://www.springer.com/computer/ai/book/978-1-4419-7746-5",
notes = "part of \cite{Riolo:2010:GPTP}",
}
@InProceedings{Harding:2011:GECCO,
author = "Simon Harding and Julian F. Miller and Wolfgang
Banzhaf",
title = "{SMCGP2}: self modifying cartesian genetic programming
in two dimensions",
booktitle = "GECCO '11: Proceedings of the 13th annual conference
on Genetic and evolutionary computation",
year = "2011",
editor = "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",
isbn13 = "978-1-4503-0557-0",
pages = "1491--1498",
keywords = "genetic algorithms, genetic programming, cartesian
genetic programming, developmental systems",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001576.2001777",
publisher = "ACM",
publisher_address = "New York, NY, USA",
size = "8 pages",
abstract = "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.",
notes = "hill climbing. General solution to parity.
Also known as \cite{2001777} GECCO-2011 A joint meeting
of the twentieth international conference on genetic
algorithms (ICGA-2011) and the sixteenth annual genetic
programming conference (GP-2011)",
}
@InProceedings{Harding:2011:GECCOcompQ,
author = "Simon Harding and Julian F. Miller and Wolfgang
Banzhaf",
title = "{SMCGP2}: finding algorithms that approximate
numerical constants using quaternions and complex
numbers",
booktitle = "GECCO '11: Proceedings of the 13th annual conference
companion on Genetic and evolutionary computation",
year = "2011",
editor = "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",
isbn13 = "978-1-4503-0690-4",
keywords = "genetic algorithms, genetic programming, cartesian
genetic programming: Poster",
pages = "197--198",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001858.2001968",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{2001968} Distributed on CD-ROM at
GECCO-2011.
ACM Order Number 910112.",
}
@InProceedings{Harding:2011:GECCOcomp,
author = "Simon Harding and Wolfgang Banzhaf",
title = "Implementing cartesian genetic programming classifiers
on graphics processing units using {GPU}.{NET}",
booktitle = "GECCO 2011 Computational intelligence on consumer
games and graphics hardware (CIGPU)",
year = "2011",
editor = "Simon Harding and W. B. Langdon and Man Leung Wong and
Garnett Wilson and Tony Lewis",
isbn13 = "978-1-4503-0690-4",
keywords = "genetic algorithms, genetic programming, cartesian
genetic programming, GPU",
pages = "463--470",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001858.2002034",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{2002034} Distributed on CD-ROM at
GECCO-2011.
ACM Order Number 910112.",
}
@InCollection{Harding:2011:CGP.ch4,
author = "Simon L. Harding and Julian F. Miller and Wolfgang
Banzhaf",
title = "Self-Modifying Cartesian Genetic Programming",
booktitle = "Cartesian Genetic Programming",
publisher = "Springer",
editor = "Julian F. Miller",
year = "2011",
series = "Natural Computing Series",
chapter = "4",
pages = "101--124",
keywords = "genetic algorithms, genetic programming, Cartesian
Genetic Programming",
isbn13 = "978-3-642-17309-7",
URL = "http://www.springer.com/computer/theoretical+computer+science/book/978-3-642-17309-7",
doi = "doi:10.1007/978-3-642-17310-3_4",
abstract = "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.",
notes = "part of \cite{Miller:CGP}",
}
@InCollection{Harding:2011:CGP.ch8,
author = "Simon L. Harding and Wolfgang Banzhaf",
title = "Hardware Acceleration for {CGP}: Graphics Processing
Units",
booktitle = "Cartesian Genetic Programming",
publisher = "Springer",
editor = "Julian F. Miller",
year = "2011",
series = "Natural Computing Series",
chapter = "8",
pages = "231--253",
keywords = "genetic algorithms, genetic programming, Cartesian
Genetic Programming, GPU",
isbn13 = "978-3-642-17309-7",
URL = "http://www.springer.com/computer/theoretical+computer+science/book/978-3-642-17309-7",
doi = "doi:10.1007/978-3-642-17310-3_8",
abstract = "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.",
notes = "part of \cite{Miller:CGP}",
}
@InProceedings{Hardison:2008:gecco,
author = "Nicholas E. Hardison and Theresa J. Fanelli and Scott
M. Dudek and David M. Reif and Marylyn D. Ritchie and
Alison A. Motsinger-Reif",
title = "A balanced accuracy fitness function leads to robust
analysis using grammatical evolution neural networks in
the case of class imbalance",
booktitle = "GECCO '08: Proceedings of the 10th annual conference
on Genetic and evolutionary computation",
year = "2008",
editor = "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",
isbn13 = "978-1-60558-130-9",
pages = "353--354",
address = "Atlanta, GA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p353.pdf",
doi = "doi:10.1145/1389095.1389159",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "12-16 " # jul,
keywords = "genetic algorithms, genetic programming, gene-gene
interactions, grammatical evolution, neural networks,
single nucleotide polymorphism, Bioinformatics,
computational biology: Poster",
notes = "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 \cite{1389159}",
}
@InProceedings{Hardison:2011:GECCO,
author = "Nicholas E. Hardison and Alison A. Motsinger-Reif",
title = "The power of quantitative grammatical evolution neural
networks to detect gene-gene interactions",
booktitle = "GECCO '11: Proceedings of the 13th annual conference
on Genetic and evolutionary computation",
year = "2011",
editor = "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",
isbn13 = "978-1-4503-0557-0",
pages = "299--306",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, Bioinformatics, computational, systems, and
synthetic biology",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001576.2001618",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{2001618} GECCO-2011 A joint
meeting of the twentieth international conference on
genetic algorithms (ICGA-2011) and the sixteenth annual
genetic programming conference (GP-2011)",
}
@Article{Hardy:2002:IJMPc,
author = "Yorick Hardy and W.-H. Steeb",
title = "Gene Expression Programming and One-dimensional
chaotic maps",
journal = "International Journal of Modern Physics C",
year = "2002",
volume = "13",
number = "1",
pages = "25--30",
month = jan,
keywords = "genetic algorithms, genetic programming, Gene
expression programming, chromosomes, replication,
chaotic maps",
doi = "doi:10.1142/S0129183102002912",
abstract = "Gene expression programming is applied to find
one-dimensional maps. A survey on gene expression
programming is also given.",
notes = "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",
}
@InProceedings{Haridass:2010:SSST,
author = "Sai sri Krishna Haridass and David H. K. Hoe",
title = "Fault tolerant Block Based Neural Networks",
booktitle = "42nd Southeastern Symposium on System Theory (SSST
2010)",
year = "2010",
month = "7-9 " # mar,
pages = "357--361",
address = "University of Texas at Tyler, USA",
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/SSST.2010.5442804",
ISSN = "0094-2898",
notes = "Is this a GP? Also known as \cite{5442804}",
}
@InProceedings{harik:1999:A,
author = "Georges R. Harik and Fernando G. Lobo",
title = "A parameter-less genetic algorithm",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "258--265",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/parameter-less-ga.ps",
URL = "ftp://ftp-illigal.ge.uiuc.edu/pub/papers/Publications/lobo/parameter-less-ga.ps.Z",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{Harman:2007:ICPC,
author = "Mark Harman",
title = "Search Based Software Engineering for Program
Comprehension",
booktitle = "15th International Conference on Program Comprehension
(ICPC 2007)",
year = "2007",
editor = "Kenny Wong",
address = "Banff, Canada",
month = "26-29 " # jun,
publisher = "IEEE",
note = "Invited paper",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.dcs.kcl.ac.uk/staff/mark/icpc07.ps",
notes = "http://www-user.cs.ualberta.ca/conferences/icpc2007/",
}
@InProceedings{harman:2010:Manifesto,
author = "Mark Harman and Yue Jia and William B. Langdon",
title = "A Manifesto for Higher Order Mutation Testing",
booktitle = "Mutation 2010",
year = "2010",
editor = "Lydie {du Bousquet} and Jeremy Bradbury and Gordon
Fraser",
pages = "80--89",
address = "Paris",
month = "6 " # apr,
publisher = "IEEE Computer Society",
note = "Keynote",
keywords = "genetic algorithms, genetic programming, SBSE",
isbn13 = "978-0-7695-4050-4",
URL = "http://www.dcs.kcl.ac.uk/pg/jiayue/publications/papers/HarmanJL10.pdf",
doi = "doi:10.1109/ICSTW.2010.13",
size = "10 pages",
abstract = "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.",
notes = "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 \cite{HarmanJL10}",
}
@Article{Harman:2010:ACM,
author = "Mark Harman",
title = "Automated Patching Techniques: The Fix Is In",
journal = "Communications of the ACM",
volume = "53",
number = "5",
year = "2010",
ISSN = "0001-0782",
pages = "108",
month = jun,
publisher = "ACM",
address = "New York, NY, USA",
keywords = "genetic algorithms, genetic programming, SBSE",
ISSN = "0001-0782",
doi = "doi:10.1145/1735223.1735248",
size = "1 pages",
abstract = "Finding bugs is technically demanding and yet
economically vital. How much more difficult yet
valuable would it be to automatically fix bugs?",
notes = "Technical Perspective. technical perspective. Intro to
\cite{Weimer:2010:ACM} Also known as \cite{1735248}",
}
@Article{Harman:2011:ieeeC,
author = "Mark Harman",
journal = "Computer",
title = "Software Engineering Meets Evolutionary Computation",
year = "2011",
month = oct,
volume = "44",
number = "10",
pages = "31--39",
note = "Cover feature",
keywords = "genetic algorithms, genetic programming, SBSE,
evolutionary computation, realistic algorithm, software
design, software engineering",
ISSN = "0018-9162",
doi = "doi:10.1109/MC.2011.263",
size = "9 pages",
abstract = "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.",
notes = "also known as \cite{6036090}",
}
@InCollection{harmeling:2000:SSPGA,
author = "Stefan Harmeling",
title = "Solving Satisfiability Problems with Genetic
Algorithms",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2000",
year = "2000",
editor = "John R. Koza",
pages = "206--213",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms",
notes = "part of \cite{koza:2000:gagp}",
}
@InProceedings{harper:2005:CEC,
author = "Robin Harper and Alan Blair",
title = "A Structure Preserving Crossover In Grammatical
Evolution",
booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary
Computation",
year = "2005",
editor = "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",
volume = "3",
pages = "2537--2544",
address = "Edinburgh, UK",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "2-5 " # sep,
organisation = "IEEE Computational Intelligence Society, Institution
of Electrical Engineers (IEE), Evolutionary Programming
Society (EPS)",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, Grammatical
Evolution",
ISBN = "0-7803-9363-5",
abstract = "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.",
notes = "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.",
}
@InProceedings{Harper:2006:CECx,
author = "Robin Harper and Alan Blair",
title = "A Self-Selecting Crossover Operator",
booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary
Computation",
year = "2006",
editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and
Simon M. Lucas",
pages = "5569--5576",
address = "Vancouver",
month = "6-21 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-9487-9",
size = "8 pages",
abstract = "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.",
notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
the IEE.
IEEE Catalog Number: 06TH8846D",
}
@InProceedings{Harper_2006_CEC,
author = "Robin Harper and Alan Blair",
title = "Dynamically Defined Functions In Grammatical
Evolution",
booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary
Computation",
year = "2006",
pages = "9188--9195",
address = "Vancouver",
month = "6-21 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, grammars, search problems, Backus Naur form
grammar, arbitrary language, genotype, phenotype",
ISBN = "0-7803-9487-9",
doi = "doi:10.1109/CEC.2006.1688638",
size = "8 pages",
abstract = "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.",
notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
the IEE.
minesweeper Also known as \cite{1688638}. IEEE Xplore
gives pages 2638--2645.",
}
@PhdThesis{Harper:thesis,
author = "Robin Thomas Ross Harper",
title = "Enhancing Grammatical Evolution",
school = "School of Computer Science and Engineering, The
University of New South Wales",
year = "2009",
address = "Sydney 2052, Australia",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
size = "195 pages",
abstract = "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.",
}
@InProceedings{Harper:2010:cec,
author = "Robin Harper",
title = "Genetic Programming -To much {P} and not enough {G}?",
booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-6910-9",
abstract = "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.",
doi = "doi:10.1109/CEC.2010.5586050",
notes = "WCCI 2010. Also known as \cite{5586050}",
}
@InProceedings{Harper:2010:cec2,
author = "Robin Harper",
title = "Spatial co-evolution in Age Layered Planes ({SCALP})",
booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-6910-9",
abstract = "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.",
doi = "doi:10.1109/CEC.2010.5586342",
notes = "WCCI 2010. Also known as \cite{5586342}",
}
@InProceedings{Harper:2010:cec3,
author = "Robin Harper",
title = "{GE}, explosive grammars and the lasting legacy of bad
initialisation",
booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, Grammatical
Evolution",
isbn13 = "978-1-4244-6910-9",
abstract = "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.",
doi = "doi:10.1109/CEC.2010.5586336",
notes = "WCCI 2010. Also known as \cite{5586336}",
}
@InProceedings{Harper:2011:GECCO,
author = "Robin Harper",
title = "Co-evolving robocode tanks",
booktitle = "GECCO '11: Proceedings of the 13th annual conference
on Genetic and evolutionary computation",
year = "2011",
editor = "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",
isbn13 = "978-1-4503-0557-0",
pages = "1443--1450",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001576.2001770",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{2001770} GECCO-2011 A joint
meeting of the twentieth international conference on
genetic algorithms (ICGA-2011) and the sixteenth annual
genetic programming conference (GP-2011)",
}
@InProceedings{Harper:2011:GECCOcomp,
author = "Robin Harper",
title = "Dynamic {L}-systems in {GE}",
booktitle = "GECCO '11: Proceedings of the 13th annual conference
companion on Genetic and evolutionary computation",
year = "2011",
editor = "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",
isbn13 = "978-1-4503-0690-4",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, Generative and developmental systems:
Poster",
pages = "209--210",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001858.2001975",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{2001975} Distributed on CD-ROM at
GECCO-2011.
ACM Order Number 910112.",
}
@InProceedings{harrell:1999:EAPFIWMDP,
author = "Laura J. Harrell and S. Ranji Ranjithan",
title = "Evaluation of Alternative Penalty Function
Implementations in a Watershed Management Design
Problem",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1551--1558",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "real world applications",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-736.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-736.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{Harries:1997:eaossGP,
author = "Kim Harries and Peter Smith",
title = "Exploring Alternative Operators and Search Strategies
in Genetic Programming",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "147--155",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/harries.gp97_paper.ps.gz",
notes = "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.
)",
}
@Misc{harries:1998:cgediass,
author = "K. Harries and P. W. H. Smith",
title = "Code Growth, Explicitly Defined Introns and
Alternative Selection Schemes",
howpublished = "www",
year = "1998",
note = "Earlier version of Evolutionary Computation 6 (4),
336-360, 1998",
keywords = "genetic algorithms, genetic programming, Introns,
Bloat, Parsimony",
URL = "http://www.soi.city.ac.uk/homes/peters/pub/Introns6.ps",
URL = "http://citeseer.ist.psu.edu/harries98code.html",
size = "26 pages",
abstract = "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.",
notes = "Final version is \cite{PWHSmith:1998:cgediass}",
}
@Article{harrigan:2004:TXL,
author = "George G. Harrigan and Roxanne H. LaPlante and Greg N.
Cosma and Gary Cockerell and Royston Goodacre and Jane
F. Maddox and James P. Luyendyk and Patricia E. Ganey
and Robert A. Roth",
title = "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",
journal = "Toxicology Letters",
year = "2004",
volume = "146",
number = "3",
pages = "197--205",
month = "2 " # feb,
keywords = "genetic algorithms, genetic programming, Bacterial
lipopolysaccharide, High-throughput infrared
spectroscopy, Idiosyncratic toxicity, Metabonomics",
doi = "doi:10.1016/j.toxlet.2003.09.011",
abstract = "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.",
notes = "Pharmacia Corporation, GMax-Bio",
}
@InProceedings{1274094,
author = "Kyle Ira Harrington",
title = "Predicting reactions from amino acid sequences in {S}.
cerevisiae: an evolutionary computation approach",
booktitle = "Genetic and Evolutionary Computation Conference
{(GECCO2007)} workshop program",
year = "2007",
month = "7-11 " # jul,
editor = "Tina Yu",
isbn13 = "978-1-59593-698-1",
pages = "2725--2728",
address = "London, United Kingdom",
keywords = "genetic algorithms, genetic programming, GP^2, push,
PushGP",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2725.pdf",
doi = "doi:10.1145/1274000.1274094",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Distributed on CD-ROM at GECCO-2007 ACM Order No.
910071",
}
@Article{Harris:2009:HNO,
author = "Andrew T. Harris and Anxhela Lungari and Christopher
J. Needham and Stephen L. Smith and Michael A. Lones
and Sheila E. Fisher and Xuebin Yang and Nicola Cooper
and Jennifer Kirkham and D. Alastair Smith and Dominic
P. Martin-Hirsch and Alec S. High",
title = "Potential for Raman Spectroscopy to Provide Cancer
Screening Using a Peripheral Blood Sample",
journal = "Head \& Neck Oncology",
year = "2009",
volume = "1",
pages = "34",
month = sep,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.headandneckoncology.org/content/1/1/34",
doi = "doi:10.1186/1758-3284-1-34",
pubmedid = "19761601",
ISSN = "1758-3284",
abstract = "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.",
notes = "Also known as \cite{19761601}",
}
@TechReport{Harris:1996:edgegpRN,
author = "Christopher Harris and Bernard Buxton",
title = "Evolving Edge Detectors",
year = "1996",
institution = "UCL",
type = "Research Note",
number = "RN/96/3",
address = "Gower Street, London, WC1E 6BT, UK",
month = jan,
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/edgegp.ps.gz",
keywords = "genetic algorithms, genetic programming, Edge
Detection",
abstract = "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.",
}
@InProceedings{Harris:1996:edgegp,
author = "Christopher Harris and Bernard Buxton",
title = "Evolving Edge Detectors with Genetic Programming",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "309--315",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp96edge.ps.gz",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
size = "6 pages",
abstract = "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.",
notes = "GP-96",
}
@TechReport{Harris:1996:gpcomRN,
author = "Christopher Harris and Bernard Buxton",
title = "{GP}-{COM}: {A} Distributed, Component-Based Genetic
Programming System in {C}++",
year = "1996",
institution = "UCL",
type = "Research Note",
number = "RN/96/2",
address = "Gower Street, London, WC1E 6BT, UK",
month = jan,
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gpcom.ps",
keywords = "genetic algorithms, genetic programming, Software
System",
abstract = "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.",
}
@InProceedings{Harris:1996:gpcom,
author = "Christopher Harris and Bernard Buxton",
title = "{GP}-{COM}: {A} Distributed, Component-Based Genetic
Programming System in {C}++",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "425",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gp96com.ps.gz",
size = "1 page",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96",
}
@TechReport{Harris:1997:ledGPpsa,
author = "Christopher Harris and Bernard Buxton",
title = "Low-level Edge Detection Using Genetic Programming:
performance, specificity and application to real-world
signals",
year = "1997",
institution = "UCL",
type = "Research Note",
number = "RN/97/7",
address = "Gower Street, London, WC1E 6BT, UK",
URL = "http://citeseer.ist.psu.edu/404512.html",
keywords = "genetic algorithms, genetic programming, Edge
Detection",
notes = "
404512.html PDF link broken 22 Oct 2004",
}
@InProceedings{harris:1997:STGPphtexc,
author = "Christopher Harris",
title = "Strongly Types {GP} to promote hierarchy through
explicit syntax constraints",
booktitle = "Late Breaking Papers at the GP-97 Conference",
year = "1997",
editor = "John Koza",
pages = "72--80",
address = "Stanford, CA, USA",
publisher_address = "Stanford, California, 94305-3079 USA",
month = "13-16 " # jul,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/staff/C.Harris/stgp_structure.ps.gz
broken",
size = "9 pages",
notes = "GP-97LB
It's ms-word postscript, so use pageview to look at it
rather than ghostview, should print fine.",
}
@InProceedings{harris:1997:ehSTGP,
author = "Christopher Harris",
title = "Enforcing Hierarchy on Solutions with Strongly Typed
Genetic Programming",
booktitle = "Late Breaking Papers at the 1997 Genetic Programming
Conference",
year = "1997",
editor = "John R. Koza",
pages = "292",
address = "Stanford University, CA, USA",
publisher_address = "Stanford University, Stanford, California,
94305-3079, USA",
month = "13--16 " # jul,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-206995-8",
notes = "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",
}
@PhdThesis{harris:thesis,
author = "Christopher Harris",
title = "An investigation into the Application of Genetic
Programming techniques to Signal Analysis and Feature
Detection",
school = "University College, London",
year = "1997",
month = "26 " # sep,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/c.harris/thesisps.zip",
size = "186 pages",
}
@Article{Harris:2003:IJRBM,
author = "E. L. Harris and V. Babovic and R. A. Falconer",
title = "Velocity predictions in compound channels with
vegetated floodplains using genetic programming",
journal = "International Journal of River Basin Management",
year = "2003",
volume = "1",
number = "2",
pages = "117--123",
keywords = "genetic algorithms, genetic programming, Evolutionary
computation, hydrodynamic processes, floodplain
vegetation",
ISSN = "1571-5124",
doi = "doi:10.1080/15715124.2003.9635198",
size = "7 pages",
abstract = "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.",
notes = "PhD 2003 Environmental Hydroinformatics Tools for
Water Quality Management",
}
@InProceedings{harris:1999:PIWRUGA,
author = "S. D. Harris and R. Mustata and L. Elliott and D. B.
Ingham and D. Lesnic",
title = "Parameter Identification Within Rocks Using Genetic
Algorithms",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1779",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "real world applications, poster papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-758_2.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-758_2.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{harris:1999:TRCRRUGA,
author = "S. D. Harris and L. Elliott and D. B. Ingham and M.
Pourkashanian and C. W. Wilson",
title = "The Retrieval of Chemical Reaction Rates Using Genetic
Algorithms",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1780",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "real world applications, poster papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-759_2.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-759_2.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InCollection{harris:2000:GPFF,
author = "Sarah Harris",
title = "Genetically-Learned 7-Input Parity Function by an 8 x
8 {FPGA}",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2000",
year = "2000",
editor = "John R. Koza",
pages = "214--220",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms",
notes = "part of \cite{koza:2000:gagp}",
}
@InProceedings{Harris:2011:PLDI,
author = "William R. Harris and Sumit Gulwani",
title = "Spreadsheet table transformations from examples",
booktitle = "Proceedings of the 32nd ACM SIGPLAN conference on
Programming language design and implementation,
PLDI'11",
year = "2011",
pages = "317--328",
address = "San Jose, California, USA",
publisher_address = "New York, NY, USA",
acmid = "1993536",
publisher = "ACM",
keywords = "genetic algorithms, genetic programming, end-user
programming, program synthesis, programming by example,
spreadsheet programming, table manipulation, user
intent",
isbn13 = "978-1-4503-0663-8",
doi = "doi:10.1145/1993498.1993536",
size = "12 pages",
notes = "Also known as \cite{Harris:2011:STT:1993498.1993536}",
}
@Article{Harris:2011:SIGPlan,
author = "William R. Harris and Sumit Gulwani",
title = "Spreadsheet table transformations from examples",
journal = "ACM SIGPLAN Notices",
volume = "46",
issue = "6",
month = jun,
year = "2011",
pages = "317--328",
keywords = "genetic algorithms, genetic programming, end-user
programming, program synthesis, programming by example,
spreadsheet programming, table manipulation, user
intent",
ISSN = "0362-1340",
doi = "doi:10.1145/1993316.1993536",
size = "12 pages",
acmid = "1993536",
publisher = "ACM",
abstract = "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.",
notes = "As \cite{Harris:2011:PLDI} ? Also known as
\cite{Harris:2011:STT:1993316.1993536}",
}
@InProceedings{1274068,
author = "Gregory Anthony Harrison and Eric W. Worden",
title = "Genetically programmed learning classifier system
description and results",
booktitle = "Genetic and Evolutionary Computation Conference
{(GECCO2007)} workshop program",
year = "2007",
month = "7-11 " # jul,
editor = "Tina Yu",
isbn13 = "978-1-59593-698-1",
pages = "2729--2736",
address = "London, United Kingdom",
keywords = "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",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2729.pdf",
doi = "doi:10.1145/1274000.1274068",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Distributed on CD-ROM at GECCO-2007 ACM Order No.
910071",
}
@InProceedings{harrison:2004:eurogp,
author = "Michael L. Harrison and James A. Foster",
title = "Co-evolving Faults to Improve the Fault Tolerance of
Sorting Networks",
booktitle = "Genetic Programming 7th European Conference, EuroGP
2004, Proceedings",
year = "2004",
editor = "Maarten Keijzer and Una-May O'Reilly and Simon M.
Lucas and Ernesto Costa and Terence Soule",
volume = "3003",
series = "LNCS",
pages = "57--66",
address = "Coimbra, Portugal",
publisher_address = "Berlin",
month = "5-7 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-21346-5",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=57",
abstract = "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.",
notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in
conjunction with EvoCOP2004 and EvoWorkshops2004",
}
@InProceedings{hart:1999:AISASCE,
author = "Emma Hart and Peter Ross",
title = "An Immune System Approach to Scheduling in Changing
Environments",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1559--1566",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "real world applications",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-723.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-723.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@Article{hart:2003:GPEM,
author = "Emma Hart and Peter Ross",
title = "Exploiting the Analogy between the Immune System and
Sparse Distributed Memories",
journal = "Genetic Programming and Evolvable Machines",
year = "2003",
volume = "4",
number = "4",
pages = "333--358",
month = dec,
keywords = "artificial immune systems, sparse distributed memory,
data-clustering",
ISSN = "1389-2576",
doi = "doi:10.1023/A:1026191011609",
abstract = "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.",
notes = "Special issue on artificial immune systems Article ID:
5144847",
}
@Article{hart:2005:GPEM,
author = "Emma Hart and Peter Ross and David Corne",
title = "Evolutionary Scheduling: {A} Review",
journal = "Genetic Programming and Evolvable Machines",
year = "2005",
volume = "6",
number = "2",
pages = "191--220",
month = jun,
note = "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",
}
@InProceedings{hart:1999:CEPEPSAADDA,
author = "William E. Hart",
title = "Comparing Evolutionary Programs and Evolutionary
Pattern Search Algorithms: {A} Drug Docking
Application",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "855--862",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "evolution strategies and evolutionary programming",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/Har99-gecco.ps.gz",
URL = "ftp://ftp.cs.sandia.gov/pub/papers/wehart/1999/Har99-gecco.ps.gz",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InCollection{hart:1995:TAGPCMPE,
author = "Jonathan Joseph Hart",
title = "The Application of Genetic Programming to Cooperative
Movement Planning and Execution",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1995",
year = "1995",
editor = "John R. Koza",
pages = "86--95",
address = "Stanford, California, 94305-3079 USA",
month = "11 " # dec,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-195720-5",
notes = "part of \cite{koza:1995:gagp}",
}
@InProceedings{hart:2002:gecco:lbp,
title = "Evolving Software with Multiple Outputs and Multiple
Populations",
author = "John Hart and Martin Shepperd",
booktitle = "Late Breaking Papers at the Genetic and Evolutionary
Computation Conference ({GECCO-2002})",
editor = "Erick Cant{\'u}-Paz",
year = "2002",
month = jul,
pages = "223--227",
address = "New York, NY",
publisher = "AAAI",
publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
keywords = "genetic algorithms, genetic programming",
notes = "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 \cite{hart:2002:TR02-06}",
}
@TechReport{hart:2002:TR02-06,
author = "John Hart and Martin Shepperd",
title = "Evolving Software with Multiple Outputs and Multiple
Populations",
institution = "School of Design, Engineering and Computing,
Bournemouth University",
year = "2002",
number = "TR02-06",
address = "Royal London House, Christchurch Rd, Bournemouth, BH1
3LT, UK",
month = jul,
keywords = "genetic algorithms, genetic programming, evolutionary
algorithms, search, embedded system",
URL = "http://dec.bournemouth.ac.uk/ESERG/Technical_Reports/TR02-06/TR02-06.pdf",
abstract = "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.",
notes = "as \cite{hart:2002:gecco:lbp}",
size = "pages",
}
@PhdThesis{hart:thesis,
author = "John K. Hart",
title = "Automatic control program creation using concurrent
Evolutionary Computing",
school = "Bournemouth University",
year = "2004",
address = "UK",
month = jan,
keywords = "genetic algorithms, genetic programming",
notes = "related publications \cite{hart:2002:gecco:lbp}
\cite{hart:2004:eurogp}",
}
@TechReport{hart:2004:eurogpTR,
author = "John Hart and Martin Shepperd",
title = "The Evolution of Concurrent Control Software Using
Genetic Programming",
institution = "Empirical Software Engineering Research Group School
of Design, Engineering \& Computing, Bournemouth
University",
year = "2003",
number = "TR03-08",
address = "Royal London House, Christchurch Rd, Bournemouth, BH1
3LT, UK",
keywords = "genetic algorithms, genetic programming, linear
genetic programming, embedded software",
URL = "http://dec.bournemouth.ac.uk/ESERG/Technical_Reports/TR03-08/TR03-08.pdf",
abstract = "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.",
notes = "See also \cite{hart:2004:eurogp}",
size = "10 pages",
}
@InProceedings{hart:2004:eurogp,
author = "John Hart and Martin Shepperd",
title = "The Evolution of Concurrent Control Software Using
Genetic Programming",
booktitle = "Genetic Programming 7th European Conference, EuroGP
2004, Proceedings",
year = "2004",
editor = "Maarten Keijzer and Una-May O'Reilly and Simon M.
Lucas and Ernesto Costa and Terence Soule",
volume = "3003",
series = "LNCS",
pages = "289--298",
address = "Coimbra, Portugal",
publisher_address = "Berlin",
month = "5-7 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-21346-5",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=289",
abstract = "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.",
notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in
conjunction with EvoCOP2004 and EvoWorkshops2004
See also \cite{hart:2004:eurogpTR}",
}
@InProceedings{hartley:1999:A,
author = "Adrian R. Hartley",
title = "Accuracy-based fitness allows similar performance to
humans in static and dynamic classification
environments",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "266--273",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/Hartley1999a.ps.gz",
URL = "http://www.cs.bris.ac.uk/~kovacs/lcs.archive/Hartley1999a.ps.gz",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{Hartmann:2002:gecco,
author = "Morten Hartmann and Frode Eskelund and Pauline C.
Haddow and Julian F. Miller",
title = "Evolving Fault Tolerance On An Unreliable Technology
Platform",
booktitle = "GECCO 2002: Proceedings of the Genetic and
Evolutionary Computation Conference",
editor = "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",
year = "2002",
pages = "171--177",
address = "New York",
publisher_address = "San Francisco, CA 94104, USA",
month = "9-13 " # jul,
publisher = "Morgan Kaufmann Publishers",
keywords = "evolvable hardware, digital circuits, fault tolerance,
noise robustness",
ISBN = "1-55860-878-8",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/EH275.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/EH275.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-04.pdf",
notes = "GECCO-2002. A joint meeting of the eleventh
International Conference on Genetic Algorithms
(ICGA-2002) and the seventh Annual Genetic Programming
Conference (GP-2002)",
}
@InProceedings{harvey:1998:bcGP,
author = "Brad Harvey and James A. Foster and Deborah Frincke",
title = "Byte Code Genetic Programming",
booktitle = "Late Breaking Papers at the Genetic Programming 1998
Conference",
year = "1998",
editor = "John R. Koza",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "Stanford, CA, USA",
month = "22-25 " # jul,
publisher = "Stanford University Bookstore",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.csds.uidaho.edu/deb/jvm.pdf",
URL = "http://citeseer.ist.psu.edu/547985.html",
size = "5 pages",
abstract = "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.",
notes = "GP-98LB",
}
@Unpublished{harvey:1997:ob,
author = "Inman Harvey",
title = "Open the Box",
note = "Position paper at the Workshop on Evolutionary
Computation with Variable Size Representation at
ICGA-97",
month = "20 " # jul,
year = "1997",
address = "East Lansing, MI, USA",
keywords = "genetic algorithms, variable size representation,
SAGA",
size = "4 pages",
}
@InProceedings{harvey:1999:TBCGP,
author = "Brad Harvey and James Foster and Deborah Frincke",
title = "Towards Byte Code Genetic Programming",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1234",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, poster
papers",
ISBN = "1-55860-611-4",
URL = "http://citeseer.ist.psu.edu/468509.html",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-418.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-418.ps",
abstract = "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.",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{harvey:1999:TOMSMFSRPGA,
author = "K. Burton Harvey and Chrisila C. Pettey",
title = "The Outlaw Method for Solving Multimodal Functions
with Split Ring Parallel Genetic Algorithms",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "274--280",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-382.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-382.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{conf/evoW/HarveyPBTPYVSB00,
title = "Finding Golf Courses: The Ultra High Tech Approach",
author = "Neal R. Harvey and Simon Perkins and Steven P. Brumby
and James Theiler and Reid B. Porter and A. Cody Young
and Anil K. Varghese and John J. Szymanski and Jeffrey
J. Bloch",
booktitle = "Real-World Applications of Evolutionary Computing",
year = "2000",
editor = "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",
volume = "1803",
series = "LNCS",
pages = "54--64",
address = "Edinburgh",
publisher_address = "Berlin",
month = "17 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-67353-9",
bibdate = "2002-01-03",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/evoW/evoW2000.html#HarveyPBTPYVSB00",
URL = "http://www.genie.lanl.gov/green/publications/harveyEvoIASP2000.pdf",
size = "12 pages",
abstract = "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.",
notes = "EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel,
EvoSTIM, EvoRob, and EvoFlight, Edinburgh, Scotland,
UK, April 17, 2000 Proceedings",
}
@InProceedings{Harvey:2000:SPIE,
author = "N. R. Harvey and S. P. Brumby and S. J. Perkins and R.
B. Porter and J. Theiler and A. C. Young and J. J.
Szymanski and J. J. Bloch",
title = "Parallel evolution of image processing tools for
multispectral imagery",
booktitle = "Imaging Spectrometry VI, Procceedings of SPIE",
year = "2000",
editor = "Michael R. Descour and Sylvia S. Shen",
volume = "4132",
pages = "72--82",
keywords = "genetic algorithms, genetic programming, GENIE,
ALADDIN",
URL = "http://public.lanl.gov/jt/Papers/harveySPIE4132.ps.gz",
size = "11 pages",
abstract = "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...",
}
@Article{oai:CiteSeerPSU:561309,
author = "Neal R. Harvey and James Theiler and Steven P. Brumby
and Simon Perkins and John J. Szymanski and Jeffrey J.
Bloch and Reid B. Porter and Mark Galassi and A. Cody
Young",
title = "Comparison of {GENIE} and conventional supervised
classifiers for multispectral image feature
extraction",
journal = "IEEE Transactions on Geoscience and Remote Sensing",
year = "2002",
volume = "40",
number = "2",
pages = "393--404",
month = feb,
keywords = "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",
ISSN = "0196-2892",
URL = "http://nis-www.lanl.gov/~simes/webdocs/harveyIEEE_TGARS2001.pdf",
URL = "http://citeseer.ist.psu.edu/561309.html",
size = "12 pages",
abstract = "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.",
notes = "On line version not identical to IEEE version
Inspec Accession Number: 7265352, CODEN: IGRSD2",
}
@InProceedings{Harwerth:2011:EuroGP,
author = "Michael Harwerth",
title = "Experiments on Islands",
booktitle = "Proceedings of the 14th European Conference on Genetic
Programming, EuroGP 2011",
year = "2011",
month = "27-29 " # apr,
editor = "Sara Silva and James A. Foster and Miguel Nicolau and
Mario Giacobini and Penousal Machado",
series = "LNCS",
volume = "6621",
publisher = "Springer Verlag",
address = "Turin, Italy",
pages = "239--249",
organisation = "EvoStar",
keywords = "genetic algorithms, genetic programming: poster",
isbn13 = "978-3-642-20406-7",
doi = "doi:10.1007/978-3-642-20407-4_21",
abstract = "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.",
notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in
conjunction with EvoCOP2011 EvoBIO2011 and
EvoApplications2011",
}
@Article{Hasan:2006:PLoS,
author = "Samiul Hasan and Sabine Daugelat and P. S. Srinivasa
Rao and Mark Schreiber",
title = "Prioritizing Genomic Drug Targets in Pathogens:
Application to Mycobacterium tuberculosis",
journal = "PLoS Computational Biology",
year = "2006",
volume = "2",
number = "6",
pages = "e61",
month = jun,
keywords = "genetic algorithms",
URL = "http://compbiol.plosjournals.org/archive/1553-7358/2/6/pdf/10.1371_journal.pcbi.0020061-L.pdf",
doi = "doi:10.1371/journal.pcbi.0020061",
size = "12 pages",
abstract = "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.",
}
@InProceedings{Hasegawa:1997:mg2br,
author = "Yasuhisa Hasegawa and Toshio Fukuda",
title = "Motion Generation of Two-link Brachiation Robot",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "Artifical life and evolutionary robotics",
pages = "407--412",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
notes = "GP-97",
}
@InProceedings{Hasegawa:2006:CEC,
author = "Yoshihiko Hasegawa and Hitoshi Iba",
title = "Optimizing Programs with Estimation of {Bayesian}
Network",
booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary
Computation",
year = "2006",
editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and
Simon M. Lucas",
pages = "5527--5534",
address = "Vancouver",
month = "6-21 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-9487-9",
size = "8 pages",
abstract = "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",
notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
the IEE.
IEEE Catalog Number: 06TH8846D",
}
@InProceedings{Hasegawa:2006:ASPGP,
title = "Estimation of {Bayesian} network for program
generation",
author = "Yoshihiko Hasegawa and Hitoshi Iba",
booktitle = "Proceedings of the Third Asian-Pacific workshop on
Genetic Programming",
year = "2006",
editor = "The Long Pham and Hai Khoi Le and Xuan Hoai Nguyen",
pages = "35--46",
ISSN = "18590209",
address = "Military Technical Academy, Hanoi, VietNam",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.iba.k.u-tokyo.ac.jp/~hasegawa/hasegawa_aspgp2006.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/aspgp06/hasegawa.pdf",
size = "12 pages",
abstract = "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.",
notes = "http://www.aspgp.org",
}
@InProceedings{Hasegawa:2007:cec,
author = "Yoshihiko Hasegawa and Hitoshi Iba",
title = "Estimation of Distribution Algorithm Based on
Probabilistic Grammar with Latent Annotations",
booktitle = "2007 IEEE Congress on Evolutionary Computation",
year = "2007",
editor = "Dipti Srinivasan and Lipo Wang",
pages = "1043--1050",
address = "Singapore",
month = "25-28 " # sep,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "1-4244-1340-0",
file = "1692.pdf",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and
the IET.
IEEE Catalog Number: 07TH8963C",
}
@Article{Hasegawa:2008:TEC,
title = "A {Bayesian} Network Approach to Program Generation",
author = "Yoshihiko Hasegawa and Hitoshi Iba",
journal = "IEEE Transactions on Evolutionary Computation",
year = "2008",
month = dec,
volume = "12",
number = "6",
pages = "750--764",
keywords = "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",
ISSN = "1089-778X",
doi = "doi:10.1109/TEVC.2008.915999",
size = "15 pages",
abstract = "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.",
notes = "POLE, EPT, Kullback-Leibler. Max problem
\cite{langdon:1997:MAX}. DMAX deceptive max problem.
Royal tree problem.
Also known as \cite{4470578}",
}
@Article{Hasegawa:2009:ieeeTEC,
title = "Latent Variable Model for Estimation of Distribution
Algorithm Based on a Probabilistic Context-Free
Grammar",
author = "Yoshihiko Hasegawa and Hitoshi Iba",
journal = "IEEE Transactions on Evolutionary Computation",
year = "2009",
month = aug,
volume = "13",
number = "4",
pages = "858--878",
keywords = "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",
doi = "doi:10.1109/TEVC.2009.2015574",
ISSN = "1089-778X",
}
size = {21 pages},
abstract = {Estimation of distribution algorithms are evolutionary
algorithms using probabilistic techniques instead of traditional
genetic operators. Recently, the application of probabilistic
techniques to program and function evolution has received increasing
attention, and this approach promises to provide a strong
alternative to the traditional genetic programming
techniques. Although a probabilistic context-free grammar (PCFG) is
a widely used model for probabilistic program evolution, a
conventional PCFG is not suitable for estimating interactions among
nodes because of the context freedom assumption. In this paper, we
have proposed a new evolutionary algorithm named programming with
annotated grammar estimation based on a PCFG with latent
annotations, which allows this context freedom assumption to be
weakened. By applying the proposed algorithm to several
computational problems, it is demonstrated that our approach is
markedly more effective at estimating building blocks than prior
approaches.},
notes = {PAGE.
Royal tree, DMAX complex arithmetic
Also known as \cite{5175364}},
)
@InProceedings{Hassan:2008:gecco,
author = "Ghada Hassan and Christopher D. Clack",
title = "Multiobjective robustness for portfolio optimization
in volatile environments",
booktitle = "GECCO '08: Proceedings of the 10th annual conference
on Genetic and evolutionary computation",
year = "2008",
editor = "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",
isbn13 = "978-1-60558-130-9",
pages = "1507--1514",
address = "Atlanta, GA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1507.pdf",
doi = "doi:10.1145/1389095.1389387",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "12-16 " # jul,
keywords = "genetic algorithms, genetic programming, dynamic
environment, finance, multiobjective optimisation,
portfolio optimisation, robustness, Real-World
application",
notes = "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 \cite{1389387}",
}
@InProceedings{Hassan:2008:geccocomp,
author = "Ghada Hassan",
title = "Non-linear factor model for asset selection using
multi objective genetic programming",
year = "2008",
editor = "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",
isbn13 = "978-1-60558-131-6",
booktitle = "GECCO-2008 Workshop: Advanced Research Challenges in
Financial Evolutionary Computing (ARC-FEC)",
pages = "1859--1862",
address = "Atlanta, GA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1859.pdf",
doi = "doi:10.1145/1388969.1388990",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "12-16 " # jul,
keywords = "genetic algorithms, genetic programming, Factor
models, finance, multiobjective optimisation, portfolio
optimisation",
notes = "Distributed on CD-ROM at GECCO-2008
ACM Order Number 910081. Also known as \cite{1388990}",
}
@InProceedings{DBLP:conf/gecco/HassanC09,
author = "Ghada Hassan and Christopher D. Clack",
title = "Robustness of multiple objective {GP} stock-picking in
unstable financial markets: real-world applications
track",
booktitle = "GECCO '09: Proceedings of the 11th Annual conference
on Genetic and evolutionary computation",
year = "2009",
editor = "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",
pages = "1513--1520",
address = "Montreal",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "8-12 " # jul,
organisation = "SigEvo",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-60558-325-9",
bibsource = "DBLP, http://dblp.uni-trier.de",
doi = "doi:10.1145/1569901.1570104",
abstract = "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.",
notes = "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.",
}
@PhdThesis{Hassan:thesis,
title = "Multiobjective genetic programming for financial
portfolio management in dynamic environments",
author = "Ghada Nasr Aly Hassan",
school = "University College London",
year = "2010",
bibsource = "OAI-PMH server at eprints.ucl.ac.uk",
language = "eng",
oai = "oai:eprints.ucl.ac.uk.OAI2:20456",
type = "Doctoral",
address = "UK",
keywords = "genetic algorithms, genetic programming",
URL = "http://discovery.ucl.ac.uk/20456/1/20456.pdf",
URL = "http://eprints.ucl.ac.uk/20456/",
size = "160 pages",
abstract = "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.",
}
@Article{journals/isca/Hassan10,
author = "Yasser Fouad Hassan",
title = "Rough Set Genetic Programming",
journal = "International Journal of Computers and Their
Applications",
volume = "17",
number = "3",
year = "2010",
pages = "161--171",
bibsource = "DBLP, http://dblp.uni-trier.de",
keywords = "genetic algorithms, genetic programming",
ISSN = "1076-5204",
URL = "http://www.isca-hq.org/j-list.htm",
}
@InProceedings{hatanaka:2001:hmimbgp,
author = "Toshiharu Hatanaka and Katsuji Uosaki",
title = "Hammerstein Model Identification Method Based on
Genetic Programming",
booktitle = "Proceedings of the 2001 Congress on Evolutionary
Computation CEC2001",
year = "2001",
pages = "1430--1435",
address = "COEX, World Trade Center, 159 Samseong-dong,
Gangnam-gu, Seoul, Korea",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "27-30 " # may,
organisation = "IEEE Neural Network Council (NNC), Evolutionary
Programming Society (EPS), Institution of Electrical
Engineers (IEE)",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, System
identification, Hammerstein models, Nonlinear systems,
Evolutionary computation",
ISBN = "0-7803-6658-1",
notes = "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",
}
@InProceedings{hatta:1998:appiGA,
author = "Koichi Hatta and Shin'ichi Wakabayashi and Tetsushi
Koide",
title = "Adapting Parameters Based on Pedigree of Individuals
in a Genetic Algorithm",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "510--517",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms",
notes = "SGA-98",
}
@InCollection{haugh:2002:ELCDGP,
author = "Justin C. Haugh",
title = "Evolution of Life Cycle Differentiation using Genetic
Programming",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2002",
year = "2002",
editor = "John R. Koza",
pages = "102--110",
address = "Stanford, California, 94305-3079 USA",
month = jun,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.genetic-programming.org/sp2002/Haugh.pdf",
oai = "oai:CiteSeerXPSU:10.1.1.140.6874",
abstract = "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",
notes = "part of \cite{koza:2002:gagp} 10 by 10 world. Snakes
and mice. lilgp problem -> gpc++ 0.40",
}
@InProceedings{eurogp:HauptmanS05,
author = "Ami Hauptman and Moshe Sipper",
editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre
Collet and Jano I. {van Hemert} and Marco Tomassini",
title = "{GP}-EndChess: Using Genetic Programming to Evolve
Chess Endgame Players",
booktitle = "Proceedings of the 8th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3447",
year = "2005",
address = "Lausanne, Switzerland",
month = "30 " # mar # " - 1 " # apr,
organisation = "EvoNet",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-25436-6",
pages = "120--131",
URL = "http://www.cs.bgu.ac.il/~sipper/papabs/eurogpchess-final.pdf",
URL = "http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=120",
doi = "doi:10.1007/b107383",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in
conjunction with EvoCOP2005 and EvoWorkshops2005",
}
@InProceedings{Hauptman:gecco05lbp,
author = "Ami Hauptman and Moshe Sipper",
title = "Analyzing the Intelligence of a Genetically Programmed
Chess Player",
booktitle = "Late breaking paper at Genetic and Evolutionary
Computation Conference {(GECCO'2005)}",
year = "2005",
month = "25-29 " # jun,
editor = "Franz Rothlauf",
address = "Washington, D.C., USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005lbp/papers/21-hauptmann.pdf",
keywords = "genetic algorithms, genetic programming",
abstract = "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",
notes = "Distributed on CD-ROM at GECCO-2005",
}
@InProceedings{eurogp07:hauptman,
author = "Ami Hauptman and Moshe Sipper",
title = "Evolution of an Efficient Search Algorithm for the
Mate-In-{N} Problem in Chess",
editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and
Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar",
booktitle = "Proceedings of the 10th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "4445",
year = "2007",
address = "Valencia, Spain",
month = "11-13 " # apr,
pages = "78--89",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-71602-5",
isbn13 = "978-3-540-71602-0",
doi = "doi:10.1007/978-3-540-71605-1_8",
abstract = "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.",
notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in
conjunction with EvoCOP2007, EvoBIO2007 and
EvoWorkshops2007",
}
@InProceedings{DBLP:conf/gecco/HauptmanESK09,
author = "Ami Hauptman and Achiya Elyasaf and Moshe Sipper and
Assaf Karmon",
title = "{GP}-rush: using genetic programming to evolve solvers
for the rush hour puzzle",
booktitle = "GECCO '09: Proceedings of the 11th Annual conference
on Genetic and evolutionary computation",
year = "2009",
editor = "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",
pages = "955--962",
address = "Montreal",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "8-12 " # jul,
organisation = "SigEvo",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-60558-325-9",
bibsource = "DBLP, http://dblp.uni-trier.de",
doi = "doi:10.1145/1569901.1570032",
abstract = "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.",
notes = "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.",
}
@MastersThesis{haynes:1994:masters,
author = "Thomas D. Haynes",
title = "A Simulation of Adaptive Agents in a Hostile
Environment",
school = "University of Tulsa",
year = "1994",
address = "Tulsa, OK, USA",
month = apr,
keywords = "genetic algorithms, genetic programming",
broken = "http://euler.mcs.utulsa.edu/~haynes/masters.ps",
URL = "http://citeseer.ist.psu.edu/2240.html",
size = "254 pages",
abstract = "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.",
notes = "
",
}
@TechReport{Hayes:1994:ecs,
author = "Thomas Haynes and Roger Wainwright and Sandip Sen",
title = "Evolving Cooperation Strategies",
institution = "The University of Tulsa",
year = "1994",
type = "Technical Report",
number = "UTULSA-MCS-94-10",
address = "Tulsa, OK, USA",
month = "16 " # dec,
keywords = "genetic algorithms, genetic programming, ccoperation
strategies",
URL = "http://www.mcs.utulsa.edu/~rogerw/papers/Haynes-icmas95.pdf",
abstract = "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.",
size = "9 pages",
notes = "
",
}
@InProceedings{Hayes:1995:agents,
author = "Thomas D. Haynes and Roger L. Wainwright",
title = "A Simulation of Adaptive Agents in Hostile
Environment",
booktitle = "Proceedings of the 1995 ACM Symposium on Applied
Computing",
year = "1995",
editor = "K. M. George and Janice H. Carroll and Ed Deaton and
Dave Oppenheim and Jim Hightower",
pages = "318--323",
address = "Nashville, USA",
publisher = "ACM Press",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.mcs.utulsa.edu/~rogerw/papers/Haynes-sac95.ps",
URL = "http://citeseer.ist.psu.edu/2240.html",
doi = "doi:10.1145/315891.316007",
size = "8 pages",
abstract = "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.",
notes = "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{"}.",
}
@InProceedings{Hayes:1995:ecsICMAS,
author = "Thomas D. Haynes and Roger L. Wainwright and Sandip
Sen",
title = "Evolving Cooperating Strategies",
booktitle = "Proceedings of the first International Conference on
Multiple Agent Systems",
year = "1995",
editor = "Victor Lesser",
pages = "450",
address = "San Francisco, USA",
month = "12--14 " # jun,
publisher = "AAAI Press/MIT Press",
note = "Poster",
keywords = "genetic algorithms, genetic programming, evolutionary
computation, cooperation strategies, poster",
ISBN = "0-262-62102-9",
URL = "http://www.mcs.utulsa.edu/~rogerw/papers/Haynes-icmas95.pdf",
size = "1 page",
abstract = "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.",
notes = "13 page version available via url
",
}
@InProceedings{Hayes:1995,
author = "Thomas Haynes and Roger Wainwright and Sandip Sen and
Dale Schoenefeld",
title = "Strongly typed genetic programming in evolving
cooperation strategies",
booktitle = "Genetic Algorithms: Proceedings of the Sixth
International Conference (ICGA95)",
year = "1995",
editor = "Larry J. Eshelman",
pages = "271--278",
address = "Pittsburgh, PA, USA",
publisher_address = "San Francisco, CA, USA",
month = "15-19 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-370-0",
URL = "http://www.mcs.utulsa.edu/~rogerw/papers/Haynes-icga95.pdf",
abstract = "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.",
notes = "Our printers barf at graph on page 8.
",
}
@InProceedings{Hayes:1995:ebspp,
author = "Thomas Haynes and Sandip Sen",
title = "Evolving Behavioral Strategies in Predators and Prey",
booktitle = "IJCAI-95 Workshop on Adaptation and Learning in
Multiagent Systems",
year = "1995",
editor = "Sandip Sen",
pages = "32--37",
address = "Montreal, Quebec, Canada",
publisher_address = "San Francisco, CA, USA",
month = "20-25 " # aug,
organisation = "IJCAII,AAAI,CSCSI",
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, cooperation
strategies",
broken = "http://euler.mcs.utulsa.edu/~haynes/icjai95.ps",
URL = "http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/734/http:zSzzSzwww.cs.twsu.eduzSz~hayneszSzicjai95.pdf/haynes96evolving.pdf",
URL = "http://citeseer.ist.psu.edu/haynes96evolving.html",
abstract = "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.",
notes = "see also \cite{Haynes:1996:EBS}
",
}
@InProceedings{Haynes95:Team,
author = "Thomas Haynes and Sandip Sen and Dale Schoenefeld and
Roger Wainwright",
title = "Evolving a Team",
booktitle = "Working Notes for the AAAI Symposium on Genetic
Programming",
year = "1995",
editor = "E. V. Siegel and J. R. Koza",
pages = "23--30",
address = "MIT, Cambridge, MA, USA",
publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA",
month = "10--12 " # nov,
publisher = "AAAI",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.aaai.org/Papers/Symposia/Fall/1995/FS-95-01/FS95-01-004.pdf",
URL = "http://www.mcs.utulsa.edu/~rogerw/papers/Haynes-team.pdf",
URL = "http://www.aaai.org/Library/Symposia/Fall/fs95-01.php#23",
size = "8 pages",
abstract = "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.",
notes = "AAAI-95f GP. Part of \cite{siegel:1995:aaai-fgp}
{\em Telephone:} 415-328-3123 {\em Fax:} 415-321-4457
{\em email} info@aaai.org {\em URL:}
http://www.aaai.org/",
}
@InCollection{Haynes95:Prey,
author = "Thomas Haynes and Sandip Sen",
title = "Evolving Behavioral Strategies in Predators and Prey",
booktitle = "Adaptation and Learning in Multiagent Systems",
publisher = "Springer Verlag",
year = "1995",
editor = "Gerhard Wei{\ss} and Sandip Sen",
volume = "1042",
series = "Lecture Notes in Artificial Intelligence",
pages = "113--126",
address = "Berlin, Germany",
keywords = "genetic algorithms, genetic programming, STGP",
isbn13 = "978-3-540-60923-0",
doi = "doi:10.1007/3-540-60923-7_22",
abstract = "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.",
size = "14 pages",
notes = "Published in 1996?
",
affiliation = "The University of Tulsa Department of Mathematical &
Computer Sciences USA USA",
}
@TechReport{Haynes:1995:EMC,
author = "Thomas Haynes and Sandip Sen and Dale Schoenefeld and
Roger Wainwright",
title = "Evolving Multiagent Coordination Strategies with
Genetic Programming",
number = "UTULSA-MCS-95-04",
institution = "The University of Tulsa",
year = "1995",
month = may # " 31,",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.mcs.utulsa.edu/~rogerw/papers/Haynes-jp.pdf",
URL = "http://citeseer.ist.psu.edu/26626.html",
abstract = "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.",
notes = "
",
}
@InCollection{haynes:1996:aigp2,
author = "Thomas D. Haynes and Dale A. Schoenefeld and Roger L.
Wainwright",
title = "Type Inheritance in Strongly Typed Genetic
Programming",
booktitle = "Advances in Genetic Programming 2",
publisher = "MIT Press",
year = "1996",
editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}",
pages = "359--376",
chapter = "18",
address = "Cambridge, MA, USA",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-262-01158-1",
URL = "http://www.mcs.utulsa.edu/~rogerw/papers/Haynes-hier.pdf",
URL = "http://cisnet.mit.edu/Advances-in-Genetic-Programming/376",
abstract = "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.",
}
@InProceedings{haynes:1996:esr,
author = "Thomas Haynes and Rose Gamble and Leslie Knight and
Roger Wainwright",
title = "Entailment for Specification Refinement",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "90--97",
address = "Stanford University, CA, USA",
publisher_address = "Cambridge, MA, USA",
publisher = "MIT Press",
URL = "http://www.mcs.utulsa.edu/~rogerw/papers/Haynes-theorem.pdf",
size = "9 pages",
abstract = "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.",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96",
}
@TechReport{Haynes:1995:CDG,
author = "Thomas Haynes",
title = "Clique Detection via Genetic Programming",
number = "UTULSA-MCS-95-02",
institution = "The University of Tulsa",
year = "1995",
month = apr # " 24,",
keywords = "genetic algorithms, genetic programming",
broken = "http://euler.mcs.utulsa.edu/~haynes/tr_clique.ps",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/2785/http:zSzzSzeuler.mcs.utulsa.eduzSz~hayneszSztr_clique.pdf/haynes95clique.pdf",
URL = "http://citeseer.ist.psu.edu/135522.html",
abstract = "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.",
}
@TechReport{Haynes:1996:CDGb,
author = "Thomas Haynes and Dale Schoenefeld",
title = "Clique Detection via Genetic Programming",
number = "UTULSA-MCS-96-05",
institution = "The University of Tulsa",
month = mar # " 15,",
notes = "Full version of GP'96 poster",
year = "1996",
keywords = "genetic algorithms, genetic programming",
broken = "http://euler.mcs.utulsa.edu/~haynes/clique.ps",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/4146/http:zSzzSzeuler.mcs.utulsa.eduzSz~hayneszSzclique.pdf/haynes95clique.pdf",
URL = "http://citeseer.ist.psu.edu/haynes95clique.html",
abstract = "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.",
}
@InProceedings{haynes:1996:cdGP,
author = "Thomas Haynes and Dale Schoenefeld",
title = "Clique Detection via Genetic Programming",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "426",
address = "Stanford University, CA, USA",
publisher_address = "Cambridge, MA, USA",
publisher = "MIT Press",
size = "1 page",
abstract = "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.",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96 see also technical report Haynes:1995:CDGb",
}
@TechReport{Haynes:1996:DCSa,
author = "Thomas Haynes",
title = "Duplication of Coding Segments in Genetic
Programming",
number = "UTULSA-MCS-96-03",
institution = "The University of Tulsa",
month = mar # " 11,",
year = "1996",
keywords = "genetic algorithms, genetic programming",
notes = "Longer version of AAAI '96 paper
\cite{Haynes:1996:DCSb}",
broken = "http://euler.mcs.utulsa.edu/~haynes/tr_duplicate.ps",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/989/http:zSzzSzwww.umsl.eduzSz~hayneszSztr_duplicate.pdf/haynes96duplication.pdf",
URL = "http://citeseer.ist.psu.edu/haynes96duplication.html",
abstract = "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.",
}
@InProceedings{Haynes:1996:DCSb,
author = "Thomas Haynes",
title = "Duplication of Coding Segments in Genetic
Programming",
booktitle = "Proceedings of the Thirteenth National Conference on
Artificial Intelligence",
month = "4-6 " # aug,
year = "1996",
address = "Portland, USA",
volume = "1",
publisher = "AAAI Press / MIT Press",
ISBN = "0-262-51091-X",
pages = "344--349",
keywords = "genetic algorithms, genetic programming",
notes = "see also tech report \cite{Haynes:1996:DCSa}",
abstract = "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.",
URL = "http://citeseer.ist.psu.edu/haynes96duplication.html",
}
@InCollection{Haynes:1996:EBS,
author = "Thomas Haynes and Sandip Sen",
title = "Evolving Behavioral Strategies in Predators and Prey",
pages = "113--126",
editor = "Gerhard Wei{\ss} and Sandip Sen",
booktitle = "Adaptation and Learning in Multi--Agent Systems",
year = "1996",
publisher = "Springer Verlag",
series = "Lecture Notes in Artificial Intelligence",
address = "Berlin, Germany",
keywords = "genetic algorithms, genetic programming",
notes = "see also \cite{Hayes:1995:ebspp}",
broken = "http://euler.mcs.utulsa.edu/~haynes/icjai95.ps",
URL = "http://citeseer.ist.psu.edu/rd/13718071%2C21714%2C1%2C0.25%2CDownload/http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/734/http:zSzzSzwww.cs.twsu.eduzSz%7EhayneszSzicjai95.pdf/haynes96evolving.pdf",
URL = "http://citeseer.ist.psu.edu/haynes96evolving.html",
abstract = "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.",
}
@TechReport{Haynes:1996:CF,
author = "Thomas Haynes and Sandip Sen",
title = "Cooperation of the Fittest",
number = "UTULSA-MCS-96-09",
institution = "The University of Tulsa",
year = "1996",
month = apr # " 12,",
size = "9+ pages",
keywords = "genetic algorithms, genetic programming",
broken = "http://euler.mcs.utulsa.edu/~haynes/coopevol.ps",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/2230/http:zSzzSzeuler.mcs.utulsa.eduzSz~hayneszSzcoopevol.pdf/haynes96cooperation.pdf",
URL = "http://citeseer.ist.psu.edu/haynes96cooperation.html",
abstract = "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.",
notes = "evolution of cooperation (multi-agent,multi-tree) NOT
coevolution of fitness function evolution. Our printer
barfs on page 9.",
}
@InProceedings{haynes:1996:cms,
author = "Thomas Haynes",
title = "Collective Memory Search",
booktitle = "Late Breaking Papers at the Genetic Programming 1996
Conference Stanford University July 28-31, 1996",
year = "1996",
editor = "John R. Koza",
pages = "38--46",
address = "Stanford University, CA, USA",
publisher_address = "Stanford University, Stanford, California
94305-3079, USA",
month = "28--31 " # jul,
publisher = "Stanford Bookstore",
ISBN = "0-18-201031-7",
keywords = "genetic algorithms, genetic programming",
notes = "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 \cite{haynes:1997:cms}",
}
@InProceedings{haynes1996:cf,
author = "Thomas Haynes and Sandip Sen",
title = "Cooperation of the Fittest",
booktitle = "Late Breaking Papers at the Genetic Programming 1996
Conference Stanford University July 28-31, 1996",
year = "1996",
editor = "John R. Koza",
pages = "47--55",
address = "Stanford University, CA, USA",
publisher_address = "Stanford University, Stanford, California
94305-3079, USA",
month = "28--31 " # jul,
publisher = "Stanford Bookstore",
ISBN = "0-18-201031-7",
keywords = "genetic algorithms, genetic programming",
notes = "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",
}
@InProceedings{haynes:1997:cms,
author = "Thomas Haynes",
title = "Collective Memory Search",
booktitle = "Proceedings of the 1997 ACM Symposium on Applied
Computing",
year = "1997",
editor = "Barrett Bryant and Janice Carroll and Dave Oppenheim
and Jim Hightower and K. M. George",
pages = "217--222",
address = "Hyatt Sainte Claire Hotel, San Jose, California, USA",
publisher_address = "New York",
month = "28 " # feb # "-2 " # mar,
publisher = "Association for Computing Machinery",
keywords = "genetic algorithms, genetic programming",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/2284/http:zSzzSzadept.cs.twsu.eduzSz~thomaszSzcollect.pdf/haynes97collective.pdf",
URL = "http://citeseer.ist.psu.edu/haynes97collective.html",
abstract = "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...",
notes = "ACM SAC-97 0-89791-850-9
citeseer says twsu.edu/~thomas/collect.ps see
\cite{haynes:1996:cms}",
}
@InProceedings{Haynes:1997:adskr,
author = "Thomas Haynes",
title = "On-line Adaptation of Search via Knowledge Reuse",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming, distributed
search",
pages = "156--161",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.54.3381",
size = "8 pages",
abstract = "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.",
notes = "GP-97",
}
@InProceedings{Haynes:1997:caet,
author = "Thomas Haynes and Sandip Sen",
title = "Crossover Operators for Evolving {A} Team",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "162--167",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
URL = "http://www.mcs.utulsa.edu/~sandip/gp97.ps",
size = "6 pages",
notes = "GP-97",
}
@InProceedings{Haynes:1997:ccas,
author = "Thomas Haynes",
title = "Competitive Computational Agent Society",
booktitle = "Late Breaking Papers at the 1997 Genetic Programming
Conference",
year = "1997",
editor = "John R. Koza",
pages = "293",
address = "Stanford University, CA, USA",
publisher_address = "Stanford University, Stanford, California,
94305-3079, USA",
month = "13--16 " # jul,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-206995-8",
notes = "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",
}
@InProceedings{haynes:1997:pbbGP,
author = "Thomas Haynes",
title = "Phenotypical Building Blocks for Genetic Programming",
booktitle = "Genetic Algorithms: Proceedings of the Seventh
International Conference",
year = "1997",
editor = "Thomas Back",
pages = "26--33",
address = "Michigan State University, East Lansing, MI, USA",
publisher_address = "San Francisco, CA, USA",
month = "19-23 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-487-1",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/2284/http:zSzzSzadept.cs.twsu.eduzSz~thomaszSzpbb_gp.pdf/haynes97phenotypical.pdf",
URL = "http://citeseer.ist.psu.edu/haynes97phenotypical.html",
size = "8 pages",
abstract = "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.",
notes = "ICGA-97
citeseer says twsu.edu/~thomas/pbb_gp.ps",
}
@InProceedings{Haynes:1997:aaaiMAL,
author = "Thomas Haynes",
title = "Augmenting Collective Adaptation with Simple Process
Agents",
booktitle = "Papers from the AAAI Workshop on Multiagent Learning",
year = "1997",
editor = "Sandip Sen",
pages = "41--46",
organisation = "AAAI",
note = "Published in AAAI Technical Report WS-97-03",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.aaai.org/Papers/Workshops/1997/WS-97-03/WS97-03-008.pdf",
size = "6 pages",
abstract = "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.",
notes = "http://www.aaai.org/Library/Workshops/ws97-03.php",
}
@InProceedings{Haynes:1998:CRS,
author = "Thomas Haynes",
title = "A Comparision of Random Search versus Genetic
Programming as Engines for Collective Adaptation",
editor = "V. William Porto and N. Saravanan and D. Waagen and A.
E. Eiben",
booktitle = "Evolutionary Programming VII: Proceedings of the
Seventh Annual Conference on Evolutionary Programming",
year = "1998",
volume = "1447",
series = "LNCS",
pages = "683--692",
address = "Mission Valley Marriott, San Diego, California, USA",
publisher_address = "Berlin",
month = "25-27 " # mar,
organisation = "Natural Selection, Inc.",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-64891-7",
broken = "http://www.cs.twsu.edu/~haynes/random.ps",
doi = "doi:10.1007/BFb0040819",
size = "10 pages",
abstract = "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.",
notes = "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.",
}
@PhdThesis{haynes:thesis,
author = "Thomas Dunlop Haynes",
title = "Collective Adaptation: The Sharing of Building
Blocks",
school = "Department of Mathematical and Computer Sciences,
University of Tulsa",
year = "1998",
address = "Tulsa, OK, USA",
month = apr,
keywords = "genetic algorithms, genetic programming",
broken = "http://www.cs.twsu.edu/~haynes/thesis.ps",
size = "147 pages (normal spacing)",
abstract = "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).",
notes = "a ROUGH DRAFT available via
http://citeseer.ist.psu.edu/haynes96explicit.html",
}
@InProceedings{haynes:1998:acaspa,
author = "Thomas Haynes",
title = "Augmenting Collective Adaptation with Simple Process
Agents",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "116--121",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-548-7",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/4146/http:zSzzSzeuler.mcs.utulsa.eduzSz~hayneszSzactive.pdf/haynes97augmenting.pdf",
URL = "http://citeseer.ist.psu.edu/haynes97augmenting.html",
abstract = "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...",
notes = "GP-98
citeseer says utulsa.edu/~haynes/active.ps",
}
@InProceedings{haynes:1998:prdec,
author = "Thomas Haynes",
title = "Perturbing the Representation, Decoding, and
Evaluation of Chromosomes",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "122--127",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-548-7",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/2284/http:zSzzSzadept.cs.twsu.eduzSz~thomaszSzcook.pdf/haynes98perturbing.pdf",
URL = "http://citeseer.ist.psu.edu/haynes98perturbing.html",
size = "7 pages",
abstract = "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...",
notes = "GP-98
citeseer says twsu.edu/~thomas/cook.ps",
}
@Article{haynes:1998:caxcs,
author = "Thomas Haynes",
title = "Collective Adaptation: The Exchange of Coding
Segments",
journal = "Evolutionary Computation",
year = "1998",
volume = "6",
number = "4",
pages = "311--338",
month = "Winter",
keywords = "genetic algorithms, genetic programming, collective
adaptation, coding segments, duplication of coding
segments, collective memory",
URL = "http://www.mitpressjournals.org/doi/pdfplus/10.1162/evco.1998.6.4.311",
doi = "doi:10.1162/evco.1998.6.4.311",
size = "29 pages",
abstract = "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.",
notes = "Evolutionary Computation (Journal)
Special Issue: Variable-Length Representation and
Noncoding Segments for Evolutionary Algorithms Edited
by Annie S. Wu and Wolfgang Banzhaf",
}
@InProceedings{Haynes:1999:DCAa,
author = "Thomas Haynes",
title = "Distributed Collective Adaptation Applied to a Hard
Combinatorial Optimization Problem",
booktitle = "Proceedings of the 1999 ACM Symposium on Applied
Computing",
year = "1999",
editor = "Janice Carroll and Hisham Haddad and Dave Oppenheim
and Barrett Bryant and Gary B. Lamont",
pages = "339--343",
publisher = "ACM Press",
keywords = "genetic algorithms, genetic programming",
broken = "http://adept.cs.twsu.edu/~thomas/mpi.ps",
URL = "http://delivery.acm.org/10.1145/300000/298377/p339-haynes.pdf",
doi = "doi:10.1145/298151.298377",
size = "5 pages",
abstract = "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.",
notes = "(GA track)",
}
@InProceedings{Haynes:1999:DCAb,
author = "Thomas Haynes",
title = "Distributing Collective Adaptation via Message
Passing",
booktitle = "Proceedings of the 1999 ACM Symposium on Applied
Computing",
year = "1999",
editor = "Janice Carroll and Hisham Haddad and Dave Oppenheim
and Barrett Bryant and Gary B. Lamont",
pages = "501--505",
publisher = "ACM Press",
keywords = "genetic algorithms, genetic programming",
broken = "http://adept.cs.twsu.edu/~thomas/cluster.ps",
doi = "doi:10.1145/298151.298429",
abstract = "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.",
notes = "(PC Cluster track)",
}
@Proceedings{haynes:1999:fogp,
title = "Foundations of Genetic Programming",
year = "1999",
editor = "Thomas Haynes and William B. Langdon and Una-May
O'Reilly and Riccardo Poli and Justinian Rosca",
pages = "52",
address = "Orlando, Florida, USA",
month = "13 " # jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/fogp/workshop.html",
size = "20 pages",
notes = "GECCO'99 WKSHOP, GECCO-99WKS Part of
wu:1999:GECCOWKS",
}
@InProceedings{hazan:evows06,
author = "Amaury Hazan and Rafael Ramirez and Esteban Maestre
and Alfonso Perez and Antonio Pertusa",
title = "Modelling Expressive Performance: a Regression Tree
Approach Based on Strongly Typed Genetic Programming",
booktitle = "Applications of Evolutionary Computing,
EvoWorkshops2006: {EvoBIO}, {EvoCOMNET}, {EvoHOT},
{EvoIASP}, {EvoInteraction}, {EvoMUSART}, {EvoSTOC}",
year = "2006",
month = "10-12 " # apr,
editor = "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",
series = "LNCS",
volume = "3907",
publisher = "Springer Verlag",
address = "Budapest",
publisher_address = "Berlin",
keywords = "genetic algorithms, genetic programming, STGP",
ISBN = "3-540-33237-5",
pages = "676--687",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3907&spage=676",
doi = "doi:10.1007/11732242_64",
abstract = "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.",
notes = "part of \cite{evows06}",
}
@InProceedings{Hazell:2008:geccocomp,
author = "Alex Hazell and Stephen L. Smith",
title = "Towards an objective assessment of Alzheimer's
disease: the application of a novel evolutionary
algorithm in the analysis of figure copying tasks",
year = "2008",
editor = "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",
isbn13 = "978-1-60558-131-6",
booktitle = "GECCO-2008 Workshop: MedGEC Medical Applications of
Genetic and Evolutionary Computation",
pages = "2073--2080",
address = "Atlanta, GA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p2073.pdf",
doi = "doi:10.1145/1388969.1389024",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "12-16 " # jul,
keywords = "genetic algorithms, genetic programming, Alzheimer's
disease, cartesian genetic programming, evolutionary
algorithm(s), image analysis, medical applications",
notes = "Distributed on CD-ROM at GECCO-2008
ACM Order Number 910081. Also known as \cite{1389024}",
}
@InProceedings{he:2005:EH,
author = "Jingsong He and Xufa Wang and Min Zhang and Jiying
Wang and Qiansheng Fang",
title = "New Research on Scalability of Lossless Image
Compression by {GP} Engine",
booktitle = "Proceedings of the 2005 NASA/DoD Conference on
Evolvable Hardware",
year = "2005",
editor = "Jason Lohn and David Gwaltney and Gregory Hornby and
Ricardo Zebulum and Didier Keymeulen and Adrian
Stoica",
pages = "160--164",
address = "Washington, DC, USA",
month = "29 " # jun # "-1 " # jul,
publisher = "IEEE Press",
publisher_address = "IEEE Service Center 445 Hoes Lane Asia P.O. Box
1331 Piscataway, NJ 08855-1331",
organisation = "NASA, DoD",
keywords = "genetic algorithms, genetic programming, EHW",
ISBN = "0-7695-2399-4",
doi = "doi:10.1109/EH.2005.35",
abstract = "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.",
notes = "EH2005 IEEE Computer Society Order Number P2399",
}
@InProceedings{He:2010:CIKM,
author = "Qiang He and Jun Ma and Shuaiqiang Wang",
title = "Directly optimizing evaluation measures in learning to
rank based on the clonal selection algorithm",
booktitle = "Proceedings of the 19th ACM international conference
on Information and knowledge management, CIKM '10",
year = "2010",
pages = "1449--1452",
address = "Toronto, ON, Canada",
publisher = "ACM",
keywords = "genetic algorithms, genetic programming, clonal
selection algorithm, information retrieval, learning to
rank, machine learning, ranking function: Poster",
isbn13 = "978-1-4503-0099-5",
doi = "doi:10.1145/1871437.1871644",
size = "4 pages",
acmid = "1871644",
abstract = "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.",
}
@InProceedings{He:2006:ciea,
author = "Mingyi He and Yifan Zhang and Yuzhen Xie and Na Liang
and Changyun Wen",
title = "Classification of Multi-spectral/Hyperspectral Data
using Genetic Programming and Error-correcting Output
Codes",
booktitle = "1ST IEEE Conference on Industrial Electronics and
Applications",
year = "2006",
pages = "1--6",
address = "Singapore",
month = "24-26 " # may,
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-9514-X",
doi = "doi:10.1109/ICIEA.2006.257153",
abstract = "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",
notes = "INSPEC Accession Number: 9096919
Sch. of Electron. & Inf., Northwestern Polytech Univ.,
Xi'an;",
}
@InProceedings{He3:2008:cec,
author = "Pei He and Lishan Kang and Ming Fu",
title = "Formality Based Genetic Programming",
booktitle = "2008 IEEE World Congress on Computational
Intelligence",
year = "2008",
editor = "Jun Wang",
pages = "4080--4087",
address = "Hong Kong",
month = "1-6 " # jun,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-1823-7",
file = "EC0867.pdf",
doi = "doi:10.1109/CEC.2008.4631354",
abstract = "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.",
keywords = "genetic algorithms, genetic programming, program
verification, approximate program, automatic
programming, formality based genetic programming,
software testing",
notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
EPS and the IET.",
}
@InProceedings{heckendorn:1999:PTSSGM,
author = "Robert B. Heckendorn and Soraya Rana and Darrell
Whitley",
title = "Polynomial Time Summary Statistics for a
Generalization of {MAXSAT}",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "281--288",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/maxsat99.pdf",
URL = "http://www.cs.colostate.edu/~genitor/1999/maxsat99.pdf",
abstract = "NK landscape, Walsh analysis",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@Article{Hecker200986,
author = "Michael Hecker and Sandro Lambeck and Susanne Toepfer
and Eugene {van Someren} and Reinhard Guthke",
title = "Gene regulatory network inference: Data integration in
dynamic models--{A} review",
journal = "Biosystems",
volume = "96",
number = "1",
pages = "86--103",
year = "2009",
ISSN = "0303-2647",
doi = "doi:10.1016/j.biosystems.2008.12.004",
URL = "http://www.sciencedirect.com/science/article/B6T2K-4V7MSTS-1/2/db669ac3459da19bab3535dc038303d5",
keywords = "genetic algorithms, genetic programming, Systems
biology, Reverse engineering, Biological modelling,
Knowledge integration",
abstract = "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.",
notes = "survey",
}
@InProceedings{Hedar:2010:ICCTD,
author = "Abdel-Rahman Hedar and Mostafa Kamel Osman",
title = "Scatter Programming",
booktitle = "2nd International Conference on Computer Technology
and Development (ICCTD), 2010",
year = "2010",
month = "2-4 " # nov,
pages = "451--455",
address = "Cairo",
abstract = "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.",
keywords = "genetic algorithms, genetic programming, cartesian
genetic programming, grammatical evolution, artificial
intelligence, machine learning, scatter programming,
learning (artificial intelligence)",
doi = "doi:10.1109/ICCTD.2010.5645839",
notes = "symbolic regression, 6-mux. Also known as
\cite{5645839}",
}
@Article{journals/ijitdm/HedarMF11,
author = "Abdel-Rahman Hedar and Emad Mabrouk and Masao
Fukushima",
title = "Tabu Programming: a New Problem Solver through
Adaptive Memory Programming over Tree Data Structures",
journal = "International Journal of Information Technology and
Decision Making",
volume = "10",
number = "2",
year = "2011",
pages = "373--406",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1142/S0219622011004373",
oai = "oai:RePEc:wsi:ijitdm:v:10:y:2011:i:02:p:373-406",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
}
@Article{Hedberg:2005:IS,
author = "Sara Resse Hedberg",
title = "Evolutionary computing: the rise of electronic
breeding",
journal = "Intelligent Systems",
year = "2005",
volume = "20",
number = "6",
pages = "12--15",
month = nov # "-" # dec,
keywords = "genetic algorithms, genetic programming, biological
evolution, electronic breeding, evolutionary
computing",
doi = "doi:10.1109/MIS.2005.104",
ISSN = "1541-1672",
size = "4 pages",
abstract = "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.",
notes = "high level",
}
@InProceedings{heddad:evows04,
author = "Amine Heddad and Markus Brameier and Robert M.
MacCallum",
title = "Evolving Regular Expression-based Sequence Classifiers
for Protein Nuclear Localisation",
booktitle = "Applications of Evolutionary Computing,
EvoWorkshops2004: {EvoBIO}, {EvoCOMNET}, {EvoHOT},
{EvoIASP}, {EvoMUSART}, {EvoSTOC}",
year = "2004",
month = "5-7 " # apr,
editor = "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",
series = "LNCS",
volume = "3005",
address = "Coimbra, Portugal",
publisher = "Springer Verlag",
publisher_address = "Berlin",
pages = "31--40",
keywords = "genetic algorithms, genetic programming, evolutionary
computation, perl, grammar, BNF, linear GP, LGP, RE,
regular expressions",
ISBN = "3-540-21378-3",
URL = "http://www.sbc.su.se/~maccallr/publications/heddad-evobio2004.pdf",
abstract = "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.",
notes = "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. \cite{brameier:nucpred}",
}
@InProceedings{Hedman:2002:gecco,
author = "Karl Hedman and David Persson and Per Skoglund and Dan
Wiklund and Krister Wolff and Peter Nordin",
title = "Sensing And Direction In Locomotion Learning With {A}
Random Morphology Robot",
booktitle = "GECCO 2002: Proceedings of the Genetic and
Evolutionary Computation Conference",
editor = "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",
year = "2002",
pages = "1297",
address = "New York",
publisher_address = "San Francisco, CA 94104, USA",
month = "9-13 " # jul,
publisher = "Morgan Kaufmann Publishers",
keywords = "genetic algorithms, genetic programming, evolutionary
robotics, poster paper, evolutionary algorithm, random
morphology",
ISBN = "1-55860-878-8",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/ROB211.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/ROB211.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-23.pdf",
abstract = "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.",
notes = "GECCO-2002. A joint meeting of the eleventh
International Conference on Genetic Algorithms
(ICGA-2002) and the seventh Annual Genetic Programming
Conference (GP-2002)",
}
@InCollection{Heiberg:1997:lbn,
author = "Vilhelm Heiberg",
title = "Learning {Bayesian} Networks Using a Genetic
Algorithm",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1997",
publisher = "Stanford Bookstore",
year = "1997",
editor = "John R. Koza",
pages = "86--97",
address = "Stanford, California, 94305-3079 USA",
month = "17 " # mar,
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-205981-2",
abstract = "for learning baysian networks .... intelligent Greedy
Search outperforms GA and SA",
notes = "part of \cite{koza:1997:GAGPs}",
}
@Article{oai:biomedcentral.com:1471-2156-7-23,
title = "The challenge for genetic epidemiologists: how to
analyze large numbers of {SNP}s in relation to complex
diseases",
author = "A Geert Heidema and Jolanda M A Boer and Nico
Nagelkerke and Edwin C M Mariman and Daphne L {van der
A} and Edith J M Feskens",
year = "2006",
month = apr # "~21",
journal = "BMC Genetics",
volume = "7",
number = "23",
publisher = "BioMed Central Ltd.",
bibsource = "OAI-PMH server at www.biomedcentral.com",
language = "en",
oai = "oai:biomedcentral.com:1471-2156-7-23",
rights = "Copyright 2006 Heidema et al; licensee BioMed Central
Ltd.",
type = "Commentary",
keywords = "genetic algorithms, genetic programming",
ISSN = "1471-2156",
URL = "http://www.biomedcentral.com/content/pdf/1471-2156-7-23.pdf",
URL = "http://www.biomedcentral.com/1471-2156/7/23",
doi = "doi:10.1186/1471-2156-7-23",
size = "15 pages",
abstract = "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.",
notes = "Open Access",
}
@Article{HeidrichMeisner2009152,
author = "Verena Heidrich-Meisner and Christian Igel",
title = "Neuroevolution strategies for episodic reinforcement
learning",
journal = "Journal of Algorithms",
volume = "64",
number = "4",
pages = "152--168",
year = "2009",
note = "Special Issue: Reinforcement Learning",
ISSN = "0196-6774",
doi = "doi:10.1016/j.jalgor.2009.04.002",
URL = "http://www.sciencedirect.com/science/article/B6WH3-4W7RY8J-3/2/22f7075bc25dab10a8ff3714e2fee303",
keywords = "genetic algorithms, genetic programming, Reinforcement
learning, Evolution strategy, Covariance matrix
adaptation, Partially observable Markov decision
process, Direct policy search",
abstract = "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.",
notes = "compared against CE \cite{gruau:1996:ceVdeGNN}",
}
@Article{Hein:1994:TI,
author = "Carl Hein and Alex Meystel",
title = "A genetic technique for robotic trajectory planning",
journal = "Telematics and Informatics",
year = "1994",
volume = "11",
pages = "351--364",
number = "4",
abstract = "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.",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6V1H-48V1Y16-6/2/1a0f7979e649fe0ff30f590d6fc5e0b5",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{heiss-czedik:1997:highlevel,
author = "D. Heiss-Czedik",
title = "Is Genetic Programming Dependent on High-level
Primitives?",
booktitle = "Artificial Neural Nets and Genetic Algorithms:
Proceedings of the International Conference,
ICANNGA97",
year = "1997",
editor = "George D. Smith and Nigel C. Steele and Rudolf F.
Albrecht",
address = "University of East Anglia, Norwich, UK",
publisher = "Springer-Verlag",
note = "published in 1998",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-211-83087-1",
notes = "Dorothea Heiss, nee
Czedik-Eysenberg
http://www.sys.uea.ac.uk/Research/ResGroups/MAG/ICANNGA97/papers_frame.html",
}
@InProceedings{Helm:2002:WSC,
author = "Terry M. Helm and Steve W. Painter and W. Robert
Oakes",
title = "A Comparison of Three Optimization Methods for
Scheduling Maintenance of High Cost, Long-Lived Capital
Assets",
booktitle = "Proceedings of the 2002 Winter Simulation Conference",
year = "2002",
editor = "E. Yucesan and C.-H. Chen and J. L. Snowdon and J. M.
Charnes",
volume = "2",
pages = "880--1884",
keywords = "genetic algorithms, genetic programming, constraint
handling, financial data processing, investment,
minimisation, scheduling, constraint programming,
costs, investments, long-lived capital assets,
maintenance scheduling, minimization, optimization",
URL = "http://www.informs-sim.org/wsc02papers/259.pdf",
doi = "doi:10.1109/WSC.2002.1166483",
abstract = "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",
}
@InProceedings{helmer:1999:FSUGAID,
author = "Guy Helmer and Johnny Wong and Vasant Honavar and Les
Miller",
title = "Feature Selection Using a Genetic Algorithm for
Intrusion Detection",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1781",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "real world applications, poster papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-737.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-737.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@Misc{Helmer:1999:snake,
author = "Martin Helmer and Martin Hemberg",
title = "Moving a Snake Robot using Genetic Programming",
howpublished = "www",
year = "1999",
month = "15 " # dec,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.dd.chalmers.se/~f96mahe/evcomp.html
broken",
size = "20k",
abstract = "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.",
}
@InProceedings{Helmuth:2011:GECCOcomp,
author = "Thomas Helmuth and Lee Spector and Brian Martin",
title = "Size-based tournaments for node selection",
booktitle = "GECCO 2011 Graduate students workshop",
year = "2011",
editor = "Miguel Nicolau",
isbn13 = "978-1-4503-0690-4",
keywords = "genetic algorithms, genetic programming",
pages = "799--802",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001858.2002095",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{2002095} Distributed on CD-ROM at
GECCO-2011.
ACM Order Number 910112.",
}
@InProceedings{eurogp07:hemberg,
author = "Erik Hemberg and Conor Gilligan and Michael O'Neill
and Anthony Brabazon",
title = "A Grammatical Genetic Programming Approach to
Modularity in Genetic Algorithms",
editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and
Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar",
booktitle = "Proceedings of the 10th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "4445",
year = "2007",
address = "Valencia, Spain",
month = "11-13 " # apr,
pages = "1--11",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-71602-5",
isbn13 = "978-3-540-71602-0",
doi = "doi:10.1007/978-3-540-71605-1_1",
abstract = "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.",
notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in
conjunction with EvoCOP2007, EvoBIO2007 and
EvoWorkshops2007",
}
@InProceedings{conf/eurogp/HembergOB08,
title = "Altering Search Rates of the Meta and Solution
Grammars in the m{GGA}",
author = "Erik Hemberg and Michael O'Neill and Anthony
Brabazon",
bibdate = "2008-04-15",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#HembergOB08",
booktitle = "Proceedings of the 11th European Conference on Genetic
Programming, EuroGP 2008",
address = "Naples",
month = "26-28 " # mar,
publisher = "Springer",
year = "2008",
volume = "4971",
editor = "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",
isbn13 = "978-3-540-78670-2",
pages = "362--373",
series = "Lecture Notes in Computer Science",
doi = "doi:10.1007/978-3-540-78671-9_31",
keywords = "genetic algorithms, genetic programming",
notes = "Part of \cite{conf/eurogp/2008} EuroGP'2008 held in
conjunction with EvoCOP2008, EvoBIO2008 and
EvoWorkshops2008",
}
@InProceedings{Hemberg:2008:cec,
author = "Erik Hemberg and Michael O'Neill and Anthony
Brabazon",
title = "Grammatical Bias and Building Blocks in Meta-Grammar
Grammatical Evolution",
booktitle = "2008 IEEE World Congress on Computational
Intelligence",
year = "2008",
editor = "Jun Wang",
address = "Hong Kong",
month = "1-6 " # jun,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
isbn13 = "978-1-4244-1823-7",
file = "EC0802.pdf",
abstract = "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.",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
EPS and the IET.",
}
@InProceedings{DBLP:conf/gecco/Hemberg09,
author = "Erik Hemberg",
title = "An exploration of learning and grammars in grammatical
evolution",
booktitle = "GECCO-2009 Graduate student workshop",
year = "2009",
editor = "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",
pages = "2705--2708",
address = "Montreal",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "8-12 " # jul,
organisation = "SigEvo",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
isbn13 = "978-1-60558-325-9",
bibsource = "DBLP, http://dblp.uni-trier.de",
doi = "doi:10.1145/1570256.1570389",
abstract = "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.",
notes = "Distributed on CD-ROM at GECCO-2009.
ACM Order Number 910092.",
}
@InProceedings{Hemberg:2008:ECSummerSchool,
author = "Erik Hemberg and Nic McPhee and Michael O'Neill and
Anthony Brabazon",
title = "Pre-, In- and Postfix grammars for Symbolic Regression
in Grammatical Evolution",
booktitle = "IEEE Workshop and Summer School on Evolutionary
Computing",
year = "2008",
editor = "T. M. McGinnity",
pages = "18--22",
address = "University of Ulster, Derry, Northern Ireland",
month = "18-22 " # aug,
keywords = "genetic algorithms, genetic programming, Grammatical
Evolution",
notes = "http://isel.infm.ulst.ac.uk/conference/wssec2008/wssec08.htm",
}
@InProceedings{Hemberg:2009:Mendel,
author = "Erik Hemberg and Michael O'Neill and Anthony
Brabazon",
title = "An investigation into automatically defined function
representations in Grammatical Evolution",
booktitle = "15th International Conference on Soft Computing,
Mendel'09",
year = "2009",
editor = "R. Matousek and L. Nolle",
address = "Brno, Czech Republic",
month = "24-26 " # jun,
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
isbn13 = "978-80-214-3884-2",
notes = "ID09045
http://www.mendel-conference.org/tmp/ScheduleMendel2009b.pdf
Also in electronic form ISSN 1803-3814",
}
@PhdThesis{Hemberg:thesis,
author = "Erik Anders Pieter Hemberg",
title = "An Exploration of Grammars in Grammatical Evolution",
school = "University College Dublin",
address = "Ireland",
month = "17 " # sep,
year = "2010",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
URL = "http://ncra.ucd.ie/papers/exploration_of_grammars_in_grammatical_evolution.pdf",
size = "265 pages",
abstract = "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.",
}
@InProceedings{Hemberg:2011:GECCOcomp,
author = "Erik Hemberg and Lester Ho and Michael O'Neill and
Holger Claussen",
title = "A symbolic regression approach to manage femtocell
coverage using grammatical genetic programming",
booktitle = "3rd symbolic regression and modeling workshop for
GECCO 2011",
year = "2011",
editor = "Steven Gustafson and Ekaterina Vladislavleva",
isbn13 = "978-1-4503-0690-4",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
pages = "639--646",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001858.2002061",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{2002061} Distributed on CD-ROM at
GECCO-2011.
ACM Order Number 910112.",
}
@MastersThesis{hemberg:2001:masters,
author = "Martin Hemberg",
title = "{GENR8} - {A} Design Tool for Surface Generation",
school = "Department of Physical Resource Theory",
year = "2001",
address = "Chalmers University, Sweden",
month = jun # " 29",
keywords = "genetic algorithms, genetic programming, lindenmayer
system, development, grammatical evolution",
URL = "http://www.ai.mit.edu/projects/emergentDesign/genr8/main.pdf",
size = "90 pages",
abstract = "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.",
notes = "Master of Science Engineering Physics",
}
@InProceedings{hemberg:2001:adtsg,
author = "Martin Hemberg and Una-May O'Reilly and Peter Nordin",
title = "{GENR8} - {A} Design Tool for Surface Generation",
booktitle = "2001 Genetic and Evolutionary Computation Conference
Late Breaking Papers",
year = "2001",
editor = "Erik D. Goodman",
pages = "160--167",
address = "San Francisco, California, USA",
month = "9-11 " # jul,
keywords = "genetic algorithms, genetic programming, architecture,
Lindenmayer systems, BNF grammar, HEMLS, grammatical
evolution, Alias|Wavefront Maya",
URL = "http://www.ai.mit.edu/projects/emergentDesign/genr8/lateGecco.pdf",
notes = "GECCO-2001LB",
}
@InProceedings{hemberg:2001:adtsg2,
author = "Martin Hemberg and Una-May O'Reilly",
title = "{GENR8} - {A} Design Tool for Surface Generation",
booktitle = "Graduate Student Workshop",
year = "2001",
editor = "Conor Ryan",
pages = "413--416",
address = "San Francisco, California, USA",
month = "7 " # jul,
keywords = "genetic algorithms, genetic programming",
notes = "GECCO-2001WKS Part of heckendorn:2001:GECCOWKS, see
\cite{hemberg:2001:adtsg}, GENR8",
}
@InProceedings{hemberg:2002:gecco:workshop,
title = "{GENR8} - Using Grammatical Evolution In {A} Surface
Design Tool",
author = "Martin Hemberg and Una-May O'Reilly",
pages = "120--123",
booktitle = "{GECCO 2002}: Proceedings of the Bird of a Feather
Workshops, Genetic and Evolutionary Computation
Conference",
editor = "Alwyn M. Barry",
year = "2002",
month = "8 " # jul,
publisher = "AAAI",
address = "New York",
publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
URL = "http://www.ai.mit.edu/projects/emergentDesign/genr8/gecco2002.pdf",
size = "4 pages",
notes = "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",
}
@InProceedings{hemberg:2004:eurogp,
author = "Martin Hemberg and Una-May O'Reilly",
title = "Extending Grammatical Evolution to Evolve Digital
Surfaces with Genr8",
booktitle = "Genetic Programming 7th European Conference, EuroGP
2004, Proceedings",
year = "2004",
editor = "Maarten Keijzer and Una-May O'Reilly and Simon M.
Lucas and Ernesto Costa and Terence Soule",
volume = "3003",
series = "LNCS",
pages = "299--308",
address = "Coimbra, Portugal",
publisher_address = "Berlin",
month = "5-7 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
ISBN = "3-540-21346-5",
URL = "http://www.ai.mit.edu/projects/emergentDesign/genr8/euroGPpaper.pdf",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=299",
abstract = "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.",
notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in
conjunction with EvoCOP2004 and EvoWorkshops2004 GENR8
http://www.ai.mit.edu/projects/emergentDesign/genr8/euroGPposter.pdf",
}
@InProceedings{hemberg:2004:ALwks,
author = "Martin Hemberg and Una-May O'Reilly",
title = "Using Generative Growth Systems to Design
Architectural Form",
booktitle = "Workshop and Tutorial Proceedings Ninth International
Conference on the Simulation and Synthesis of Living
Systems(Alife {XI})",
year = "2004",
editor = "Mark Bedau and Phil Husbands and Tim Hutton and
Sanjeev Kumar and Hideaki Sizuki",
pages = "33--36",
address = "Boston, Massachusetts",
month = "12 " # sep,
note = "Self-organisation and development in artificial and
natural systems workshop.",
keywords = "genetic algorithms, genetic programming, gramatical
evolution, Genr8, HEMLS, Lindenmayer (L-systems), BNF",
URL = "http://www.cs.ucl.ac.uk/staff/S.Kumar/hemberg-oreilly.zip",
abstract = "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.",
notes = "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.
\cite{broughton:1999:e3DwlsGPwww} Rosenman and John S
Gero, GADES, GENRE, The Groningen Twister, Jackson, J.
Frazer, MoSS, AgencyGP \cite{o'reilly:2001:aagpd},
Genr8 \cite{hemberg:2001:masters}",
}
@MastersThesis{Hemert:mastersthesis:1998,
author = "J. I. {van Hemert}",
title = "Applying Adaptive Evolutionary Algorithms to Hard
Problems",
school = "Leiden University",
year = "1998",
month = "31 " # aug,
URL = "http://www.vanhemert.co.uk/publications/IR-98-19.ps.gz",
keywords = "constraint satisfaction; data mining",
abstract = "Supervised by A.E. Eiben and E. Marchiori",
type = "Master's thesis",
}
@TechReport{tr-01-01,
title = "An Engineering Approach to Evolutionary Art",
author = "J. I. {van Hemert} and M. L. M. Jansen",
year = "2001",
month = "31 " # jan,
institution = "Leiden University",
number = "TR-01-01",
URL = "http://www.vanhemert.co.uk/publications/tr01-01.An_Engineering_Approach_to_Evolutionary_Art.pdf",
URL = "http://www.vanhemert.co.uk/publications/tr01-01.An_Engineering_Approach_to_Evolutionary_Art.ps.gz",
keywords = "genetic algorithms, genetic programming, evolutionary
art",
abstract = "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.",
size = "pages",
}
@TechReport{tr-01-02,
title = "A ``Futurist'' approach to dynamic environments",
author = "Jano I. {van Hemert} and Clarissa {Van Hoyweghen} and
Eduard Lukschandl and Katja Verbeeck",
year = "2001",
month = "31 " # jan,
institution = "Leiden University",
number = "{TR-01-02}",
URL = "http://www.vanhemert.co.uk/publications/tr01-02.A_Futurist_Approach_to_Dynamic_Environments.pdf",
URL = "http://www.vanhemert.co.uk/publications/tr01-02.A_Futurist_Approach_to_Dynamic_Environments.ps.gz",
keywords = "genetic algorithms, genetic programming, dynamic
problems, interactive evolution",
abstract = "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.",
}
@InProceedings{hemert:2001:gecco,
title = "An Engineering Approach to Evolutionary Art",
author = "J. I. {van Hemert} and M. L. M. Jansen",
pages = "177",
year = "2001",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2001)",
editor = "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",
address = "San Francisco, California, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "7-11 " # jul,
keywords = "genetic algorithms, genetic programming: Poster, art,
abstract, Internet, human induced fitness function,
subjective, gene bank, evolutionary art",
ISBN = "1-55860-774-9",
URL = "http://www.vanhemert.co.uk/publications/gecco2001.An_Engineering_Approach_to_Evolutionary_Art.pdf",
URL = "http://www.vanhemert.co.uk/publications/gecco2001.An_Engineering_Approach_to_Evolutionary_Art.ps.gz",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d02.pdf",
abstract = "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.",
notes = "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 \cite{spector:2001:GECCO}
See also \cite{tr-01-01}",
}
@InCollection{Henderson1999223,
author = "Robert W. Henderson and Robert Powell",
title = "West Indian Herpetoecology",
editor = "Brian I. Crother",
booktitle = "Caribbean Amphibians and Reptiles",
publisher = "Academic Press",
address = "San Diego",
year = "1999",
pages = "223--268",
isbn13 = "978-0-12-197955-3",
doi = "doi:10.1016/B978-012197955-3/50019-7",
URL = "http://www.sciencedirect.com/science/article/B87C3-4PN0BJP-K/2/14f280906c919939952ffbddf6b96c6c",
notes = "Not on GP",
}
@InProceedings{oai:CiteSeerPSU:536164,
author = "S. Hengpraprohm and P. Chongstitvatana",
title = "Selective Crossover in Genetic Programming",
booktitle = "ISCIT International Symposium on Communications and
Information Technologies",
year = "2001",
address = "ChiangMai Orchid, ChiangMai Thailand",
month = "14-16 " # nov,
keywords = "genetic algorithms, genetic programming",
citeseer-isreferencedby = "oai:CiteSeerPSU:87839;
oai:CiteSeerPSU:272763; oai:CiteSeerPSU:322608",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:536164",
rights = "unrestricted",
URL = "http://www.cp.eng.chula.ac.th/~piak/paper/ISCIT534.pdf",
URL = "http://citeseer.ist.psu.edu/536164.html",
abstract = "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.",
notes = "http://www.ecti.or.th/conferences/ISCIT/
Chulalongkorn University, Thailand",
}
@InProceedings{Hengpraprohm:2007:FBIT,
author = "S. Hengpraprohm and P. Chongstitvatana",
title = "Selecting Informative Genes from Microarray Data for
Cancer Classification with Genetic Programming
Classifier Using {K}-Means Clustering and {SNR}
Ranking",
booktitle = "Proceedings of the 2007 International Conference
Frontiers in the Convergence of Bioscience and
Information Technologies (FBIT 2007)",
year = "2007",
pages = "211--218",
address = "Jeju Island, Korea",
month = oct # " 11-13",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-0-7695-2999-8",
URL = "http://www.computer.org/portal/web/csdl/doi/10.1109/FBIT.2007.84",
doi = "doi:10.1109/FBIT.2007.84",
abstract = "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.",
notes = "DOI broken 9 May 2010 Dept. of Comput. Eng.,
Chulalongkorn Univ., Chulalongkorn",
}
@InProceedings{Hengpraprohm:2008:ICICIC,
author = "Supoj Hengpraprohm and Prabhas Chongstitvatana",
title = "A Genetic Programming Ensemble Approach to Cancer
Microarray Data Classification",
booktitle = "3rd International Conference on Innovative Computing
Information and Control, ICICIC '08",
year = "2008",
month = jun # " 18-" # jun # " 20",
pages = "340--340",
address = "Dalian, Liaoning China",
isbn13 = "978-0-7695-3161-8",
keywords = "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",
URL = "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4603529",
doi = "doi:10.1109/ICICIC.2008.35",
abstract = "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.",
notes = "Also known as \cite{4603529}",
}
@Article{journals/kbs/HennessyMCR05,
title = "An improved genetic programming technique for the
classification of Raman spectra",
author = "Kenneth Hennessy and Michael G. Madden and Jennifer
Conroy and Alan G. Ryder",
journal = "Knowledge Based Systems",
year = "2005",
number = "4-5",
volume = "18",
pages = "217--224",
month = aug,
note = "AI-2004, Cambridge, England, 13th-15th December 2004",
bibdate = "2005-11-24",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/kbs/kbs18.html#HennessyMCR05",
keywords = "genetic algorithms, genetic programming, Machine
learning, Neural networks, Spectroscopy, Raman",
doi = "doi:10.1016/j.knosys.2004.10.001",
abstract = "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.",
}
@InCollection{henry:1994:ca,
author = "Kelvin C. Henry",
title = "Exploring Cellular Automata Using a Two-Dimensional
Genetic Algorithm",
booktitle = "Genetic Algorithms at Stanford 1994",
year = "1994",
editor = "John R. Koza",
pages = "57--66",
address = "Stanford, California, 94305-3079 USA",
month = dec,
organisation = "Stanford University",
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, life, GENESIS",
ISBN = "0-18-187263-3",
notes = "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",
}
@InCollection{Heralic:2007:hrnd,
author = "Almir Heralic and Krister Wolff and Mattias Wahde",
title = "Central Pattern Generators for Gait Generation in
Bipedal Robots",
booktitle = "Humanoid Robots: New Developments",
publisher = "I-Tech Education and Publishing",
year = "2007",
editor = "Armando Carlos {de Pina Filho}",
chapter = "17",
pages = "285--304",
month = jun,
note = "Invited book chapter",
address = "Vienna, Austria",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-902613-00-4",
URL = "http://www.intechopen.com/download/pdf/pdfs_id/237",
URL = "http://www.intechopen.com/articles/show/title/central_pattern_generators_for_gait_generation_in_bipedal_robots",
abstract = "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.",
size = "20 pages",
}
@InProceedings{hernandez:1999:SDMEACS,
author = "German Hernandez and Jerome A. Goldstein and Fernando
Niao",
title = "Stochastic Differential Model for Evolutionary
Algorithms over Continuous Spaces",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "863--870",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "evolution strategies and evolutionary programming",
ISBN = "1-55860-611-4",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{hernandez:2004:otdospngbmogp,
title = "On the design of state-of-the-art pseudorandom number
generators by means of genetic programming",
author = "Julio Cesar Hernandez and Andre Seznec and Pedro
Isasi",
pages = "1510--1516",
booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary
Computation",
year = "2004",
publisher = "IEEE Press",
month = "20-23 " # jun,
address = "Portland, Oregon",
ISBN = "0-7803-8515-2",
doi = "doi:10.1109/CEC.2004.1331075",
keywords = "genetic algorithms, genetic programming, Evolutionary
Computation in Cryptology and Computer Security,
cellular automata, fitness function, pseudorandom
number generators, cellular automata, random number
generation",
abstract = "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.",
notes = "PRNG. Also known as \cite{1331075}. CEC 2004 - A joint
meeting of the IEEE, the EPS, and the IEE.",
}
@InProceedings{hernandez-aguirre:2000:gsbfbmgp,
author = "Arturo Hernandez-Aguirre and Bill P. Buckles and
Carlos A. Coello-Coello",
title = "Gate-level Synthesis of {Boolean} Functions using
Binary Multiplexers and Genetic Programming",
booktitle = "Proceedings of the 2000 Congress on Evolutionary
Computation CEC00",
year = "2000",
pages = "675--682",
address = "La Jolla Marriott Hotel La Jolla, California, USA",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "6-9 " # jul,
organisation = "IEEE Neural Network Council (NNC), Evolutionary
Programming Society (EPS), Institution of Electrical
Engineers (IEE)",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, hybrid
systems",
ISBN = "0-7803-6375-2",
URL = "http://www.lania.mx/~ccoello/papers/hernandez00.ps.gz",
URL = "http://citeseer.ist.psu.edu/309980.html",
abstract = "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.",
notes = "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",
}
@InProceedings{Hernandez-Castro:2006:CEC,
author = "Julio C. Hernandez-Castro and Juan M. Estevez-Tapiador
and Arturo Ribagorda-Garnacho and Benjamin
Ramos-Alvarez",
title = "Wheedham: An Automatically Designed Block Cipher by
means of Genetic Programming",
booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary
Computation",
year = "2006",
editor = "Gary G. Yen and Lipo Wang and Piero Bonissone and
Simon M. Lucas",
pages = "499--506",
address = "Vancouver",
month = "6-21 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-9487-9",
size = "8 pages",
abstract = "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.",
notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
the IEE.
IEEE Catalog Number: 06TH8846D",
}
@InProceedings{Hershkovitz:2008:ESDA,
author = "Shany Hershkovitz and Sioma Baltianski and Yoed Tsur",
title = "{Nb}-Doped Barium Titanate: Concentration-Properties
Relations",
booktitle = "9th Biennial Conference on Engineering Systems Design
and Analysis (ESDA2008)",
year = "2008",
volume = "1",
pages = "499--504",
address = "Haifa, Israel",
month = jul # " 7-9",
publisher = "ASME",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-0-7918-4836-4",
doi = "doi:10.1115/ESDA2008-59049",
abstract = "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.",
notes = "Technion-Israel Institute of Technology, Haifa,
Israel",
}
@Article{Hershkovitz2011104,
author = "Shany Hershkovitz and Sioma Baltianski and Yoed Tsur",
title = "Harnessing evolutionary programming for impedance
spectroscopy analysis: {A} case study of mixed
ionic-electronic conductors",
journal = "Solid State Ionics",
volume = "188",
number = "1",
pages = "104--109",
year = "2011",
note = "9th International Symposium on Systems with Fast Ionic
Transport",
ISSN = "0167-2738",
doi = "doi:10.1016/j.ssi.2010.10.004",
URL = "http://www.sciencedirect.com/science/article/B6TY4-51D5RFW-2/2/78396a47420bfca2e3d664e88b21c461",
keywords = "genetic algorithms, genetic programming, Impedance
spectroscopy, Warburg elements, Parametric analysis",
abstract = "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).",
}
@InProceedings{hervas:2001:MTRSI,
author = "Javier Hervas and Paul L. Rosin",
title = "Image Thresholding For Landslide Detection By Genetic
Programming",
booktitle = "Proceedings of the First International Workshop on
Multitemporal Remote Sensing Images",
year = "2001",
editor = "Lorenzo Bruzzone and Paul Smits",
address = "University of Trento, Italy",
month = "13-14 " # sep,
publisher = "World Scientific Publishing",
keywords = "genetic algorithms, genetic programming",
ISBN = "981-02-4955-1",
notes = "see also \cite{oai:CiteSeerPSU:555070}",
}
@Misc{oai:CiteSeerPSU:555070,
title = "Image Thresholding For Landslide Detection By Genetic
Programming",
author = "Javier Hervas and Paul L. Rosin",
year = "2003",
month = jan # "~02",
abstract = "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",
citeseer-isreferencedby = "oai:CiteSeerPSU:93111",
citeseer-references = "oai:CiteSeerPSU:557560",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:555070",
rights = "unrestricted",
URL = "http://www.cs.cf.ac.uk/User/Paul.Rosin/resources/papers/gp2.pdf",
URL = "http://citeseer.ist.psu.edu/555070.html",
keywords = "genetic algorithms, genetic programming",
notes = "see also \cite{hervas:2001:MTRSI}
http://www.worldscibooks.com/compsci/4997.html",
}
@InProceedings{hetland:2002:RASC,
author = "Magnus Lie Hetland and Pal Saetrom",
title = "Temporal Rule Discovery using Genetic Programming and
Specialized Hardware",
booktitle = "Proceedings of the 4th International Conference on
Recent Advances in Soft Computing",
year = "2002",
editor = "Ahmad Lotfi and Jon Garibaldi and Robert John",
pages = "182--188",
address = "Nottingham, United Kingdom",
publisher_address = "Nottingham, United Kingdom",
month = "12-13 " # dec,
publisher = "The Nottingham Trent University",
keywords = "genetic algorithms, genetic programming, Time series,
sequence mining, rule discovery, pattern matching
hardware",
ISBN = "1-84233-076-4",
URL = "http://hetland.org/research/2002/sc2103.pdf",
URL = "http://citeseer.ist.psu.edu/549830.html",
abstract = "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.",
notes = "RASC http://www.rasc2002.info/ See also
\cite{hetland:2005:ML}",
}
@Article{hetland:2005:ML,
author = "Magnus Lie Hetland and Pal Saetrom",
title = "Evolutionary Rule Mining in Time Series Databases",
journal = "Machine Learning",
year = "2005",
volume = "58",
number = "2-3",
pages = "107--125",
month = feb,
keywords = "genetic algorithms, genetic programming, sequence
mining, knowledge discovery, time series, specialised
hardware",
ISSN = "0885-6125",
doi = "doi:10.1007/s10994-005-5823-8",
abstract = "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.",
}
@Misc{Hewgill:Final,
author = "Adam Hewgill",
title = "{COSC} 4{P77} Final Project Improvements to lilgp
Genetic Programming System",
howpublished = "www",
note = "Brock Strongly Typed lilgp",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cosc.brocku.ca/Offerings/5P71/bstlilgp/bstlilgp_unix/lilgp%20Improvments.pdf",
size = "11 pages",
notes = "bstlilgp-0.5.0
http://www.cosc.brocku.ca/Offerings/5P71/bstlilgp/bstlilgp_unix/bstlilgp-0.5.0.zip",
}
@InProceedings{hewgill:2002:gecco:lbp,
title = "Real-Time Competitive Evolutionary Computation",
author = "Adam Hewgill",
booktitle = "Late Breaking Papers at the Genetic and Evolutionary
Computation Conference ({GECCO-2002})",
editor = "Erick Cant{\'u}-Paz",
year = "2002",
month = jul,
pages = "228--232",
address = "New York, NY",
publisher = "AAAI",
publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
keywords = "genetic algorithms, genetic programming, alife",
URL = "http://www.cosc.brocku.ca/files/downloads/research/cs0217.pdf",
notes = "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",
}
@TechReport{hewgill:2003:06,
author = "Adam Hewgill and Brian J. Ross",
title = "Procedural 3{D} Texture Synthesis Using Genetic
Programming",
institution = "Brock University, Department of Computer Science",
year = "2003",
type = "Technical Report",
number = "CS-03-06",
address = "St. Catharines, Ontario, Canada L2S 3A1",
month = apr # " 2003",
keywords = "genetic algorithms, genetic programming, procedural
textures, evolution",
URL = "http://www.cosc.brocku.ca/files/downloads/research/cs0306.pdf",
URL = "http://citeseer.ist.psu.edu/559621.html",
abstract = "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.",
notes = "see also \cite{hewgill:2004:CG}",
size = "26 pages",
}
@InProceedings{hewgill:gecco03lbp,
title = "The Evolution of {3D} Procedural Textures",
pages = "146--147",
author = "Adam Hewgill and Brian J. Ross",
year = "2003",
address = "Chicago, USA",
month = "12 " # jul,
editor = "Bart Rylander",
keywords = "genetic algorithms, genetic programming, STGP",
URL = "http://adamhewgill.com/research/gen3d_LBP.pdf",
booktitle = "Genetic and Evolutionary Computation Conference Late
Breaking Papers",
notes = "GECCO-2003LB, lilgp",
}
@Article{hewgill:2004:CG,
author = "Adam Hewgill and Brian J. Ross",
title = "Procedural {3D} Texture Synthesis Using Genetic
Programming",
journal = "Computers and Graphics",
year = "2004",
volume = "28",
number = "4",
pages = "569--584",
month = aug,
keywords = "genetic algorithms, genetic programming, Procedural
textures, Evolution, grammar BNF",
URL = "http://www.cosc.brocku.ca/~bross/research/HewgillRoss04.pdf",
URL = "http://www.sciencedirect.com/science/article/B6TYG-4CS4FCT-1/2/b8a5d381a1371ba6545d194a470dfa89",
ISSN = "0097-8493",
doi = "doi:10.1016/j.cag.2004.04.012",
abstract = "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.",
notes = "\cite{hewgill: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",
}
@InCollection{hewlett:1998:RNUGPVFDO,
author = "William R. Hewlett",
title = "Reynolds Numbers: Using Genetic Programming and Vite
to find Formulas to Describe Organizations",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1998",
year = "1998",
editor = "John R. Koza",
pages = "20--28",
address = "Stanford, California, 94305-3079 USA",
month = "17 " # mar,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-212568-8",
notes = "part of \cite{koza:1998:GAGPs}",
}
@InProceedings{heywood:2000:rbGPFPGA,
author = "M. I. Heywood and A. N. Zincir-Heywood",
title = "Register Based Genetic Programming on {FPGA} Computing
Platforms",
booktitle = "Genetic Programming, Proceedings of EuroGP'2000",
year = "2000",
editor = "Riccardo Poli and Wolfgang Banzhaf and William B.
Langdon and Julian F. Miller and Peter Nordin and
Terence C. Fogarty",
volume = "1802",
series = "LNCS",
pages = "44--59",
address = "Edinburgh",
publisher_address = "Berlin",
month = "15-16 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-67339-3",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1802&spage=44",
URL = "http://users.cs.dal.ca/~mheywood/X-files/Publications/EuroGP-2k0.pdf",
abstract = "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.",
notes = "EuroGP'2000, part of
\cite{poli:2000:GP}
http://users.cs.dal.ca/~mheywood/X-files/Publications/EuroGP-2k0.pdf
has additional revisions.",
}
@InProceedings{Heywood:2000:PBGP,
author = "M. I. Heywood and A. N. Zincir-Heywood",
title = "Page-based linear genetic programming",
booktitle = "Systems, Man, and Cybernetics, 2000 IEEE International
Conference",
year = "2000",
volume = "5",
pages = "3823--3828",
address = "IEEE Press",
month = "8-11 " # oct,
keywords = "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",
ISBN = "0-7803-6583-6",
URL = "http://ieeexplore.ieee.org/iel5/7099/19140/00886606.pdf?isNumber=19140",
size = "6 pages",
abstract = "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.",
}
@Article{heywood:2002:SMCB,
author = "M. I. Heywood and A. N. Zincir-Heywood",
title = "Dynamic Page Based Crossover in Linear Genetic
Programming",
journal = "IEEE Transactions on Systems, Man, and Cybernetics:
Part B - Cybernetics",
year = "2002",
volume = "32",
number = "3",
pages = "380--388",
month = jun,
email = "mheywood@cs.dal.ca",
keywords = "genetic algorithms, genetic programming, linear
genetic programming",
ISSN = "1083-4419",
abstract = "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.",
}
@TechReport{hiden:1996:npcaGP,
author = "H. G. Hiden and M. J. Willis and P. Turner and M. T.
Tham and G. A. Montague",
title = "Non-linear Principal Components Analysis Using Genetic
Programming",
institution = "Chemical Engineering, Newcastle University",
year = "1996",
address = "UK",
note = "Extended Abstract, ICANNGA '97, Norwich, UK",
keywords = "genetic algorithms, genetic programming",
broken = "http://lorien.ncl.ac.uk/sorg/paper9a.ps",
abstract = "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.",
notes = "MSword postscript not compatible with unix.
",
}
@InProceedings{hiden:1997:ndddmGP,
author = "Hugo Hiden and Mark Willis and Ben McKay and Gary
Montague",
title = "Non-Linear And Direction Dependent Dynamic Modelling
Using Genetic Programming",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "168--173",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/hiden_1997_ndddmGP.pdf",
size = "6 pages",
notes = "GP-97",
}
@InProceedings{hinden:1997:npcaGAL,
author = "Hugo Hiden and Mark Willis and Ming Tham and Paul
Turner and Gary Montague",
title = "Non-Linear Principal Components Analysis using Genetic
Programming",
booktitle = "Second International Conference on Genetic Algorithms
in Engineering Systems: Innovations and Applications,
GALESIA",
year = "1997",
editor = "Ali Zalzala",
pages = "302--307",
address = "University of Strathclyde, Glasgow, UK",
publisher_address = "Savoy Place, London WC2R 0BL, UK",
month = "1-4 " # sep,
publisher = "Institution of Electrical Engineers",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-85296-693-8",
broken = "http://lorien.ncl.ac.uk/sorg/paper13.ps",
URL = "http://scitation.aip.org/getpdf/servlet/GetPDFServlet?filetype=pdf&id=IEECPS0019970CP446000302000001&idtype=cvips&prog=normal",
doi = "doi:10.1049/cp:19971197",
size = "6 pages",
abstract = "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.",
notes = "GALESIA'97
see also \cite{hiden:1999:CCE}",
}
@InProceedings{hiden:1997:GPndmcps,
author = "H. G. Hiden and M. J. Willis and G. A. Montague",
title = "Using Genetic Programming to Develop Non-Linear
Dynamic Models of Chemical Process Systems",
booktitle = "IChemE Jubilee Research Event",
year = "1997",
volume = "2",
pages = "789--792",
address = "Nottingham, UK",
month = "8-9 " # apr,
organisation = "Institute of Chemical Engineers",
keywords = "genetic algorithms, genetic programming",
notes = "Comparison of GP with feedforward ANN and finite
Impulse response model",
}
@InProceedings{hiden:1998:plsGP,
author = "Hugo Hiden and Ben McKay and Mark Willis and Gary
Montague",
title = "Non-Linear Partial Least Squares using Genetic
Programming",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "128--133",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-548-7",
notes = "GP-98",
}
@Article{hiden:1999:CCE,
author = "H. G. Hiden and M. J. Willis and M. T. Tham and G. A.
Montague",
title = "Non-linear principal components analysis using genetic
programming",
journal = "Computers and Chemical Engineering",
year = "1999",
volume = "23",
number = "3",
pages = "413--425",
month = "28 " # feb,
keywords = "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",
doi = "doi:10.1016/S0098-1354(98)00284-1",
size = "13 pages",
abstract = "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.",
notes = "Matlab, Maple, pop=60",
}
@PhdThesis{Hiden:thesis,
author = "Hugo George Hiden",
title = "Data-based modelling using genetic programming",
school = "University of Newcastle upon Tyne",
year = "1998",
keywords = "genetic algorithms, genetic programming",
size = "pages",
notes = "Ming Tham - I don't think the thesis is online.",
}
@InProceedings{higuchi:1994:evaa,
author = "Tetsuya Higuchi and Hitoshi Iba and Bernard
Manderick",
title = "Applying Evolvable Hardware to Autonomous Agents",
booktitle = "Parallel Problem Solving from Nature III",
year = "1994",
editor = "Yuval Davidor and Hans-Paul Schwefel and Reinhard
M{\"a}nner",
series = "LNCS",
volume = "866",
pages = "524--533",
address = "Jerusalem",
publisher_address = "Berlin, Germany",
month = "9-14 " # oct,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, reinforcement learning, Evovable
Hardware",
ISBN = "3-540-58484-6",
URL = "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-58484-6",
doi = "doi:10.1007/3-540-58484-6_295",
size = "10 pages",
abstract = "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.",
notes = "Describes software reconfigurable logic device which
changes its own hardware to adapt to its
environment.
PPSN3",
}
@InProceedings{hikage:1998:cemse,
author = "Tomofumi Hikage and Hitoshi Hemmi and Katsunori
Shimohara",
title = "Comparison of Evolutionary Methods for Smoother
Evolution",
booktitle = "Proceedings of the Second International Conference on
Evolvable Systems: From Biology to Hardware (ICES 98)",
year = "1998",
editor = "Moshe Sipper and Daniel Mange and Andres Perez-Uribe",
volume = "1478",
series = "LNCS",
pages = "115--124",
address = "Lausanne, Switzerland",
publisher_address = "Berlin",
month = "23-25 " # sep,
publisher = "Springer Verlag",
keywords = "genetic algorithms, genetic programming, HDL",
ISBN = "3-540-64954-9",
URL = "http://link.springer.de/link/service/series/0558/papers/1478/14780115.pdf",
size = "8 pages",
abstract = "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.",
notes = "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",
}
@InProceedings{Hildebrandt:2010:gecco,
author = "Torsten Hildebrandt and Jens Heger and Bernd
Scholz-Reiter",
title = "Towards improved dispatching rules for complex shop
floor scenarios: a genetic programming approach",
booktitle = "GECCO '10: Proceedings of the 12th annual conference
on Genetic and evolutionary computation",
year = "2010",
editor = "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",
isbn13 = "978-1-4503-0072-8",
pages = "257--264",
keywords = "genetic algorithms, genetic programming, Combinatorial
optimization and metaheuristics",
month = "7-11 " # jul,
organisation = "SIGEVO",
address = "Portland, Oregon, USA",
doi = "doi:10.1145/1830483.1830530",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "hyperheuristic
Also known as \cite{1830530} GECCO-2010 A joint meeting
of the nineteenth international conference on genetic
algorithms (ICGA-2010) and the fifteenth annual genetic
programming conference (GP-2010)",
}
@InProceedings{1274014,
author = "James A. Hilder and Andy M. Tyrrell",
title = "An evolutionary platform for developing
next-generation electronic circuits",
booktitle = "Late breaking paper at Genetic and Evolutionary
Computation Conference {(GECCO'2007)}",
year = "2007",
month = "7-11 " # jul,
editor = "Peter A. N. Bosman",
isbn13 = "978-1-59593-698-1",
pages = "2483--2488",
address = "London, United Kingdom",
keywords = "genetic algorithms, genetic programming, EHW, analogue
circuit design, genetic algorithms, SPICE",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2483.pdf",
doi = "doi:10.1145/1274000.1274014",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Distributed on CD-ROM at GECCO-2007 ACM Order No.
910071",
}
@InProceedings{Hilder:2009:PRIME,
author = "James A. Hilder and James Alfred Walker and Andy M.
Tyrrell",
title = "Designing variability tolerant logic using
evolutionary algorithms",
booktitle = "Research in Microelectronics and Electronics, PRIME
2009. Ph.D.",
year = "2009",
month = "12-17 " # jul,
pages = "184--187",
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/RME.2009.5201345",
notes = "Also known as \cite{5201345}",
}
@InProceedings{Hilder:2010:AHS,
author = "James Hilder and James A. Walker and Andy Tyrrell",
title = "Use of a multi-objective fitness function to improve
cartesian genetic programming circuits",
booktitle = "2010 NASA/ESA Conference on Adaptive Hardware and
Systems (AHS)",
year = "2010",
month = "15-18 " # jun,
pages = "179--185",
abstract = "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.",
keywords = "genetic algorithms, genetic programming, cartesian
genetic programming",
doi = "doi:10.1109/AHS.2010.5546262",
notes = "Hex to 7-Segment Display Driver. Also known as
\cite{5546262}",
}
@Article{Hill:2007:JH,
author = "David J. Hill and Barbara S. Minsker and Albert J.
Valocchi and Vladan Babovic and Maarten Keijzer",
title = "Upscaling models of solute transport in porous media
through genetic programming",
journal = "Journal of Hydroinformatics",
year = "2007",
volume = "9",
number = "4",
pages = "251--266",
publisher = "IWA Publishing",
keywords = "genetic algorithms, genetic programming, data-driven
modeling, knowledge discovery, solute transport",
ISSN = "1464-7141",
URL = "http://www.iwaponline.com/jh/009/0251/0090251.pdf",
doi = "doi:10.2166/hydro.2007.028",
size = "16 pages",
abstract = "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.",
notes = "Synthetic aquifers, ALP, p264 'GP.. produce
mathematical models that researchers can understand.'",
}
@PhdThesis{HillDissertation,
author = "David J. Hill",
title = "Data Mining Approaches to Complex Environmental
Problems",
school = "Environmental Engineering in Civil Engineering,
University of Illinois at Urbana-Champaign",
year = "2007",
address = "Urbana, Illinois, USA",
month = "23 " # jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://gaia.rutgers.edu/docs/HillDissertation.pdf",
size = "195 pages",
abstract = "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.",
}
@InProceedings{Hill:2010:ICEC,
author = "Seamus Hill and Colm O'Riordan",
title = "A Genetic Algorithm with a multi-layered
Genotype-Phenotype Mapping",
booktitle = "Proceedings of the International Conference on
Evolutionary Computation (ICEC 2010)",
year = "2010",
editor = "Agostinho Rosa",
address = "Valencia, Spain",
month = "24-26 " # oct,
keywords = "genetic algorithms",
abstract = "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.",
notes = "GP?
http://www.icec.ijcci.org/ICEC2010/home.asp
http://www.ecta.ijcci.org/Abstracts/2010/ICEC_2010_Abstracts.htm",
}
@InProceedings{Hill:2011:EtuoaNFGMiGAtIPVoDUL,
title = "Examining the use of a Non-Trivial Fixed
Genotype-Phenotype Mapping in Genetic Algorithms to
Induce Phenotypic Variability over Deceptive Uncertain
Landscapes",
author = "Seamus Hill and Colm O'Riordan",
pages = "1403--1410",
booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary
Computation",
year = "2011",
editor = "Alice E. Smith",
month = "5-8 " # jun,
address = "New Orleans, USA",
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming,
Representation and operators",
abstract = "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.",
notes = "CEC2011 sponsored by the IEEE Computational
Intelligence Society, and previously sponsored by the
EPS and the IET.",
}
@Misc{Hillar:2009:eureqa,
author = "Christopher J. Hillar and Friedrich T. Sommer",
title = "On the article ``Distilling free-form natural laws
from experimental data''",
howpublished = "www",
year = "2009",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.msri.org/people/members/chillar/files/hs09b.pdf",
size = "4 pages",
abstract = "A recent paper \cite{Science09: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)...",
notes = "See also \cite{Schmidt:2009:rebuttal}",
}
@InCollection{alife92:hillis,
author = "W. Daniel Hillis",
title = "Co-evolving Parasites Improve Simulated Evolution as
an Optimization Procedure",
booktitle = "Artificial Life II",
publisher = "Addison-Wesley",
year = "1992",
pages = "313--324",
month = feb # " 1990",
address = "Santa Fe Institute, New Mexico, USA",
editor = "Christopher G. Langton and Charles E. Taylor and J.
Doyne Farmer and Steen Rasmussen",
volume = "X",
keywords = "genetic algorithms",
series = "Santa Fe Institute Studies in the Sciences of
Complexity",
abstract = "Evolves sorting networks. Tests evolved at same time
lead to better solutions. Also aim to reduced testing
effort.",
notes = "Not in index, see page 313-324",
size = "12 pages",
}
@TechReport{hinchcliffe:1996:c2GPcpsm,
author = "Mark Hinchliffe and Mark Willis and Hugo Hiden and
Ming Tham",
title = "A comparison of two Genetic Programming Algorithms
Applied to Chemical Process Systems Modelling",
institution = "Chemical Engineering, Newcastle University",
year = "1996",
address = "UK",
note = "Extended Abstract, submitted to: ICANNGA '97, Norwick,
UK",
keywords = "genetic algorithms, genetic programming",
broken = "http://lorien.ncl.ac.uk/sorg/paper10a.ps",
abstract = "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.",
notes = "MSword postscript not camptible with unix",
size = "7 pages",
}
@InProceedings{hinchliffe:1996:mcpsm-g,
author = "Mark Hinchliffe and Hugo Hiden and Ben McKay and Mark
Willis and Ming Tham and Geoffery Barton",
title = "Modelling Chemical Process Systems Using a Multi-Gene
Genetic Programming Algorithm",
booktitle = "Late Breaking Papers at the Genetic Programming 1996
Conference Stanford University July 28-31, 1996",
year = "1996",
editor = "John R. Koza",
pages = "56--65",
address = "Stanford University, CA, USA",
publisher_address = "Stanford University, Stanford, California
94305-3079, USA",
month = "28--31 " # jul,
publisher = "Stanford Bookstore",
ISBN = "0-18-201031-7",
keywords = "genetic algorithms, genetic programming",
broken = "http://lorien.ncl.ac.uk/sorg/paper7.ps",
abstract = "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.",
notes = "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",
}
@InProceedings{hinchliffe:1998:cpsmumoGP,
author = "Mark Hinchliffe and Mark Willis and Ming Tham",
title = "Chemical Process Sytems Modelling Using
Multi-objective Genetic Programming",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "134--139",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, MOGP",
ISBN = "1-55860-548-7",
notes = "GP-98",
}
@InProceedings{hinchliffe:1999:DCPMUMBFGPA,
author = "Mark Hinchliffe and Mark Willis and Ming Tham",
title = "Dynamic Chemical Process Modelling Using a Multiple
Basis Function Genetic Programming Algorithm",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1782",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, real world
applications, poster papers, NARMAX",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-746.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-746.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@PhdThesis{hinchliffe:thesis,
author = "Mark P. Hinchliffe",
title = "Dynamic Modelling Using Genetic Programming",
school = "School of Chemical Engineering and Advanced Materials,
University of Newcastle upon Tyne",
year = "2001",
address = "UK",
month = sep,
keywords = "genetic algorithms, genetic programming, MOGA, MOGP,
SOGP",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/hinchliffe:Thesis.pdf",
URL = "http://www.ncl.ac.uk/ceam/postgrad/pg-theses.htm",
size = "205 pages",
abstract = "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.",
notes = "{"}the results do not provide sufficient evidence to
suggest that GP will become as widely used as neural
network modelling techniques.{"} page 160.",
}
@InProceedings{oai:CiteSeerPSU:263745,
author = "Mark Hinchliffe and Mark Willis and Ming Tham and Gary
Montague",
title = "Dynamic Chemical Process Modelling Using a Multiple
Basis Function Genetic Programming Algorithm",
booktitle = "Nineteenth IASTED International Conference, Modelling,
Identification and Control",
year = "2000",
address = "Innsbruck, Austria",
month = feb # " 14-17",
keywords = "genetic algorithms, genetic programming",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:263745",
rights = "unrestricted",
URL = "http://citeseer.ist.psu.edu/rd/13718071%2C263745%2C1%2C0.25%2CDownload/http://citeseer.ist.psu.edu/cache/papers/cs/12773/http:zSzzSzwww.iasted.comzSzconferenceszSz2000zSzaustriazSzabstractszSz306-089.pdf/dynamic-chemical-process-modelling.pdf",
URL = "http://citeseer.ist.psu.edu/263745.html",
notes = "cited in \cite{hinchliffe:thesis}",
}
@InProceedings{hinchliffe:2002:IFAC,
author = "M. Hinchliffe and M. Willis",
title = "Dynamic Modelling Using Genetic Programming",
booktitle = "Proceedings of the 15th IFAC World Congress",
year = "2002",
address = "Barcelona, Spain",
keywords = "genetic algorithms, genetic programming",
notes = "cited in \cite{hinchliffe:thesis}",
}
@Article{Hinchliffe:2003:CCE,
author = "Mark P. Hinchliffe and Mark J. Willis",
title = "Dynamic systems modelling using genetic programming",
journal = "Computers \& Chemical Engineering",
year = "2003",
volume = "27",
pages = "1841--1854",
number = "12",
owner = "wlangdon",
keywords = "genetic algorithms, genetic programming, Neural
networks, Dynamic modelling, Multi-objective",
ISSN = "0098-1354",
URL = "http://www.sciencedirect.com/science/article/B6TFT-49MDYGW-2/2/742bcc7f22240c7a0381027aa5ff7e73",
doi = "doi:10.1016/j.compchemeng.2003.06.001",
abstract = "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.",
}
@InProceedings{Hingston:2011:RTwC,
title = "Red Teaming with Coevolution",
author = "Philip Hingston and Mike Preuss",
pages = "1160--1168",
booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary
Computation",
year = "2011",
editor = "Alice E. Smith",
month = "5-8 " # jun,
address = "New Orleans, USA",
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming,
Coevolutionary systems, Evolutionary simulation-based
optimization, Real-world applications",
abstract = "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.",
notes = "CEC2011 sponsored by the IEEE Computational
Intelligence Society, and previously sponsored by the
EPS and the IET.",
}
@InProceedings{hirasawa:2001:cgnpgp,
author = "Kotaro Hirasawa and M. Okubo and J. Hu and J. Murata",
title = "Comparison between Genetic Network Programming ({GNP})
and Genetic Programming ({GP})",
booktitle = "Proceedings of the 2001 Congress on Evolutionary
Computation CEC2001",
year = "2001",
pages = "1276--1282",
address = "COEX, World Trade Center, 159 Samseong-dong,
Gangnam-gu, Seoul, Korea",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "27-30 " # may,
organisation = "IEEE Neural Network Council (NNC), Evolutionary
Programming Society (EPS), Institution of Electrical
Engineers (IEE)",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, genetic
programming Network, Evolution, Ant behaviors",
ISBN = "0-7803-6658-1",
notes = "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.",
}
@InProceedings{Hirayama:2008:gecco,
author = "Yoshikazu Hirayama and Tim Clarke and Julian Francis
Miller",
title = "Fault tolerant control using Cartesian genetic
programming",
booktitle = "GECCO '08: Proceedings of the 10th annual conference
on Genetic and evolutionary computation",
year = "2008",
editor = "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",
isbn13 = "978-1-60558-130-9",
pages = "1523--1530",
address = "Atlanta, GA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1523.pdf",
doi = "doi:10.1145/1389095.1389389",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "12-16 " # jul,
keywords = "genetic algorithms, genetic programming, cartesian
genetic programming, Fault Tolerance robotics,
Real-World application",
notes = "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 \cite{1389389}",
}
@InProceedings{Hiroyasu:2010:CISP,
author = "Tomoyuki Hiroyasu and Sosuke Fujita and Akihito
Watanabe and Mitsunori Miki and Maki Ogura and Manabu
Fukumoto",
title = "Comparison of {GP} and {SAP} in the image-processing
filter construction using pathology images",
booktitle = "3rd International Congress on Image and Signal
Processing (CISP 2010)",
year = "2010",
month = "16-18 " # oct,
volume = "2",
pages = "904--908",
abstract = "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.",
keywords = "genetic algorithms, genetic programming, GP, SAP,
image processing filter construction, medical image
processing, pathology images, simulated annealing
programming, medical image processing, simulated
annealing",
doi = "doi:10.1109/CISP.2010.5646895",
notes = "'GP can derive the best solution with less evaluation
time than SAP.' Also known as \cite{5646895}",
}
@InProceedings{GPandIPDpaper1999Hirsch,
author = "Laurence Hirsch and Masoud Saeedi",
title = "Modelling exchange using the prisoner's dilemma and
genetic programming",
booktitle = "Proceedings of the Computer Society of Iran Computing
Conference",
year = "1999",
editor = "Rasool Jalili",
address = "Sharif University of Technology, Tehran, Iran",
month = "26-28 " # jan,
keywords = "genetic algorithms, genetic programming",
URL = "http://shura.shu.ac.uk/id/eprint/3809",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/GPandIPDpaper1999Hirsch.pdf",
size = "8 pages",
abstract = "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.",
notes = "CSICC 98 http://persia.org/Conferences/conf3/cp.html",
}
@InProceedings{hirsch:2004:eurogp,
author = "Laurence Hirsch and Masoud Saeedi and Robin Hirsch",
title = "Evolving Text Classifiers with Genetic Programming",
booktitle = "Genetic Programming 7th European Conference, EuroGP
2004, Proceedings",
year = "2004",
editor = "Maarten Keijzer and Una-May O'Reilly and Simon M.
Lucas and Ernesto Costa and Terence Soule",
volume = "3003",
series = "LNCS",
pages = "309--317",
address = "Coimbra, Portugal",
publisher_address = "Berlin",
month = "5-7 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-21346-5",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=309",
abstract = "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.",
notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in
conjunction with EvoCOP2004 and EvoWorkshops2004",
}
@InProceedings{eurogp:HirschSH05,
author = "Laurence Hirsch and Masoud Saeedi and Robin Hirsch",
editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre
Collet and Jano I. {van Hemert} and Marco Tomassini",
title = "Evolving Rules for Document Classification",
booktitle = "Proceedings of the 8th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3447",
year = "2005",
address = "Lausanne, Switzerland",
month = "30 " # mar # " - 1 " # apr,
organisation = "EvoNet",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-25436-6",
pages = "85--95",
URL = "http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=85",
doi = "doi:10.1007/b107383",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in
conjunction with EvoCOP2005 and EvoWorkshops2005",
}
@Article{journals/aai/HirschSH05,
title = "Evolving Text Classification Rules with Genetic
Programming",
author = "Laurence Hirsch and Masoud Saeedi and Robin Hirsch",
journal = "Applied Artificial Intelligence",
year = "2005",
number = "7",
volume = "19",
pages = "659--676",
month = aug,
bibdate = "2005-12-01",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/aai/aai19.html#HirschSH05",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.journalsonline.tandf.co.uk/openurl.asp?genre=article&issn=0883-9514&volume=19&issue=7&spage=659",
doi = "doi:10.1080/08839510590967307",
abstract = "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.",
}
@InProceedings{1277279,
author = "Laurence Hirsch and Robin Hirsch and Masoud Saeedi",
title = "Evolving Lucene search queries for text
classification",
booktitle = "GECCO '07: Proceedings of the 9th annual conference on
Genetic and evolutionary computation",
year = "2007",
editor = "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",
volume = "2",
isbn13 = "978-1-59593-697-4",
pages = "1604--1611",
address = "London",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1604.pdf",
doi = "doi:10.1145/1276958.1277279",
publisher = "ACM Press",
publisher_address = "New York, NY, USA",
month = "7-11 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, apache
lucene, text classification",
abstract = "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.",
notes = "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",
}
@InProceedings{Hirsch:2010:cec,
author = "Laurie Hirsch",
title = "Evolved Apache Lucene SpanFirst queries are good text
classifiers",
booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-6910-9",
abstract = "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.",
doi = "doi:10.1109/CEC.2010.5585955",
notes = "WCCI 2010. Also known as \cite{5585955}",
}
@Article{hirsh:2000:GP,
author = "Haym Hirsh and Wolfgang Banzhaf and John R. Koza and
Conor Ryan and Lee Spector and Christian Jacob",
title = "Genetic Programming",
journal = "IEEE Intelligent Systems",
year = "2000",
volume = "15",
number = "3",
pages = "74--84",
month = may # "-" # jun,
keywords = "genetic algorithms, genetic programming, artificial
computer code evolution, machine intelligence,
automatic programming, arbitrary computational
processes",
ISSN = "1094-7167",
URL = "http://ieeexplore.ieee.org/iel5/5254/18363/00846288.pdf",
size = "11 pages",
abstract = "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.",
notes = "Collection of essays by each author with introduction
by Hirsh. See \cite{banzhaf:2000:IS},
\cite{koza:2000:IS}, \cite{ryan:2000:IS},
\cite{spector:2000:IS}, jacob:2000:IS.",
}
@InProceedings{Hiruma:2011:EaERTG,
title = "Evolving an Effective Robot Tour Guide",
author = "Hideru Hiruma and Alex Fukunaga and Kazuki Komiya and
Hitoshi Iba",
pages = "137--144",
booktitle = "Proceedings of the 2011 IEEE Congress on Evolutionary
Computation",
year = "2011",
editor = "Alice E. Smith",
month = "5-8 " # jun,
address = "New Orleans, USA",
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming, Evolutionary
Robotics, Robotics, Emerging areas",
abstract = "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.",
notes = "CEC2011 sponsored by the IEEE Computational
Intelligence Society, and previously sponsored by the
EPS and the IET.",
}
@InCollection{ho:1994:gqo,
author = "Alex Ho and George Lumpkin",
title = "The Genetic Query Optimizer",
booktitle = "Genetic Algorithms at Stanford 1994",
year = "1994",
editor = "John R. Koza",
pages = "67--76",
address = "Stanford, California, 94305-3079 USA",
month = dec,
organisation = "Stanford University",
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, Oracle Corporation, Relational
Database Query",
ISBN = "0-18-187263-3",
abstract = "{"}For complex queries, we find that the genetic
algorithm produces more efficient query plans in a
running time comparable to that of conventional
methods{"}.",
notes = "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",
}
@InProceedings{Ho:2009:ispimrc,
author = "Lester T. W. Ho and Imran Ashraf and Holger Claussen",
title = "Evolving femtocell coverage optimization algorithms
using genetic programming",
booktitle = "IEEE 20th International Symposium on Personal, Indoor
and Mobile Radio Communications",
year = "2009",
month = sep,
pages = "2132--2136",
abstract = "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.",
keywords = "genetic algorithms, genetic programming, distributed
algorithm, enterprise environment, femtocell coverage
optimization, self-configuration capability,
self-optimisation capability, cellular radio",
doi = "doi:10.1109/PIMRC.2009.5450062",
notes = "Bell Labs., Alcatel-Lucent, Swindon, UK. Also known as
\cite{5450062}",
}
@InProceedings{ho:1999:AEGMEA,
author = "Shinn-Ying Ho and Xiao-I Chang",
title = "An Efficient Generalized Multiobjective Evolutionary
Algorithm",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "871--878",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "evolution strategies and evolutionary programming",
ISBN = "1-55860-611-4",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{ho:1999:IGANICUOA,
author = "Shinn-Ying Ho and Li-Sun Shu and Hung-Ming Chen",
title = "Intelligent Genetic Algorithm with a New Intelligent
Crossover Using Orthogonal Arrays",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "289--296",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{ho:1999:SLKBPPUIGA,
author = "Shinn-Ying Ho and Hung-Ming Chen and Li-Sun Shu",
title = "Solving Large Knowledge Base Partitioning Problems
Using an Intelligent Genetic Algorithm",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1567--1572",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "real world applications",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-747NEW.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-747NEW.pdf",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@Article{ho:2006:biosystems,
author = "Shinn-Ying Ho and Chih-Hung Hsieh and Hung-Ming Chen
and Hui-Ling Huang",
title = "Interpretable gene expression classifier with an
accurate and compact fuzzy rule base for microarray
data analysis",
journal = "Biosystems",
year = "2006",
volume = "85",
number = "3",
pages = "165--176",
month = sep,
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1016/j.biosystems.2006.01.002",
abstract = "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.",
notes = "PMID: 16490299 [PubMed - in process]",
}
@InProceedings{Nguyen:2001:ADFA-csc,
author = "X. H. Nguyen and R. I. (Bob) McKay",
booktitle = "Post-graduate ADFA Conference on Computer Science",
address = "Canberra, Australia",
notes = "Refereed Regional and National Conference and Workshop
Papers",
pages = "93--100",
title = "A Framework for Tree-adjunct Grammar Guided Genetic
Programming",
URL = "http://sc.snu.ac.kr/PAPERS/TAG3P.pdf",
year = "2001",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{hoai:2001:AJWIES,
author = "N. X. Hoai",
title = "Solving the Symbolic Regression with Tree-Adjunct
Grammar Guided Genetic Programming: The Preliminary
Results",
booktitle = "Australasia-Japan Workshop on Intelligent and
Evolutionary Systems",
year = "2001",
editor = "Nikola Kasabov and Peter Whigham",
address = "University of Otago, Dunedin, New Zealand",
month = "19-21st " # nov,
keywords = "genetic algorithms, genetic programming",
notes = "http://divcom.otago.ac.nz/infosci/KEL/conferences/IESWorkshop/default.htm",
}
@InProceedings{hoai:2001:HIS,
title = "Solving Trignometric Identities with Tree Adjunct
Grammar Guided Genetic Programming",
author = "N. X. Hoai",
editor = "Ajith Abraham and Mario Koppen",
booktitle = "2001 International Workshop on Hybrid Intelligent
Systems",
series = "LNCS",
pages = "339--352",
publisher = "Springer-Verlag",
address = "Adelaide, Australia",
publisher_address = "Berlin",
month = "11-12 " # dec,
year = "2001",
email = "x.nguyen@student.adfa.edu.au",
broken = "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-7908-1480-6",
URL = "http://www.amazon.com/Hybrid-Information-Systems-Ajith-Abraham/dp/3790814806/ref=sr_1_8?s=books&ie=UTF8&qid=1326475568&sr=1-8",
ISBN = "3-7908-1480-6",
keywords = "genetic algorithms, genetic programming, Grammar
Guided Genetic Progrogramming, Tree-Adjunct Grammars,
Trigonometric Identity Discovery",
abstract = "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.",
notes = "HIS01",
}
@Article{Nguyen:2001:AJIIPS,
author = "X. H. Nguyen and R. I. (Bob) McKay and D. L. Essam",
journal = "The Australian Journal of Intelligent Information
Processing Systems",
number = "3/4",
pages = "114--121",
title = "Solving the Symbolic Regression Problem with
Tree-Adjunct Grammar Guided Genetic Programming: The
Comparative Results",
URL = "http://sc.snu.ac.kr/PAPERS/xuanetal.pdf",
volume = "7",
year = "2001",
keywords = "genetic algorithms, genetic programming",
size = "6 pages",
abstract = "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.",
}
@InProceedings{hoai:2002:EuroGP,
title = "Some Experimental Results with Tree Adjunct Grammar
Guided Genetic Programming",
author = "Nguyen Xuan Hoai and R. I. McKay and D. Essam",
editor = "James A. Foster and Evelyne Lutton and Julian Miller
and Conor Ryan and Andrea G. B. Tettamanzi",
booktitle = "Genetic Programming, Proceedings of the 5th European
Conference, EuroGP 2002",
volume = "2278",
series = "LNCS",
pages = "228--237",
publisher = "Springer-Verlag",
address = "Kinsale, Ireland",
publisher_address = "Berlin",
month = "3-5 " # apr,
year = "2002",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-43378-3",
URL = "http://link.springer-ny.com/link/service/series/0558/bibs/2278/22780228.htm",
URL = "http://link.springer-ny.com/link/service/series/0558/papers/2278/22780228.pdf",
abstract = "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.",
notes = "EuroGP'2002, part of \cite{lutton:2002:GP}",
}
@InProceedings{Nguyen:2002:ICCCS,
publisher_address = "Piscataway, NJ, USA",
author = "X. H. Nguyen and R. I. (Bob) McKay and D. L. Essam",
booktitle = "IEEE International Conference on Communications,
Circuits and Systems",
address = "Chengdu, China",
month = jul,
notes = "Refereed International Conference Papers",
pages = "1113--1117",
publisher = "IEEE Press",
title = "Can Tree Adjunct Grammar Guided Genetic Programming be
Good at Finding a Needle in a Haystack? {A} Case
Study",
URL = "http://sc.snu.ac.kr/PAPERS/hoaietal.pdf",
volume = "2",
year = "2002",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{hoai:2002:stsrpwtgggptcr,
author = "N. X. Hoai and R. I. McKay and D. Essam and R. Chau",
title = "Solving the Symbolic Regression Problem with
Tree-Adjunct Grammar Guided Genetic Programming: The
Comparative Results",
booktitle = "Proceedings of the 2002 Congress on Evolutionary
Computation CEC2002",
editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
and Garry Greenwood and Hitoshi Iba and Paul Marrow and
Mark Shackleton",
pages = "1326--1331",
year = "2002",
publisher = "IEEE Press",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
organisation = "IEEE Neural Network Council (NNC), Institution of
Electrical Engineers (IEE), Evolutionary Programming
Society (EPS)",
ISBN = "0-7803-7278-6",
month = "12-17 " # may,
notes = "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)",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
}
@InProceedings{hoai:2002:SEAL,
author = "Nguyen Xuan Hoai and Yin Shan and Robert Ian McKay",
title = "Is Ambiguity Useful or Problematic for Grammar Guided
Genetic Programming?",
booktitle = "Procedings of the 4th Asia-Pacific Conference on
Simulated Evolution And Learning (SEAL'02)",
year = "2002",
editor = "Lipo Wang and Kay Chen Tan and Takeshi Furuhashi and
Jong-Hwan Kim and Xin Yao",
pages = "449--454",
address = "Orchid Country Club, Singapore",
month = "18-22 " # nov,
keywords = "genetic algorithms, genetic programming",
ISBN = "981-04-7522-5",
URL = "http://www.cs.adfa.edu.au/~shanyin/publications/ambiguity.pdf",
URL = "http://citeseer.ist.psu.edu/545311.html",
URL = "http://sc.snu.ac.kr/PAPERS/ambiguity.pdf",
abstract = "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.",
notes = "SEAL 2002",
notes = "Refereed International Conference Papers",
}
@InProceedings{hoai03,
author = "Nguyen Xuan Hoai and R. I. McKay and H. A. Abbass",
title = "Tree Adjoining Grammars, Language Bias, and Genetic
Programming",
booktitle = "Genetic Programming, Proceedings of EuroGP'2003",
year = "2003",
editor = "Conor Ryan and Terence Soule and Maarten Keijzer and
Edward Tsang and Riccardo Poli and Ernesto Costa",
volume = "2610",
series = "LNCS",
pages = "335--344",
address = "Essex",
publisher_address = "Berlin",
month = "14-16 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-00971-X",
URL = "http://www.cs.adfa.edu.au/~abbass/publications/hardcopies/TAG3P-EuroGp-03.pdf",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=2610&spage=335",
abstract = "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.",
notes = "EuroGP'2003 held in conjunction with EvoWorkshops
2003",
}
@InCollection{Nguyen:2004:IISA,
address = "Berlin, Germany",
author = "X. H. Nguyen and R. I. (Bob) McKay and D. L. Essam",
booktitle = "Innovations in Intelligent Systems and Applications",
editor = "A. Abraham and L. Jain and B. J. {van der Zwaag}",
ISBN = "3-540-20265-X",
isbn13 = "9783540202653",
month = jan,
notes = "Book Chapter",
pages = "221--236",
publisher = "Springer-Verlag",
series = "Springer Studies in Fuzziness and Soft Computing",
title = "Finding Trigonometric Identities with Tree Adjunct
Grammar Guided Genetic Programming",
URL = "http://sc.snu.ac.kr/PAPERS/trigonometry.pdf",
url1 = "http://www.springer.com/west/home/engineering?SGWID=4-175-22-13888495-detailsPage=ppmmedia|toc",
volume = "140",
year = "2004",
keywords = "genetic algorithms, genetic programming",
size = "18 pages",
abstract = "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.",
}
@InProceedings{nguyen:2004:eurogp,
author = "Nguyen Xuan Hoai and R. I. (Bob) McKay and Daryl Essam
and Hussein Abbass",
title = "Toward an Alternative Comparison between Different
Genetic Programming Systems",
booktitle = "Genetic Programming 7th European Conference, EuroGP
2004, Proceedings",
year = "2004",
editor = "Maarten Keijzer and Una-May O'Reilly and Simon M.
Lucas and Ernesto Costa and Terence Soule",
volume = "3003",
series = "LNCS",
pages = "67--77",
address = "Coimbra, Portugal",
publisher_address = "Berlin",
month = "5-7 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-21346-5",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=67",
abstract = "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.",
notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in
conjunction with EvoCOP2004 and EvoWorkshops2004",
}
@InProceedings{nguyen:2004:aiotroiadoitagggp,
title = "An Investigation on the Roles of Insertion and
Deletion Operators in Tree Adjoining Grammar Guided
Genetic Programming",
author = "Nguyen Xuan Hoai and Robert Ian McKay",
pages = "472--477",
booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary
Computation",
year = "2004",
publisher = "IEEE Press",
month = "20-23 " # jun,
address = "Portland, Oregon",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming, Theory of
evolutionary algorithms",
abstract = "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.",
notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and
the IEE.",
}
@InProceedings{hoai:sts:gecco2004,
author = "Nguyen Xuan Hoai and R. I. McKay",
title = "Softening the Structural Difficulty in Genetic
Programming with {TAG}-Based Representation and
Insertion/Deletion Operators",
booktitle = "Genetic and Evolutionary Computation -- GECCO-2004,
Part II",
year = "2004",
editor = "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",
series = "Lecture Notes in Computer Science",
pages = "605--616",
address = "Seattle, WA, USA",
publisher_address = "Heidelberg",
month = "26-30 " # jun,
organisation = "ISGEC",
publisher = "Springer-Verlag",
volume = "3103",
ISBN = "3-540-22343-6",
ISSN = "0302-9743",
URL = "http://link.springer.de/link/service/series/0558/bibs/3103/31030605.htm",
size = "12",
keywords = "genetic algorithms, genetic programming",
notes = "GECCO-2004 A joint meeting of the thirteenth
international conference on genetic algorithms
(ICGA-2004) and the ninth annual genetic programming
conference (GP-2004)",
}
@InProceedings{Nguyen:2004:APCSEL,
author = "Xuan Hoai Nguyen and R. I. (Bob) McKay and D. L. Essam
and H. A. Abbass",
booktitle = "2004 Asia-Pacific Conference on Simulated Evolution
and Learning",
address = "Busan, Korea",
month = oct,
notes = "Refereed International Conference Papers",
title = "Genetic Transposition in Tree-Adjoining Grammar Guided
Genetic Programming: the Relocation Operator",
URL = "http://sc.snu.ac.kr/PAPERS/SEAL2004.pdf",
year = "2004",
keywords = "genetic algorithms, genetic programming",
size = "6 pages",
abstract = "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.",
}
@PhdThesis{hoai_thesis,
author = "Nguyen Xuan Hoai",
title = "A Flexible Representation for Genetic Programming from
Natural Language Processing",
school = "Australian Defence force Academy, University of New
South Wales",
year = "2004",
address = "Australia",
month = dec,
keywords = "genetic algorithms, genetic programming,
grammar-guided, genotype space, natural language
processing, phenotype space, tree adjoining grammars
(TAGs)",
URL = "http://www.library.unsw.edu.au/~thesis/adt-ADFA/uploads/approved/adt-ADFA20051024.152230/public/01front.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/hoai_thesis.tar.gz",
URL = "http://handle.unsw.edu.au/1959.4/38750",
size = "262 pages",
abstract = "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.",
notes = "separate files",
}
@InProceedings{eurogp:HoaiMEH05,
author = "Nguyen Xuan Hoai and Robert I. McKay and Daryl Essam
and Hoang Tuan Hao",
editor = "Maarten Keijzer and Andrea Tettamanzi and Pierre
Collet and Jano I. {van Hemert} and Marco Tomassini",
title = "Genetic Transposition in Tree-Adjoining Grammar Guided
Genetic Programming: The Duplication Operator",
booktitle = "Proceedings of the 8th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3447",
year = "2005",
address = "Lausanne, Switzerland",
month = "30 " # mar # " - 1 " # apr,
organisation = "EvoNet",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-25436-6",
pages = "108--119",
URL = "http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3447&spage=108",
doi = "doi:10.1007/b107383",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
notes = "Part of \cite{keijzer:2005:GP} EuroGP'2005 held in
conjunction with EvoCOP2005 and EvoWorkshops2005",
}
@Article{HBE:IEETEC:06,
title = "Representation and Structural Difficulty in Genetic
Programming",
author = "Nguyen Xuan Hoai and R. I. (Bob) McKay and Daryl
Essam",
journal = "IEEE Transactions on Evolutionary Computation",
year = "2006",
volume = "10",
number = "2",
pages = "157--166",
month = apr,
keywords = "genetic algorithms, genetic programming, Deletion,
insertion, operator, representation, structural
difficulty",
URL = "http://sc.snu.ac.kr/courses/2006/fall/pg/aai/GP/nguyen/Structdiff.pdf",
doi = "doi:10.1109/TEVC.2006.871252",
size = "10 pages",
abstract = "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.",
}
@MastersThesis{Hoang:mastersthesis,
author = "Tuan-Hao Hoang",
title = "Representation and Data Preparation Issues in
Ecological Time-Series Modeling using Genetic
Programming",
school = "School of Computer Science University College,
University of New South Wales, Australian Defence Force
Academy",
year = "2004",
type = "Maser S.c of Information Technology",
month = nov,
note = "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",
keywords = "genetic algorithms, genetic programming, TAG3P",
URL = "http://seal.tst.adfa.edu.au/~z3106820/publications/masthesis.pdf",
size = "42 pages",
abstract = "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.",
}
@InProceedings{Hao:2004:aspgp,
author = "Hoang Tuan Hao and Nguyen Xuan Hoai and Robert I
McKay",
title = "Does it Matter Where you Start? {A} Comparison of Two
Initialisation Strategies for Grammar Guided Genetic
Programming",
booktitle = "Proceedings of The Second Asian-Pacific Workshop on
Genetic Programming",
year = "2004",
editor = "R I Mckay and Sung-Bae Cho",
address = "Cairns, Australia",
month = "6-7 " # dec,
keywords = "genetic algorithms, genetic programming, GGGP, TAG,
TAG3P",
broken = "http://seal.tst.adfa.edu.au/~z3106820/publications/t.hao-initialpop.pdf",
abstract = "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]",
notes = "http://www.itee.adfa.edu.au/~rim/ASPGP/programme.html",
}
@InCollection{hoang:2005:GPTP,
author = "Tuan Hao Hoang and Nguyen Xuan Hoai and R. I. (Bob)
McKay and Daryl Essam",
title = "The Importance pf Local Search: {A} Grammar Based
Approach to Environmental Time Series Modelling",
booktitle = "Genetic Programming Theory and Practice {III}",
year = "2005",
editor = "Tina Yu and Rick L. Riolo and Bill Worzel",
volume = "9",
series = "Genetic Programming",
chapter = "11",
pages = "159--175",
address = "Ann Arbor",
month = "12-14 " # may,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, local search,
insertion, deletion, grammar guided, tree adjoining
grammar, ecological modelling, time series",
ISBN = "0-387-28110-X",
size = "17 pages",
abstract = "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.",
notes = "part of \cite{yu:2005:GPTP} Published Jan 2006 after
the workshop",
}
@InProceedings{1144141,
author = "Tuan-Hao Hoang and Nguyen Xuan Hoai and Nguyen Thi
Hien and R I McKay and Daryl Essam",
title = "{ORDERTREE}: a new test problem for genetic
programming",
booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference
on Genetic and evolutionary computation",
year = "2006",
editor = "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",
volume = "1",
ISBN = "1-59593-186-4",
pages = "807--814",
address = "Seattle, Washington, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p807.pdf",
doi = "doi:10.1145/1143997.1144141",
publisher = "ACM Press",
publisher_address = "New York, NY, 10286-1405, USA",
month = "8-12 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, benchmark
problems, graph and tree search strategies, languages,
problem difficulty, theory",
notes = "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",
}
@InProceedings{Hoang:2006:CEC,
author = "Tuan-Hao Hoang and Daryl Essam and R. I. (Bob) McKay
and Xuan Hoai Nguyen",
title = "Solving Symbolic Regression Problems using Incremental
Evaluation in Genetic Programming",
booktitle = "Proceedings of the 2006 IEEE Congress on Evolutionary
Computation",
year = "2006",
pages = "7487--7494",
address = "Vancouver",
month = "6-21 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-9487-9",
URL = "http://seal.tst.adfa.edu.au/~z3106820/publications/cec2006.devtag.pdf",
size = "8 pages",
abstract = "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.",
notes = "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
the IEE.",
}
@InProceedings{Hoang:2007:ISICA,
publisher_address = "Wuhan, China",
author = "Tuan-Hao Hoang and Daryl Essam and Robert Ian (Bob)
McKay and Xuan Hoai Nguyen",
booktitle = "Proceedings of the 2007 International Symposium on
Intelligent Computation and Applications (ISICA)",
address = "Wuhan, China",
month = sep # " 21-23",
notes = "Accepted, Refereed International Conference Papers",
publisher = "China University of Geosciences Press",
title = "Building on Success in Genetic Programming:Adaptive
Variation \& Developmental Evaluation",
URL = "http://sc.snu.ac.kr/PAPERS/dtag.pdf",
year = "2007",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{conf/isica/HoangEMH07,
author = "Tuan Hao Hoang and Daryl Essam and Bob McKay and
Nguyen Xuan Hoai",
title = "Building on Success in Genetic Programming: Adaptive
Variation and Developmental Evaluation",
booktitle = "Proceedings of the Second International Symposium on
Computation and Intelligence, ISICA 2007",
year = "2007",
editor = "Lishan Kang and Yong Liu and Sanyou Y. Zeng",
volume = "4683",
series = "Lecture Notes in Computer Science",
pages = "137--146",
address = "Wuhan, China",
month = sep # " 21-23",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming,
Developmental, Incremental Learning, Adaptive
Mutation",
isbn13 = "978-3-540-74580-8",
doi = "doi:10.1007/978-3-540-74581-5_15",
size = "10 pages",
abstract = "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.",
bibdate = "2007-08-31",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/isica/isica2007.html#HoangEMH07",
}
@InProceedings{HoaMck07,
author = "Tuan-Hao Hoang and R. McKay and D. Essam and Xuan Hoai
Nguyen",
title = "Developmental Evaluation in Genetic Programming: {A}
Position Paper",
booktitle = "Frontiers in the Convergence of Bioscience and
Information Technologies, FBIT 2007",
year = "2007",
pages = "773--778",
address = "Jeju City, Korea",
month = "11-13 " # oct,
publisher = "IEEE Press",
keywords = "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",
isbn13 = "978-0-7695-2999-8",
URL = "http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=4524062&arnumber=4524205&count=165&index=142",
doi = "doi:10.1109/FBIT.2007.104",
abstract = "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.",
notes = "FBIT 2007:
http://ieeexplore.ieee.org/servlet/opac?punumber=4524061",
}
@Article{Hoang:2008:IJKBIES,
author = "Tuan-Hao Hoang and Daryl Essam and R. I. (Bob) McKay
and Nguyen Xuan Hoai",
title = "Developmental evaluation in Genetic Programming: The
{TAG}-based frame work",
journal = "International Journal of Knowledge-Based and
Intelligent Engineering Systems",
year = "2008",
volume = "12",
number = "1",
pages = "69--82",
keywords = "genetic algorithms, genetic programming",
ISSN = "1327-2314",
publisher = "IOS Press",
URL = "http://iospress.metapress.com/content/w4qu7136432k6733/",
abstract = "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.",
notes = "KES",
}
@InProceedings{DBLP:conf/ices/HoangMEN08,
author = "Tuan Hao Hoang and R. I. (Bob) McKay and Daryl Essam
and Nguyen Xuan Hoai",
title = "Learning General Solutions through Multiple
Evaluations during Development",
booktitle = "Proceedings of the 8th International Conference
Evolvable Systems: From Biology to Hardware, ICES
2008",
year = "2008",
editor = "Gregory Hornby and Luk{\'a}s Sekanina and Pauline C.
Haddow",
series = "Lecture Notes in Computer Science",
volume = "5216",
pages = "201--212",
address = "Prague, Czech Republic",
month = sep # " 21-24",
publisher = "Springer",
bibsource = "DBLP, http://dblp.uni-trier.de",
keywords = "genetic algorithms, genetic programming, Developmental
Genetic Programming, Hyper-heuristics, Generalisation
Overfitting, Parsimony",
isbn13 = "978-3-540-85856-0",
doi = "doi:10.1007/978-3-540-85857-7_18",
abstract = "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.",
}
@Article{Hoang:2011:ieeeTEC,
author = "Tuan-Hao Hoang and R. I. McKay and Daryl Essam and
Nguyen Xuan Hoai",
title = "On Synergistic Interactions Between Evolution,
Development and Layered Learning",
journal = "IEEE Transactions on Evolutionary Computation",
year = "2011",
volume = "15",
number = "3",
pages = "287--312",
month = jun,
keywords = "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",
ISSN = "1089-778X",
doi = "doi:10.1109/TEVC.2011.2150752",
size = "26 pages",
abstract = "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.",
notes = "DTAG3P, TAG3P, tree adjoined grammar (TAG), symbolic
regression, k-parity, ordertree Also known as
\cite{5898401}",
}
@InProceedings{hocaoglu:1998:,
author = "Cem Hocaoglu and Arthur C. Sanderson",
title = "Multi-dimensional Path Planning Evolutionary
Computation using Evolutionary Computation",
booktitle = "Proceedings of the 1998 IEEE World Congress on
Computational Intelligence",
year = "1998",
pages = "165--170",
address = "Anchorage, Alaska, USA",
month = "5-9 " # may,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-4869-9",
file = "c029.pdf",
size = "6 pages",
abstract = "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.",
notes = "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
World Congress on Computational Intelligence",
}
@InCollection{hochmuth:2003:OGEPTS,
author = "Gregor Hochmuth",
title = "On the Genetic Evolution of a Perfect Tic-Tac-Toe
Strategy",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2003",
year = "2003",
editor = "John R. Koza",
pages = "75--82",
address = "Stanford, California, 94305-3079 USA",
month = "4 " # dec,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms",
URL = "http://www.genetic-programming.org/sp2003/Hochmuth.pdf",
notes = "part of \cite{koza:2003:gagp}",
}
@InProceedings{hoehn:1999:PCMGAAII,
author = "Theodore P. Hoehn and Chrisila C. Pettey",
title = "Parental and Cyclic-Rate Mutation in Genetic
Algorithms: An Initial Investigation",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "297--304",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-383.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-383.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@PhdThesis{oai:etd.ohiolink.edu:wright1133882117,
title = "Pattern Recognition via Machine Learning with Genetic
Decision-Programming",
author = "Carl C. Hoff",
year = "2005",
school = "Department of Computer Science and Engineering, Wright
State University",
bibsource = "OAI-PMH server at www.ohiolink.edu",
language = "English",
oai = "oai:etd.ohiolink.edu:wright1133882117",
rights = "unrestricted; Copyright information available at the
source archive",
keywords = "genetic algorithms, genetic programming, Computer
Science (0984), Pattern Recognition, Machine Learning,
Evolutionary Computation, Genetic
Decision-Programming",
URL = "http://rave.ohiolink.edu/etdc/view?acc_num=wright1133882117.pdf",
URL = "http://rave.ohiolink.edu/etdc/view?acc_num=wright1133882117",
size = "179 pages",
abstract = "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.",
}
@InCollection{hoffman:1999:UGADCDHCEP,
author = "Don Hoffman",
title = "Using Genetic Algorithms for Data Compression:
Discovering Huffman Codes as Efficiently as Possible",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1999",
year = "1999",
editor = "John R. Koza",
pages = "58--67",
address = "Stanford, California, 94305-3079 USA",
month = "15 " # mar,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms",
notes = "part of \cite{koza:1999:GAGPs}",
}
@InProceedings{hoffmann:1998:itfcES,
author = "Frank Hoffmann",
title = "Incremental Tuning of Fuzzy Controllers by Means of an
Evolution Strategy",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "843--851",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "Evolutionary Strategies",
ISBN = "1-55860-548-7",
notes = "GP-98",
}
@Article{Hoffmann:2001:IS,
author = "Frank Hoffmann and Oliver Nelles",
title = "Genetic programming for model selection of {TSK}-fuzzy
systems",
journal = "Information Sciences",
year = "2001",
volume = "136",
number = "1-4",
pages = "7--28",
month = aug,
keywords = "genetic algorithms, genetic programming, Fuzzy
modeling, Neuro-fuzzy system",
URL = "http://www.sciencedirect.com/science/article/B6V0C-43DDW06-2/1/69cfc0ce8977ebea74cb8cec74efa722",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/22985/http:zSzzSzwww.nada.kth.sezSz~hoffmannzSzjis2001.pdf/genetic-programming-for-model.pdf",
URL = "http://citeseer.ist.psu.edu/459134.html",
size = "22 pages",
ISSN = "0020-0255",
doi = "doi:10.1016/S0020-0255(01)00139-6",
abstract = "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.",
}
@Article{hoffmann:2004:GPEM,
author = "James P. Hoffmann and Christopher D. Ellingwood and
Osei M. Bonsu and Daniel E. Bentil",
title = "Ecological Model Selection via Evolutionary
Computation and Information Theory",
journal = "Genetic Programming and Evolvable Machines",
year = "2004",
volume = "5",
number = "2",
pages = "229--241",
month = jun,
keywords = "genetic algorithms, genetic programming, model
selection, parsimony, complexity-based fitness,
variable-length representation",
ISSN = "1389-2576",
doi = "doi:10.1023/B:GENP.0000023690.71330.42",
abstract = "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.",
notes = "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",
}
@InProceedings{hofmeyr:1999:IDAAIS,
author = "Steven A. Hofmeyr and Stephanie Forrest",
title = "Immunity by Design: An Artificial Immune System",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1289--1296",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "artificial life, adaptive behavior and agents",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-039.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-039.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@TechReport{holden:1998:RN1,
author = "S. Holden",
title = "Several Things all Genetic Programmers Should Know
About Machine Learning",
institution = "Computer Science, University College, London",
year = "1998",
type = "Research Note",
number = "RN/98/1",
month = jan,
notes = "6 Jan 2003. It exists only as a half-finished draft
I'm afraid",
size = "0 pages",
}
@InProceedings{Holena:2011:GECCOcomp,
author = "Martin Holena and David Linke and Lukas Bajer",
title = "Case study: constraint handling in evolutionary
optimization of catalytic materials",
booktitle = "GECCO 2011 Evolutionary computation techniques for
constraint handling",
year = "2011",
editor = "Carlos Artemio Coello Coello and Dara Curran and
Thomas Jansen",
isbn13 = "978-1-4503-0690-4",
keywords = "genetic algorithms, genetic programming",
pages = "333--340",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001858.2002015",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{2002015} Distributed on CD-ROM at
GECCO-2011.
ACM Order Number 910112.",
}
@InProceedings{eurogp07:holladay,
author = "Kenneth Holladay and Kay Robbins and Jeffery {von
Ronne}",
title = "{FIFTH}: {A} Stack Based {GP} Language for Vector
Processing",
editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and
Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar",
booktitle = "Proceedings of the 10th European Conference on Genetic
Programming",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "4445",
year = "2007",
address = "Valencia, Spain",
month = "11-13 " # apr,
pages = "102--113",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-71602-5",
isbn13 = "978-3-540-71602-0",
doi = "doi:10.1007/978-3-540-71605-1_10",
abstract = "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.",
notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in
conjunction with EvoCOP2007, EvoBIO2007 and
EvoWorkshops2007",
}
@InProceedings{Holladay:2007:icdsp,
title = "Evolution of Signal Processing Algorithms using Vector
Based Genetic Programming",
author = "K. L. Holladay and K. A. Robbins",
booktitle = "15th International Conference on Digital Signal
Processing",
year = "2007",
pages = "503--506",
month = jul,
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming, signal
classification, FIFTH, vector based genetic programming
language, signal classification problem, signal
processing algorithm, symbol rate estimation",
doi = "doi:10.1109/ICDSP.2007.4288629",
size = "4 pages",
abstract = "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.",
notes = "P1333
p506 GP human competitive against DPDT
Also known as \cite{4288629}",
}
@InProceedings{DBLP:conf/gecco/Holladay09,
author = "Kenneth Holladay",
title = "Characterizing the genetic programming environment for
fifth ({GPE5}) on a high performance computing
cluster",
booktitle = "GECCO '09: Proceedings of the 11th Annual conference
on Genetic and evolutionary computation",
year = "2009",
editor = "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",
pages = "1363--1370",
address = "Montreal",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "8-12 " # jul,
organisation = "SigEvo",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-60558-325-9",
bibsource = "DBLP, http://dblp.uni-trier.de",
doi = "doi:10.1145/1569901.1570084",
abstract = "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.",
notes = "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.",
}
@InProceedings{Holladay:2011:GECCOcomp,
author = "Kenneth L. Holladay and John Marshall Sharp and Marc
Janssens",
title = "Automatic pyrolysis mass loss modeling from
thermo-gravimetric analysis data using genetic
programming",
booktitle = "3rd symbolic regression and modeling workshop for
GECCO 2011",
year = "2011",
editor = "Steven Gustafson and Ekaterina Vladislavleva",
isbn13 = "978-1-4503-0690-4",
keywords = "genetic algorithms, genetic programming",
pages = "655--662",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001858.2002063",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{2002063} Distributed on CD-ROM at
GECCO-2011.
ACM Order Number 910112.",
}
@InProceedings{oai:CiteSeerPSU:279876,
title = "Design of Highly Parallel Edge Detection Nodes Using
Evolutionary Techniques",
author = "Gordon S. Hollingworth and Steve L. Smith and Andy M.
Tyrrell",
booktitle = "Proceedings of the Seventh Euromicro Workshop on
Parallel and Distributed Processing, PDP '99",
year = "1999",
pages = "35--42",
address = "Funchal",
month = "3-5 " # feb,
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/13853/http:zSzzSzwww.amp.york.ac.ukzSzexternalzSzmediazSzcalzSzbio-inspzSzpublicationszSzgsh-pdp99.pdf/hollingworth99design.pdf",
URL = "http://citeseer.ist.psu.edu/279876.html",
citeseer-references = "oai:CiteSeerPSU:60383; oai:CiteSeerPSU:15766;
oai:CiteSeerPSU:92024; oai:CiteSeerPSU:39781",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:279876",
rights = "unrestricted",
abstract = "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",
}
@InProceedings{oai:CiteSeerPSU:280684,
author = "Gordon S. Hollingworth and Andy M. Tyrrell and Steve
L. Smith",
title = "Simulation of Evolvable Hardware to Solve Low Level
Image Processing Tasks",
booktitle = "Evolutionary Image Analysis, Signal Processing and
Telecommunications: First European Workshop, EvoIASP'99
and EuroEcTel'99",
year = "1999",
editor = "Riccardo Poli and Hans-Michael Voigt and Stefano
Cagnoni and Dave Corne and George D. Smith and Terence
C. Fogarty",
volume = "1596",
series = "LNCS",
pages = "46--58",
address = "Goteborg, Sweden",
publisher_address = "Berlin",
month = "28 " # may,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-65837-8",
doi = "doi:10.1007/10704703_4",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/13853/http:zSzzSzwww.amp.york.ac.ukzSzexternalzSzmediazSzcalzSzbio-inspzSzpublicationszSzgsh-evoiasp99.pdf/hollingworth99simulation.pdf",
URL = "http://citeseer.ist.psu.edu/280684.html",
citeseer-references = "\cite{oai:CiteSeerPSU:279876};
oai:CiteSeerPSU:212034; oai:CiteSeerPSU:60383;
oai:CiteSeerPSU:92024; oai:CiteSeerPSU:15766;
oai:CiteSeerPSU:39781",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:280684",
rights = "unrestricted",
abstract = "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.",
}
@TechReport{holmes:1995:odin,
author = "Paul Holmes",
title = "The Odin Genetic Programming System",
institution = "Computer Studies, Napier University",
year = "1995",
type = "Tech Report",
number = "RR-95-3",
address = "Craiglockhart, 216 Colinton Road, Edinburgh, EH14
1DJ",
keywords = "genetic algorithms, genetic programming",
broken = "ftp://ftp.dcs.napier.ac.uk/pub/papers/rr-95-3.ps",
URL = "http://citeseer.ist.psu.edu/holmes95odin.html",
abstract = "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",
notes = "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.{"}",
size = "56 pages",
}
@InProceedings{holmes:1996:fllc,
author = "Paul Holmes and Peter J. Barclay",
title = "Functional Languages on Linear Chromosomes",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "427",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
ISBN = "0-262-61127-9",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
size = "1 page",
notes = "GP-96. See also \cite{holmes:1995:odin}",
}
@InProceedings{holmes:1998:dnricslr2pubr,
author = "John H. Holmes",
title = "Differential Negative Reinforcement Improves
Classifier System Learning Rate in Two-Class Problems
with Unequal Base Rates",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "635--642",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, classifiers, ROC",
ISBN = "1-55860-548-7",
URL = "http://cceb.med.upenn.edu/holmes/gp98.ps.gz",
size = "8 pages",
abstract = "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.",
notes = "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).",
}
@InProceedings{holmes:1999:ELCSPITDTALMT,
author = "John H. Holmes",
title = "Evaluating Learning Classifier System Performance In
Two-Choice Decision Tasks: An {LCS} Metric Toolkit",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "789",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems, poster
papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-389.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GA-389.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{Holzinger:2010:gecco,
author = "Emily Rose Holzinger and Carrie C. Buchanan and Scott
M. Dudek and Eric C. Torstenson and Stephen D. Turner
and Marylyn D. Ritchie",
title = "Initialization parameter sweep in {ATHENA}: optimizing
neural networks for detecting gene-gene interactions in
the presence of small main effects",
booktitle = "GECCO '10: Proceedings of the 12th annual conference
on Genetic and evolutionary computation",
year = "2010",
editor = "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",
isbn13 = "978-1-4503-0072-8",
pages = "203--210",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, Bioinformatics, computational, systems and
synthetic biology",
month = "7-11 " # jul,
organisation = "SIGEVO",
address = "Portland, Oregon, USA",
doi = "doi:10.1145/1830483.1830519",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{1830519} GECCO-2010 A joint
meeting of the nineteenth international conference on
genetic algorithms (ICGA-2010) and the fifteenth annual
genetic programming conference (GP-2010)",
}
@InProceedings{holzinger:evobio12,
author = "Emily R. Holzinger and Scott M. Dudek and Alex T.
Frase and Brooke Fridley and Prabhakar Chalise and
Marylyn D. Ritchie",
title = "Comparison of methods for meta-dimensional data
analysis using in silico and biological data sets",
booktitle = "10th European Conference on Evolutionary Computation,
Machine Learning and Data Mining in Bioinformatics,
{EvoBIO 2012}",
year = "2012",
month = "11-13 " # apr,
editor = "Mario Giacobini and Leonardo Vanneschi and William S.
Bush",
series = "LNCS",
volume = "7246",
publisher = "Springer Verlag",
address = "Malaga, Spain",
pages = "134--143",
organisation = "EvoStar",
isbn13 = "978-3-642-29065-7",
doi = "doi:10.1007/978-3-642-29066-4_12",
size = "10 pages",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, GENN, Systems biology, neural networks,
evolutionary computation, data integration, human
genetics",
abstract = "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.",
notes = "Part of \cite{Giacobini:2012:EvoBio} EvoBio'2012 held
in conjunction with EuroGP2012, EvoCOP2012,
EvoMusArt2012 and EvoApplications2012",
affiliation = "Center for Human Genetics Research, Vanderbilt
University, Nashville, TN, USA",
}
@Article{Homaifar1995,
author = "Abdollah Homaifar and Ed McCormick",
title = "Simultaneous Design of Membership Functions and Rule
Sets for Fuzzy Controllers Using Genetic Algorithms",
journal = "IEEE Transactions on Fuzzy Systems",
volume = "3",
number = "2",
year = "1995",
pages = "129--139",
month = may,
keywords = "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",
ISSN = "1063-6706",
URL = "http://ieeexplore.ieee.org/iel4/91/8807/00388168.pdf?isNumber=8807",
size = "11 pages",
abstract = "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.",
}
@InProceedings{Homaifar:1999:CIRA,
author = "Abdollah Homaifar and Daryl Battle and Edward
Tunstel",
title = "Soft computing-based design and control for mobile
robot path tracking",
booktitle = "Computational Intelligence in Robotics and Automation,
CIRA '99. Proceedings. 1999 IEEE International
Symposium on",
year = "1999",
pages = "35--40",
month = "8-9 " # nov,
keywords = "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",
ISBN = "0-7803-5806-6",
URL = "http://ieeexplore.ieee.org/iel5/6589/17587/00809943.pdf?isNumber=17587",
size = "6 pages",
abstract = "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.",
notes = "CIRA'99 http://web.nps.navy.mil/~yun/cira99/",
}
@Article{Homaifar:2000:IJKBIES,
author = "Abdollah Homaifar and D. Battle and E. Tunstel and G.
Dozier",
title = "Genetic Programming Design of Fuzzy Controllers for
Mobile Robot Path Tracking",
journal = "International Journal of Knowledge-Based Intelligent
Engineering Systems",
year = "2000",
volume = "4",
number = "1",
pages = "33--52",
month = jan,
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
}
@InProceedings{hondo:1996:srrs,
author = "Naohiro Hondo and Hitoshi Iba and Yukinori Kakazu",
title = "Sharing and Refinement for Reusable Subroutines of
Genetic Programming",
booktitle = "Proceedings of the 1996 {IEEE} International
Conference on Evolutionary Computation",
year = "1996",
volume = "1",
pages = "565--570",
address = "Nagoya, Japan",
month = "20-22 " # may,
organisation = "IEEE Neural Network Council",
keywords = "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",
ISBN = "0-7803-2902-3",
doi = "doi:10.1109/ICEC.1996.542661",
size = "6 pages",
abstract = "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",
notes = "ICEC-96",
}
@InProceedings{hondo:1996:COASTgp96,
author = "Naohiro Hondo and Hitoshi Iba and Yukinori Kakazu",
title = "{COAST}: An Approach to Robustness and Reusability in
Genetic Programming",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "429",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
size = "1 page",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96",
}
@InProceedings{hondo:1996:rGPrl,
author = "Naohiro Hondo and Hitoshi Iba and Yukinori Kakazu",
title = "Robust {GP} in Robot Learning",
booktitle = "Parallel Problem Solving from Nature IV, Proceedings
of the International Conference on Evolutionary
Computation",
year = "1996",
editor = "Hans-Michael Voigt and Werner Ebeling and Ingo
Rechenberg and Hans-Paul Schwefel",
series = "LNCS",
volume = "1141",
pages = "751--760",
address = "Berlin, Germany",
publisher_address = "Heidelberg, Germany",
month = "22-26 " # sep,
publisher = "Springer Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-61723-X",
doi = "doi:10.1007/3-540-61723-X_1038",
size = "10 pages",
abstract = "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.",
notes = "http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4
COAST, Wall following problem",
affiliation = "Hokkaido University Complex Systems Engineering,
Division of Systems and Information Engineering N-13
W-8, Sapporo 060 Hokkaido Japan N-13 W-8, Sapporo 060
Hokkaido Japan",
}
@InProceedings{hondo:1998:mapssrc,
author = "Naohiro Hondo and Koji Nishikawa and Hiroshi Yokoi and
Yukinori Kakazu",
title = "Multi-Agent Programming System for Starfish Robot
Control",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "140--145",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-548-7",
notes = "GP-98",
}
@InProceedings{Hong:2006:IDETC/CIE,
author = "G. Hong and L. Hu and D. Xue and Y. L. Tu and Y. L.
Xiong",
title = "Integrated Optimal Product Design and Process Planning
for One-of-a-Kind Production",
booktitle = "26th Computers and Information in Engineering
Conference",
year = "2006",
address = "Philadelphia, Pennsylvania, USA",
month = sep # " 10-13",
publisher = "ASME",
keywords = "genetic algorithms, genetic programming",
isbn_bad = "0-7918-4257-8",
doi = "doi:10.1115/DETC2006-99325",
abstract = "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.",
notes = "Gang Hong, University of Calgary, Calgary, AB,
Canada",
}
@InProceedings{Hong:2008:IDETC/CIE,
author = "G. Hong and P. R. Dean and W. Yang and Y. L. Tu and D.
Xue",
title = "Integrated Optimal Product Design and Process Planning
for One-of-a-Kind Production",
booktitle = "28th Computers and Information in Engineering
Conference IDETC/CIE2008",
year = "2008",
volume = "3",
pages = "111--120",
address = "Brooklyn, New York, USA",
month = aug # " 3-6",
publisher = "ASME",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1115/DETC2008-49141",
abstract = "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.",
notes = "Gang Hong, University of Calgary, Calgary, AB,
Canada",
}
@PhdThesis{GangHong:thesis,
author = "Gang Hong",
title = "Research on Product Design and Manufacture for
One-of-a-Kind Production",
school = "Department of Mechanical and Manufacturing
Engineering, University of Calgary",
year = "2009",
address = "Canada",
month = "16 " # mar,
keywords = "genetic algorithms, genetic programming",
URL = "http://schulich.ucalgary.ca/mechanical/files/mechanical/Gang%20Hong-PhD%20Abstract.pdf",
abstract = "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.",
notes = "Gang Tony Hong",
}
@Article{Hong:2008:IJPR,
author = "G. Hong and L. Hu and D. Xue and Y. L. Tu and Y. L.
Xiong",
title = "Identification of the optimal product configuration
and parameters based on individual customer
requirements on performance and costs in one-of-a-kind
production",
journal = "International Journal of Production Research",
year = "2008",
volume = "46",
number = "12",
pages = "3297--3326",
publisher = "Taylor \& Francis",
keywords = "genetic algorithms, genetic programming, One-of-a-kind
production (OKP), Optimization, Customer requirements",
ISSN = "1366-588X",
size = "29 pages",
abstract = "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.",
notes = "Official Journal of the International Foundation for
Production Research (IFPR)",
}
@Article{Hong2010270,
author = "Gang Hong and Deyi Xue and Yiliu Tu",
title = "Rapid identification of the optimal product
configuration and its parameters based on
customer-centric product modeling for one-of-a-kind
production",
journal = "Computers in Industry",
volume = "61",
number = "3",
pages = "270--279",
year = "2010",
ISSN = "0166-3615",
doi = "doi:10.1016/j.compind.2009.09.006",
URL = "http://people.ucalgary.ca/~dxue/journal/COMIND2010.pdf",
URL = "http://www.sciencedirect.com/science/article/B6V2D-4XHC68M-2/2/3d71e33179122a81965181a637daea9e",
keywords = "genetic algorithms, genetic programming, One-of-a-kind
production, Customer-centric product modelling, Pattern
recognition, Rough set, Optimisation",
abstract = "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.",
}
@InCollection{hong:1999:DIRUGP,
author = "Hong S. Hong",
title = "Digbital Image Restoration Using Genetic Programming",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1999",
year = "1999",
editor = "John R. Koza",
pages = "68--75",
address = "Stanford, California, 94305-3079 USA",
month = "15 " # mar,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "part of \cite{koza:1999:GAGPs}",
}
@InProceedings{Hong:aspgp03,
author = "Jin-Hyuk Hong and Sung-Bae Cho",
title = "Effective Rule Discovery Using Genetic Programming for
{DNA} Microarray Analysis",
booktitle = "Proceedings of The First Asian-Pacific Workshop on
Genetic Programming",
year = "2003",
editor = "Sung-Bae Cho and Nguyen Xuan Hoai and Yin Shan",
pages = "53--61",
address = "Rydges (lakeside) Hotel, Canberra, Australia",
month = "8 " # dec,
keywords = "genetic algorithms, genetic programming",
ISBN = "0-9751724-0-9",
notes = "\cite{aspgp03}",
}
@InProceedings{conf/mdai/HongC05,
title = "Cancer Prediction Using Diversity-Based Ensemble
Genetic Programming",
author = "Jin-Hyuk Hong and Sung-Bae Cho",
year = "2005",
pages = "294--304",
editor = "Vicenc Torra and Yasuo Narukawa and Sadaaki Miyamoto",
publisher = "Springer",
series = "Lecture Notes in Computer Science",
volume = "3558",
booktitle = "Modeling Decisions for Artificial Intelligence, Second
International Conference, MDAI 2005, Proceedings",
address = "Tsukuba, Japan",
month = jul # " 25-27",
bibdate = "2005-07-18",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/mdai/mdai2005.html#HongC05",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-27871-0",
doi = "doi:10.1007/11526018_29",
abstract = "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.",
}
@InProceedings{hong:1999:SAMCMO,
author = "Tzung-Pei Hong and Hong-Shung Wang and Wei-Chou Chen",
title = "Simultaneously Applying Multiple Crossover and
Mutation Operators",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "790",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems, poster
papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/ga305.ps",
notes = "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)",
}
@InProceedings{hong:2004:eurogp,
author = "Jin-Hyuk Hong and Sung Bae Cho",
title = "Lymphoma Cancer Classification Using Genetic
Programming with {SNR} Features",
booktitle = "Genetic Programming 7th European Conference, EuroGP
2004, Proceedings",
year = "2004",
editor = "Maarten Keijzer and Una-May O'Reilly and Simon M.
Lucas and Ernesto Costa and Terence Soule",
volume = "3003",
series = "LNCS",
pages = "78--88",
address = "Coimbra, Portugal",
publisher_address = "Berlin",
month = "5-7 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-21346-5",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=3003&spage=78",
abstract = "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.",
notes = "Part of \cite{keijzer:2004:GP} EuroGP'2004 held in
conjunction with EvoCOP2004 and EvoWorkshops2004",
}
@InProceedings{Hong:2004:aspgp,
author = "Jin-Hyuk Hong and Sung-Bae Cho",
title = "Ensemble Genetic Programming for Classifying Gene
Expression Data",
booktitle = "Proceedings of The Second Asian-Pacific Workshop on
Genetic Programming",
year = "2004",
editor = "R I Mckay and Sung-Bae Cho",
address = "Cairns, Australia",
month = "6-7 " # dec,
keywords = "genetic algorithms, genetic programming",
notes = "http://www.itee.adfa.edu.au/~rim/ASPGP/programme.html",
}
@Article{Hong:Tco:06,
author = "Jin-Hyuk Hong and Sung-Bae Cho",
title = "The classification of cancer based on {DNA} microarray
data that uses diverse ensemble genetic programming",
journal = "Artificial Intelligence In Medicine",
year = "2006",
volume = "36",
number = "1",
pages = "43--58",
month = jan,
keywords = "genetic algorithms, genetic programming, Ensemble,
Diversity, Classification",
doi = "doi:10.1016/j.artmed.2005.06.002",
abstract = "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.",
}
@Article{Hong:2007:TEC,
title = "Autonomous Language Development Using Dialogue-Act
Templates and Genetic Programming",
author = "Jin-Hyuk Hong and Sungsoo Lim and Sung-Bae Cho",
journal = "IEEE Transactions on Evolutionary Computation",
year = "2007",
volume = "11",
number = "2",
pages = "213--225",
month = apr,
keywords = "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",
doi = "doi:10.1109/TEVC.2006.890265",
ISSN = "1089-778X",
abstract = "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",
}
@Article{Hong:2002:JH,
author = "Yoon-Seok Hong and Michael R. Rosen",
title = "Identification of an urban fractured-rock aquifer
dynamics using an evolutionary self-organizing
modelling",
journal = "Journal of Hydrology",
year = "2002",
volume = "259",
pages = "89--104",
number = "1-4",
keywords = "genetic algorithms, genetic programming",
ISSN = "0022-1694",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6V6C-44KPK1K-4/2/cc33fdeeff7d3869ee62940e37e3e133",
doi = "doi:10.1016/S0022-1694(01)00587-X",
abstract = "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.",
}
@InProceedings{hong:2003:gecco:workshop,
title = "Automatic Model Induction of a Biological Waste Water
Treatment Process using Context-Free Grammar Genetic
Programming",
author = "Yoon-Seok Hong",
pages = "146--149",
booktitle = "{GECCO 2003}: Proceedings of the Bird of a Feather
Workshops, Genetic and Evolutionary Computation
Conference",
editor = "Alwyn M. Barry",
year = "2003",
month = "11 " # jul,
publisher = "AAAI",
address = "Chigaco",
publisher_address = "445 Burgess Drive, Menlo Park, CA 94025",
notes = "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",
keywords = "genetic algorithms, genetic programming",
}
@Article{Hong:2003:WR,
author = "Yoon-Seok Hong and Rao Bhamidimarri",
title = "Evolutionary self-organising modelling of a municipal
wastewater treatment plant",
journal = "Water Research",
year = "2003",
volume = "37",
pages = "1199--1212",
number = "6",
abstract = "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.",
owner = "wlangdon",
URL = "http://www.sciencedirect.com/science/article/B6V73-47XW9PY-5/2/5581df84c89448cc706b69488765c7e1",
keywords = "genetic algorithms, genetic programming, Municipal
wastewater treatment plant, Self-organising modelling,
Model evolution, Neural network, ASM2",
doi = "doi:10.1016/S0043-1354(02)00493-1",
notes = "PMID: 12598184",
}
@Article{Hong:2005:WRR,
author = "Yoon-Seok Timothy Hong and Paul A. White and David M.
Scott",
title = "Automatic rainfall recharge model induction by
evolutionary computational intelligence",
journal = "Water Resources Research",
year = "2005",
volume = "41",
number = "W08422",
email = "T.Hong@gns.cri.nz",
keywords = "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",
URL = "http://www.agu.org/pubs/crossref/2005/2004WR003577.shtml",
doi = "doi:10.1029/2004WR003577",
abstract = "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.",
}
@Article{Hong:2007:ASCE,
author = "Yoon-Seok Timothy Hong and Byeong-Cheon Paik",
title = "Evolutionary Multivariate Dynamic Process Model
Induction for a Biological Nutrient Removal Process",
journal = "Journal of Environmental Engineering",
year = "2007",
volume = "12",
month = dec,
pages = "1126--1135",
email = "hongt@lsbu.ac.uk",
publisher = "ASCE",
keywords = "genetic algorithms, genetic programming, Grammar-based
genetic programming, wastewater treatment process",
ISSN = "0733-9372",
doi = "doi:10.1061/(ASCE)0733-9372(2007)133:12(1126)",
size = "10 pages",
abstract = "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.",
}
@Article{Hong:2012:SERRA,
author = "Yoon-Seok Timothy Hong and Byeong-Cheon Paik",
title = "Inference model derivation with a pattern analysis for
predicting the risk of microbial pollution in a sewer
system",
journal = "Stochastic Environmental Research and Risk
Assessment",
note = "online first",
publisher = "Springer",
keywords = "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",
ISSN = "1436-3240",
doi = "doi:10.1007/s00477-011-0538-9",
size = "13 pages",
abstract = "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.",
affiliation = "Department of Urban Engineering, London South Bank
University, 103 Borough Road, London, SE1 0AA UK",
}
@InProceedings{Hongbo:2007:ICEMI,
author = "Yuan Hongbo and Cai Zhenjiang and Cheng Man and Gao
liai",
title = "Study on Camera Calibration for Binocular Vision Based
on Genetic programming",
booktitle = "8th International Conference on Electronic Measurement
and Instruments, ICEMI '07",
year = "2007",
pages = "3--890--3--893",
address = "Xian, China",
month = aug # " 16-" # jul # " 18 ?????",
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-1136-8",
doi = "doi:10.1109/ICEMI.2007.4351060",
abstract = "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.",
notes = "Mechanical and electricity of College Agriculture
university of Hebei, Baoding, 071001 China",
}
@InProceedings{Hoock:2010:ThRaSH,
author = "J.-B. Hoock and O. Teytaud",
title = "Racing-Based Genetic Programming",
booktitle = "4th Workshop on Theory of Randomized Search
Heuristics, ThRaSH'2010",
year = "2010",
editor = "Anne Auger and Benjamin Doerr and Thomas Jansen and
Per Kristian Lehre and Frank Neumann and Pietro S.
Oliveto and Carsten Witt",
address = "Paris",
month = mar # " 24-25",
keywords = "genetic algorithms, genetic programming",
URL = "http://trsh2010.gforge.inria.fr/abstracts/04Hoock.pdf",
size = "1 page",
notes = "Multiple Simultaneous Hypothesis Testing (MSHT)
effect. racing algorithms Co-located JET meeting at
Universite Pierre et Marie Curie",
}
@InProceedings{Hoock:2010:EuroGP,
author = "Jean-Baptiste Hoock and Olivier Teytaud",
title = "Bandit-Based Genetic Programming",
booktitle = "Proceedings of the 13th European Conference on Genetic
Programming, EuroGP 2010",
year = "2010",
editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara
Silva and Stephen Dignum and A. Sima Uyar",
volume = "6021",
series = "LNCS",
pages = "268--277",
address = "Istanbul",
month = "7-9 " # apr,
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-12147-0",
doi = "doi:10.1007/978-3-642-12148-7_23",
abstract = "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.",
notes = "Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010
held in conjunction with EvoCOP2010 EvoBIO2010 and
EvoApplications2010",
}
@InProceedings{hooper:1996:iarGPes,
author = "Dale Hooper and Nicholas S. Flann",
title = "Improving the Accuracy and Robustness of Genetic
Programming through Expression Simplification",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "428",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
ISBN = "0-262-61127-9",
URL = "http://digital.cs.usu.edu/~flann/gp.pdf",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
size = "1 page",
notes = "GP-96. Occam's razor, bloat, introns, 200 edit rules",
}
@InProceedings{Hooper:1997:rhc,
author = "Dale C. Hooper and Nicholas S. Flann and Stephanie R.
Fuller",
title = "Recombinative Hill-Climbing: {A} Stronger Search
Method for Genetic Programming",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "174--179",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/Hooper_1997_rhc.pdf",
size = "6 pages",
notes = "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 \cite{hooper:1996:iarGPes}) 0.5%
mutation. Overfitting.",
}
@InProceedings{Hoover:2011:GECCOcomp,
author = "Kristopher Hoover and Rachel Marceau and Tyndall
Harris and Nicholas Hardison and David Reif and Alison
Motsinger-Reif",
title = "Optimization of grammatical evolution decision trees",
booktitle = "GECCO '11: Proceedings of the 13th annual conference
companion on Genetic and evolutionary computation",
year = "2011",
editor = "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",
isbn13 = "978-1-4503-0690-4",
keywords = "genetic algorithms, genetic programming, grammatical
evolution, Bioinformatics, computational, systems, and
synthetic biology: Poster",
pages = "35--36",
month = "12-16 " # jul,
organisation = "SIGEVO",
address = "Dublin, Ireland",
doi = "doi:10.1145/2001858.2001879",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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.",
notes = "Also known as \cite{2001879} Distributed on CD-ROM at
GECCO-2011.
ACM Order Number 910112.",
}
@InProceedings{horn:1996:nnclCS,
author = "Jeffrey Horn and David E. Goldberg",
title = "Natural Niching for Cooperative Learning in Classifier
Systems",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "Classifier Systems, Genetic Algorithms",
pages = "553--564",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96 Classifier paper",
}
@InProceedings{horn:1999:CCBNGA,
author = "Jeffrey Horn",
title = "Controlling the Cooperative-Competitive Boundary in
Niched Genetic Algorithms",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "305--312",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms and classifier systems",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/Ga-830.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/Ga-830.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{hornby:1999:AEGSQR,
author = "G. S. Hornby and M. Fujita and S. Takamura and T.
Yamamoto and O. Hanagata",
title = "Autonomous Evolution of Gaits with the Sony Quadruped
Robot",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1297--1304",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "artificial life, adaptive behavior and agents,
robotics, evolutionary robotics, locomotion",
ISBN = "1-55860-611-4",
URL = "http://www.demo.cs.brandeis.edu/papers/hornby_gecco99_sony.pdf",
URL = "http://www.demo.cs.brandeis.edu/papers/hornby_gecco99_sony.ps.gz",
URL = "http://www.demo.cs.brandeis.edu/papers/hornby_gecco99_sony.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-011.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-011.ps",
abstract = "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",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{hornby:1999:DTCPW,
author = "Gregory S. Hornby and Brian Mirtich",
title = "Diffuse versus True Coevolution in a Physics-based
World",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1305--1312",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "artificial life, adaptive behavior and agents,
co-evolution, pursuer-evader, neural networks",
ISBN = "1-55860-611-4",
URL = "http://www.demo.cs.brandeis.edu/papers/hornby_gecco99_merl.pdf",
URL = "http://www.demo.cs.brandeis.edu/papers/hornby_gecco99_merl.ps.gz",
URL = "http://www.demo.cs.brandeis.edu/papers/hornby_gecco99_merl.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-025.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/AA-025.ps",
abstract = "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.",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{hornby:2001:taggepd,
author = "Gregory S. Hornby and Jordan B. Pollack",
title = "The Advantages of Generative Grammatical Encodings for
Physical Design",
booktitle = "Proceedings of the 2001 Congress on Evolutionary
Computation CEC2001",
year = "2001",
pages = "600--607",
address = "COEX, World Trade Center, 159 Samseong-dong,
Gangnam-gu, Seoul, Korea",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "27-30 " # may,
organisation = "IEEE Neural Network Council (NNC), Evolutionary
Programming Society (EPS), Institution of Electrical
Engineers (IEE)",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, lindenmayer
system, L-systems, generative encoding, design",
ISBN = "0-7803-6658-1",
URL = "http://www.demo.cs.brandeis.edu/papers/hornby_cec01.pdf",
URL = "http://www.demo.cs.brandeis.edu/papers/hornby_cec01.ps",
size = "8 pages",
abstract = "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.",
notes = "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",
}
@InProceedings{Hornby:2001:ICRA,
author = "Gregory S. Hornby and Hod Lipson and Jordan B.
Pollack",
title = "Evolution of Generative Design Systems for Modular
Physical Robots",
booktitle = "IEEE International Conference on Robotics and
Automation",
year = "2001",
keywords = "genetic algorithms, genetic programming, L-systems,
generative encoding, design, robotics, P0L",
URL = "http://www.demo.cs.brandeis.edu/papers/hornby_icra01.pdf",
URL = "http://www.demo.cs.brandeis.edu/papers/hornby_icra01.ps",
size = "6 pages",
abstract = "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.",
notes = "The project page for this work is at:
http://www.demo.cs.brandeis.edu/pr/evo_design/evo_design.html",
}
@InProceedings{hornby:2001:GECCO,
title = "Body-Brain Co-evolution Using {L}-systems as a
Generative Encoding",
author = "Gregory S. Hornby and Jordan B. Pollack",
pages = "868--875",
year = "2001",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2001)",
editor = "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",
address = "San Francisco, California, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "7-11 " # jul,
keywords = "genetic algorithms, genetic programming, artificial
life, adaptive behaviour, agents, L-systems,
Lindenmayer grammar, generative encoding, ANN",
ISBN = "1-55860-774-9",
URL = "http://www.demo.cs.brandeis.edu/papers/hornby_gecco01.pdf",
URL = "http://www.demo.cs.brandeis.edu/papers/hornby_gecco01.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d07.pdf",
size = "8 pages",
abstract = "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.",
notes = "A joint meeting of the tenth International Conference
on Genetic Algorithms (ICGA-2001) and the sixth Annual
Genetic Programming Conference (GP-2001) Part of
\cite{spector: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.",
}
@Article{hornby.cag.01,
author = "Gregory S. Hornby and Jordan B. Pollack",
title = "Evolving {L}-Systems To Generate Virtual Creatures",
journal = "Computers and Graphics",
volume = "25",
number = "6",
year = "2001",
pages = "1041--1048",
publisher = "Elsevier",
keywords = "genetic algorithms, genetic programming, animation,
artificial life, representation, intelligent agents,
Lindenmayer systems (L-systems)",
URL = "http://www.demo.cs.brandeis.edu/papers/hornby_cag01.pdf",
URL = "http://www.demo.cs.brandeis.edu/papers/hornby_cag01.ps",
abstract = "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.",
notes = "The project page for this work is at:
http://www.demo.cs.brandeis.edu/pr/evo_design/evo_design.html",
}
@Article{Hornby:2002:AL,
author = "Gregory S. Hornby and Jordan B. Pollack",
title = "Creating High-Level Components with a Generative
Representation for Body-Brain Evolution",
journal = "Artificial Life",
year = "2002",
volume = "8",
number = "3",
pages = "223--246",
month = "Summer",
email = "hornby@email.arc.nasa.gov",
keywords = "genetic algorithms, genetic programming, Body-brain
evolution, generative representations, representation,
Lindenmayer systems, L-systems",
ISSN = "1064-5462",
URL = "http://www.demo.cs.brandeis.edu/papers/hornby_alife02.pdf",
URL = "http://ic.arc.nasa.gov/people/hornby/genre/genre.html",
URL = "http://mitpress.mit.edu/journals/pdf/alife_8_3_223_0.pdf",
doi = "doi:10.1162/106454602320991837",
size = "30 pages",
size = "25 pages",
abstract = "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.",
notes = "The project page for this work is at:
http://www.demo.cs.brandeis.edu/pr/evo_design/evo_design.html
Managed to get two entries for this paper. Combined
them (ie also known as \cite{hornby_alife02}. April
2008.",
notes = "genetic variations are repeated if offspring
fitness<0.1 parent",
}
@PhdThesis{hornby_phd03,
author = "Gregory Scott Hornby",
title = "Generative Representations for Evolutionary Design
Automation",
school = "Brandeis University, Dept. of Computer Science",
year = "2003",
address = "Boston, MA, USA",
month = feb,
email = "hornby@email.arc.nasa.gov",
keywords = "genetic algorithms, genetic programming, generative
representation, evolutionary design",
URL = "http://www.demo.cs.brandeis.edu/papers/long.html#hornby_phd",
URL = "http://ic.arc.nasa.gov/people/hornby/genre/genre.html",
abstract = "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.",
size = "242 pages",
notes = "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",
}
@InProceedings{hornby:2003:aaaiS,
author = "Gregory S. Hornby",
title = "Creating Complex Building Blocks through Generative
Representations",
booktitle = "Computational Synthesis: From Basic Building Blocks to
High Level Functionality: Papers from the 2003 AAAI
Spring Symposium",
year = "2003",
editor = "Hod Lipson and Erik K. Antonsson and John R. Koza",
series = "AAAI technical report SS-03-02",
pages = "98--105",
address = "Stanford, California, USA",
publisher = "AAAI Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-57735-179-7",
URL = "http://citeseer.ist.psu.edu/cache/papers/cs/30633/http:zSzzSzic.arc.nasa.govzSzpeoplezSzhornbyzSzpaperszSzhornby_ascs03.pdf/hornby03creating.pdf",
URL = "http://citeseer.ist.psu.edu/693104.html",
URL = "http://www.aaai.org/Press/Reports/Symposia/Spring/ss-03-02.html",
URL = "http://ic.arc.nasa.gov/people/hornby/papers/abstracts.html#pollack_alife01",
abstract = "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.",
notes = "TR SS-03-02",
}
@InProceedings{hornby:2003:gecco,
author = "Gregory S. Hornby",
title = "Generative Representations for Evolving Families of
Designs",
booktitle = "Genetic and Evolutionary Computation -- GECCO-2003",
editor = "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",
year = "2003",
pages = "1678--1689",
address = "Chicago",
publisher_address = "Berlin",
month = "12-16 " # jul,
volume = "2724",
series = "LNCS",
ISBN = "3-540-40603-4",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, parametric
Lindenmayer systems, evolving neural networks, ANN",
URL = "http://ic.arc.nasa.gov/people/hornby/papers/hornby_gecco03.pdf",
URL = "http://ic.arc.nasa.gov/people/hornby/papers/abstracts.html#hornby_gecco03",
abstract = "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.",
notes = "GECCO-2003. A joint meeting of the twelfth
International Conference on Genetic Algorithms
(ICGA-2003) and the eighth Annual Genetic Programming
Conference (GP-2003)",
}
@Article{hornby:2003:tRA,
author = "Gregory S. Hornby and Hod Lipson and Jordan B.
Pollack",
title = "Generative Representations for the Automated Design of
Modular Physical Robots",
journal = "IEEE transactions on Robotics and Automation",
year = "2003",
volume = "19",
number = "4",
pages = "709--713",
month = aug,
keywords = "genetic algorithms, genetic programming, Design
automation, evolutionary robotics, generative
representations, Lindenmayer systems",
ISSN = "1042-296X",
URL = "http://ccsl.mae.cornell.edu/papers/ITRA03_Hornby.pdf",
URL = "http://ieeexplore.ieee.org/iel5/70/27428/01220719.pdf?isnumber=27428&arnumber=1220719",
doi = "doi:10.1109/TRA.2003.814502",
size = "17 pages",
abstract = "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.",
notes = "INSPEC Accession Number: 7719817",
}
@InProceedings{Hornby:SwT:gecco2004,
author = "Gregory S. Hornby",
title = "Shortcomings with Tree-Structured Edge Encodings for
Neural Networks",
booktitle = "Genetic and Evolutionary Computation -- GECCO-2004,
Part II",
year = "2004",
editor = "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",
series = "Lecture Notes in Computer Science",
pages = "495--506",
address = "Seattle, WA, USA",
publisher_address = "Heidelberg",
month = "26-30 " # jun,
organisation = "ISGEC",
publisher = "Springer-Verlag",
volume = "3103",
ISBN = "3-540-22343-6",
ISSN = "0302-9743",
URL = "http://ic.arc.nasa.gov/people/hornby/papers/hornby_gecco04.ps",
URL = "http://link.springer.de/link/service/series/0558/bibs/3103/31030495.htm",
size = "12 pages",
keywords = "genetic algorithms, genetic programming, neural
networks, graphs, representation",
abstract = "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.",
notes = "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",
}
@Article{Hornby:2004:EPb,
author = "Gregory S. Hornby",
title = "Functional Scalability through Generative
Representations: the Evolution of Table Designs",
journal = "Environment and Planning B: Planning and Design",
year = "2004",
volume = "31",
number = "4",
pages = "569--587",
month = jul,
keywords = "genetic algorithms, genetic programming,
representation, evolutionary design",
ISSN = "0265-8135",
URL = "http://www0.arc.nasa.gov/publications/pdf/0814.pdf",
URL = "http://ti.arc.nasa.gov/people/hornby/papers/abstracts.html#hornby_epb04",
URL = "http://www.envplan.com/epb/abstracts/b31/b3015.html",
abstract = "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.",
}
@InProceedings{hornby:2004:ALwks,
author = "Gregory S. Hornby",
title = "Properties of Artifact Representations for
Evolutionary Design",
booktitle = "Workshop and Tutorial Proceedings Ninth International
Conference on the Simulation and Synthesis of Living
Systems(Alife {XI})",
year = "2004",
editor = "Mark Bedau and Phil Husbands and Tim Hutton and
Sanjeev Kumar and Hideaki Sizuki",
pages = "-",
address = "Boston, Massachusetts",
month = "12 " # sep,
note = "Self-organisation and development in artificial and
natural systems workshop.",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/staff/S.Kumar/hornby.pdf",
size = "4 pages",
notes = "http://www.alife9.org/ ALIFE9
http://www.cs.ucl.ac.uk/staff/S.Kumar/sodans.htm
",
}
@InProceedings{1068297,
author = "Gregory S. Hornby",
title = "Measuring, enabling and comparing modularity,
regularity and hierarchy in evolutionary design",
booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on
Genetic and evolutionary computation",
year = "2005",
editor = "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",
volume = "2",
ISBN = "1-59593-010-8",
pages = "1729--1736",
address = "Washington DC, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1729.pdf",
doi = "doi:10.1145/1068009.1068297",
publisher = "ACM Press",
publisher_address = "New York, NY, 10286-1405, USA",
month = "25-29 " # jun,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, evolutionary
algorithm, computer-automated design, design,
open-ended design, evolutionary design,
representations",
notes = "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",
}
@InProceedings{1144142,
author = "Gregory S. Hornby",
title = "{ALPS}: the age-layered population structure for
reducing the problem of premature convergence",
booktitle = "{GECCO 2006:} Proceedings of the 8th annual conference
on Genetic and evolutionary computation",
year = "2006",
editor = "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",
volume = "1",
ISBN = "1-59593-186-4",
pages = "815--822",
address = "Seattle, Washington, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006/docs/p815.pdf",
doi = "doi:10.1145/1143997.1144142",
publisher = "ACM Press",
publisher_address = "New York, NY, 10286-1405, USA",
month = "8-12 " # jul,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, age,
computer-automated design, evolutionary algorithm,
open-ended design, premature convergence, reliability",
notes = "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",
}
@Article{Hornby:2006:GPEM,
author = "Gregory S. Hornby",
title = "Shortcomings with using edge encodings to represent
graph structures",
journal = "Genetic Programming and Evolvable Machines",
year = "2006",
volume = "7",
number = "3",
pages = "231--252",
month = oct,
keywords = "genetic algorithms, genetic programming, Circuits,
Graphs, Neural networks, Representations, CEEL, PEEL,
ANN",
ISSN = "1389-2576",
URL = "http://ic.arc.nasa.gov/publications/pdf/1212.pdf",
doi = "doi:10.1007/s10710-006-9007-5",
abstract = "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.",
notes = "3-parity. goal scoring robot",
}
@Article{Hornby:2007:GPEM,
author = "Gregory S. Hornby and Sanjeev Kumar and Christian
Jacob",
title = "Editorial introduction to the special issue on
developmental systems",
journal = "Genetic Programming and Evolvable Machines",
year = "2007",
volume = "8",
number = "2",
pages = "111--113",
month = jun,
note = "Special issue on developmental systems",
keywords = "genetic algorithms, genetic programming, evolvable
hardware",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-007-9026-x",
size = "3 pages",
}
@InCollection{Hornby:2007:GPTP,
author = "Gregory S. Hornby",
title = "Improving the Scalability of Generative
Representations",
booktitle = "Genetic Programming Theory and Practice {V}",
year = "2007",
editor = "Rick L. Riolo and Terence Soule and Bill Worzel",
series = "Genetic and Evolutionary Computation",
chapter = "8",
pages = "127--144",
address = "Ann Arbor",
month = "17-19" # may,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-0-387-76308-8",
doi = "doi:10.1007/978-0-387-76308-8_8",
size = "17 pages",
abstract = "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&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&H and define two
measures of overall structure and organisation by
combining the measures of MR&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&H are important components of complexity. We
also compare evolution using five representations with
different combinations of MR&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&H can greatly improve
search performance.",
notes = "part of \cite{Riolo:2007:GPTP} Published 2008",
affiliation = "NASA Ames Research Center U. C. Santa Cruz, Mail Stop
269-3 Moffett Field CA 94035",
}
@InProceedings{Hornby:2007:cec,
author = "Gregory S. Hornby",
title = "Measuring Complexity by Measuring Structure and
Organization",
booktitle = "2007 IEEE Congress on Evolutionary Computation",
year = "2007",
editor = "Dipti Srinivasan and Lipo Wang",
pages = "2017--2024",
address = "Singapore",
month = "25-28 " # sep,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "1-4244-1340-0",
file = "1518.pdf",
keywords = "genetic algorithms, genetic programming, L-system,
GENRE, ALPS",
size = "8 pages",
abstract = "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&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&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.",
notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and
the IET.
IEEE Catalog Number: 07TH8963C
3d table from cubes",
}
@InProceedings{DBLP:conf/ices/HornbyKL08,
author = "Gregory Hornby and William F. Kraus and Jason D.
Lohn",
title = "Evolving {MEMS} Resonator Designs for Fabrication",
booktitle = "Proceedings of the 8th International Conference
Evolvable Systems: From Biology to Hardware, ICES
2008",
year = "2008",
editor = "Gregory Hornby and Luk{\'a}s Sekanina and Pauline C.
Haddow",
series = "Lecture Notes in Computer Science",
volume = "5216",
pages = "213--224",
address = "Prague, Czech Republic",
month = sep # " 21-24",
publisher = "Springer",
bibsource = "DBLP, http://dblp.uni-trier.de",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-540-85856-0",
URL = "http://idesign.ucsc.edu/pubs.html",
doi = "doi:10.1007/978-3-540-85857-7_19",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
}
@InCollection{Hornby:2009:GPTP,
author = "Gregory S. Hornby",
title = "A Steady-State Version of the Age-Layered Population
Structure {EA}",
booktitle = "Genetic Programming Theory and Practice {VII}",
year = "2009",
editor = "Rick L. Riolo and Una-May O'Reilly and Trent
McConaghy",
series = "Genetic and Evolutionary Computation",
address = "Ann Arbor",
month = "14-16 " # may,
publisher = "Springer",
chapter = "6",
pages = "87--102",
keywords = "genetic algorithms, genetic programming, Age,
Evolutionary Design, Genetic Programming,
Metaheuristic, Premature Convergence",
notes = "part of \cite{Riolo:2009:GPTP}",
}
@Article{Hornby:2011:EC,
author = "Gregory. S. Hornby and Jason D. Lohn and Derek S.
Linden",
title = "Computer-Automated Evolution of an {X}-Band Antenna
for {NASA}'s Space Technology 5 Mission",
journal = "Evolutionary Computation",
year = "2011",
volume = "19",
number = "1",
pages = "1--23",
month = "Spring",
keywords = "genetic algorithms, genetic programming, Antenna,
automated design, computational design, evolutionary
design, generative representation, spacecraft",
ISSN = "1063-6560",
doi = "doi:10.1162/EVCO_a_00005",
size = "23 pages",
abstract = "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.",
notes = "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)",
}
@Misc{horner-class,
author = "Helmut Horner",
title = "A {C}++ Class Library for Genetic Programming: The
Vienna University of Economics Genetic Programming
Kernel",
howpublished = "citeseer",
year = "1996",
month = "29 " # may,
keywords = "genetic algorithms, genetic programming",
URL = "http://citeseer.nj.nec.com/horner96class.html",
size = "69 pages",
abstract = "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.",
notes = "Only appears to be available via citeseer (oct 2001)",
}
@InProceedings{horng:1999:A,
author = "Jorng-Tzong Horng and Yu-Jan Chang and Cheng-Yen Kao",
title = "Applying evolutionary algorithms to materialized view
selection in a data warehouse",
booktitle = "Late Breaking Papers at the 1999 Genetic and
Evolutionary Computation Conference",
year = "1999",
editor = "Scott Brave and Annie S. Wu",
pages = "107--115",
address = "Orlando, Florida, USA",
month = "13 " # jul,
keywords = "Genetic Algorithms",
notes = "GECCO-99LB",
}
@InProceedings{horng:1999:R,
author = "Jorng-Tzong Horng and Chien-Chin Chen and Cheng-Yen
Kao",
title = "Resolution of quadratic assignment problems using an
evolutionary algorithm",
booktitle = "Late Breaking Papers at the 1999 Genetic and
Evolutionary Computation Conference",
year = "1999",
editor = "Scott Brave and Annie S. Wu",
pages = "116--124",
address = "Orlando, Florida, USA",
month = "13 " # jul,
keywords = "Genetic Algorithms, Evolutionary Strategies",
notes = "GECCO-99LB",
}
@InProceedings{hou_2005_iscas,
author = "Hao-Sheng Hou and Shoou-Jinn Chang and Yan-Kuin Su",
title = "Economical passive filter synthesis using genetic
programming based on tree representation",
booktitle = "Proceedings of the IEEE International Symposium on
Circuits and Systems (ISCAS)",
year = "2005",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, Passive
Filter Synthesis, Circuit Representation",
URL = "http://www.ncku.edu.tw/~acadserv/abroad/94q2-10a.pdf",
URL = "http://www.epapers.org//iscas2005/ESR/paper_details.php?PHPSESSID=3b6735b25d9602780e3827e15b1ee196&paper_id=4103",
abstract = "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.",
notes = "National Cheng Kung University, Taiwan ROC",
}
@Article{hou_2005_IEICE,
author = "Hao-Sheng Hou and Shoou-Jinn Chang and Yan-Kuin Su",
title = "Practical Passive Filter Synthesis Using Genetic
Programming",
journal = "IEICE Transactions on Electronics",
year = "2005",
volume = "E88-C",
number = "6",
pages = "1180--1185",
keywords = "genetic algorithms, genetic programming, passive
filter synthesis, frequency-dependent component",
URL = "http://ietele.oxfordjournals.org/cgi/reprint/E88-C/6/1180",
doi = "doi:10.1093/ietele/e88-c.6.1180",
size = "6 pages",
abstract = "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.",
notes = "Special Section on Analog Circuit and Device
Technologies -- Papers -- CAD",
}
@PhdThesis{hou:thesis,
author = "Jia-Li Hou",
title = "Constructing Static and Dynamic Investment Strategy
Portfolios by Genetic Programming",
school = "Information Management, National Central University",
year = "2008",
type = "Doctoral Dissertation",
address = "Taiwan",
month = "8 " # jan,
keywords = "genetic algorithms, genetic programming, Portfolio,
Artificial Intelligence, Capital Allocation, Investment
Strategy, Linear Capital Allocation, Non-Linear Capital
Allocation",
URL = "http://thesis.lib.ncu.edu.tw/ETD-db/ETD-search/view_etd?URN=90443001",
size = "117 pages",
abstract = "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.",
notes = "Language zh-TW.Big5 Chinese. Locked for two years",
}
@InCollection{houlette:1998:ECGPCFP,
author = "Ryan Houlette",
title = "Evolving Communication using Genetic Programming in
the Central-Place Foraging Problem",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1998",
year = "1998",
editor = "John R. Koza",
pages = "29--38",
address = "Stanford, California, 94305-3079 USA",
month = "17 " # mar,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-212568-8",
notes = "part of \cite{koza:1998:GAGPs}",
}
@Article{Hoverstad:2010:GPEM,
author = "Boye Annfelt Hoverstad",
title = "Simdist: a distribution system for easy
parallelization of evolutionary computation",
journal = "Genetic Programming and Evolvable Machines",
year = "2010",
volume = "11",
number = "2",
pages = "185--203",
month = jun,
keywords = "genetic algorithms, Distributed computing, Program
development",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-009-9100-7",
size = "19 pages",
abstract = "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.",
notes = "http://simdist.sourceforge.net.",
}
@Article{How:2010:QF,
author = "Janice How and Martin Ling and Peter Verhoeven",
title = "Does size matter? {A} genetic programming approach to
technical trading",
journal = "Quantitative Finance",
year = "2010",
volume = "10",
number = "2",
pages = "130--140",
keywords = "genetic algorithms, genetic programming",
ISSN = "1469-7696",
URL = "http://www.informaworld.com/smpp/title~db=all~content=g918916776",
doi = "doi:10.1080/14697680902773629",
notes = "School of Economics and Finance, Queensland University
of Technology, Brisbane, Queenland 4001, Australia b
General Electric, Auckland, New Zealand",
}
@InProceedings{howard:1998:wGPpde,
author = "Daniel Howard",
title = "Why Genetic Programming for solution of partial
differential equations?",
booktitle = "Late Breaking Papers at the Genetic Programming 1998
Conference",
year = "1998",
editor = "John R. Koza",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "Stanford, CA, USA",
month = "22-25 " # jul,
publisher = "Stanford University Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "GP-98LB",
}
@InProceedings{howard:1998:tdSARiGP,
author = "Daniel Howard and Simon C. Roberts and Richard
Brankin",
title = "Target Detection in {SAR} Imagery by Genetic
Programming",
booktitle = "Late Breaking Papers at the Genetic Programming 1998
Conference",
year = "1998",
editor = "John R. Koza",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "Stanford, CA, USA",
month = "22-25 " # jul,
publisher = "Stanford University Bookstore",
keywords = "genetic algorithms, genetic programming",
notes = "GP-98LB",
}
@InProceedings{howard:1999:esdsSARi,
author = "Daniel Howard and Simon C. Roberts and Richard
Brankin",
title = "Evolution of Ship Detectors for Satellite {SAR}
Imagery",
booktitle = "Genetic Programming, Proceedings of EuroGP'99",
year = "1999",
editor = "Riccardo Poli and Peter Nordin and William B. Langdon
and Terence C. Fogarty",
volume = "1598",
series = "LNCS",
pages = "135--148",
address = "Goteborg, Sweden",
publisher_address = "Berlin",
month = "26-27 " # may,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-65899-8",
URL = "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1598&spage=135",
notes = "EuroGP'99, part of \cite{poli:1999:GP}",
}
@InProceedings{howard:1999:EuroGEN,
author = "Daniel Howard and Simon C. Roberts",
title = "Evolving object detectors for infrared imagery: a
comparison of texture analysis against simple
statistics",
booktitle = "Evolutionary Algorithms in Engineering and Computer
Science",
year = "1999",
editor = "Kaisa Miettinen and Marko M. Makela and Pekka
Neittaanmaki and Jacques Periaux",
pages = "79--86",
address = "Jyvaskyla, Finland",
publisher_address = "Chichester, UK",
month = "30 " # may # " - 3 " # jun,
publisher = "John Wiley \& Sons",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.mit.jyu.fi/eurogen99/papers/howard.ps",
notes = "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.",
}
@InProceedings{howard:1999:ASGPSIA,
author = "Daniel Howard and Simon C. Roberts",
title = "A Staged Genetic Programming Strategy for Image
Analysis",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1047--1052",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-461.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-461.pdf",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@Article{Howard:1999:AES,
author = "Daniel Howard and Simon C. Roberts and Richard
Brankin",
title = "Target detection in {SAR} imagery by genetic
programming",
journal = "Advances in Engineering Software",
volume = "30",
pages = "303--311",
year = "1999",
number = "5",
month = may,
keywords = "genetic algorithms, genetic programming",
ISSN = "0965-9978",
doi = "doi:10.1016/S0965-9978(98)00093-3",
URL = "http://www.sciencedirect.com/science/article/B6V1P-3W1XV4H-1/1/6e7aee809f33757d0326c62a21824411",
abstract = "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.",
}
@InProceedings{howard:2000:EMRRID,
author = "Daniel Howard and Simon C. Roberts",
title = "Evolution of Mesh Refinement Rules for Impact
Dynamics",
booktitle = "Proceedings of the 2000 Congress on Evolutionary
Computation CEC00",
year = "2000",
pages = "1297--1303",
address = "La Jolla Marriott Hotel La Jolla, California, USA",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "6-9 " # jul,
organisation = "IEEE Neural Network Council (NNC), Evolutionary
Programming Society (EPS), Institution of Electrical
Engineers (IEE)",
publisher = "IEEE Press",
keywords = "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",
ISBN = "0-7803-6375-2",
doi = "doi:10.1109/CEC.2000.870801",
abstract = "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.",
notes = "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",
}
@InProceedings{howard:2001:gecco,
title = "Genetic Programming solution of the
convection-diffusion equation",
author = "Daniel Howard and Simon C. Roberts",
pages = "34--41",
year = "2001",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2001)",
editor = "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",
address = "San Francisco, California, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "7-11 " # jul,
keywords = "genetic algorithms, genetic programming,
convection-diffusion, differential equations, WRM, FEM,
numerical method",
ISBN = "1-55860-774-9",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d01.pdf",
notes = "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 \cite{spector: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{"}",
}
@InProceedings{Howard11:2002:EvoWorkshops,
author = "Daniel Howard and Simon C. Roberts",
title = "The Prediction of Journey Times on Motorways using
Genetic Programming",
booktitle = "Applications of Evolutionary Computing, Proceedings of
EvoWorkshops2002: EvoCOP, EvoIASP, EvoSTim/EvoPLAN",
year = "2002",
editor = "Stefano Cagnoni and Jens Gottlieb and Emma Hart and
Martin Middendorf and G{"}unther Raidl",
volume = "2279",
series = "LNCS",
pages = "210--221",
address = "Kinsale, Ireland",
publisher_address = "Berlin",
month = "3-4 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, evolutionary
computation, applications, MIDAS, London orbital
motorway M25",
ISBN = "3-540-43432-1",
URL = "http://link.springer-ny.com/link/service/series/0558/bibs/2279/22790210.htm",
URL = "http://link.springer-ny.com/link/service/series/0558/papers/2279/22790210.pdf",
size = "12 pages",
abstract = "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.",
notes = "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)",
}
@InProceedings{Howard13:2002:EvoWorkshops,
author = "Daniel Howard and Simon C. Roberts and Conor Ryan",
title = "The Boru Data Crawler for Object Detection Tasks in
Machine Vision",
booktitle = "Applications of Evolutionary Computing, Proceedings of
EvoWorkshops2002: EvoCOP, EvoIASP, EvoSTim/EvoPLAN",
year = "2002",
editor = "Stefano Cagnoni and Jens Gottlieb and Emma Hart and
Martin Middendorf and G{"}unther Raidl",
volume = "2279",
series = "LNCS",
pages = "222--232",
address = "Kinsale, Ireland",
publisher_address = "Berlin",
month = "3-4 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming, evolutionary
computation, applications",
ISBN = "3-540-43432-1",
size = "11 pages",
abstract = "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.",
notes = "EvoWorkshops2002, part of cagnoni:2002:ews
READMEM WRITEMEM working memory. Mark decisions branch.
Flags. Second results branch. Looking for cars
",
}
@InProceedings{howard2:2002:gecco,
author = "Daniel Howard and Simon C. Roberts",
title = "Application Of Genetic Programming To Motorway Traffic
Modelling",
booktitle = "GECCO 2002: Proceedings of the Genetic and
Evolutionary Computation Conference",
editor = "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",
year = "2002",
pages = "1097--1104",
address = "New York",
publisher_address = "San Francisco, CA 94104, USA",
month = "9-13 " # jul,
publisher = "Morgan Kaufmann Publishers",
keywords = "genetic algorithms, genetic programming, real world
applications, forecasting, incident detection, motorway
traffic modelling, time series prediction",
ISBN = "1-55860-878-8",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/RWA305.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/RWA305.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-20.pdf",
notes = "GECCO-2002. A joint meeting of the eleventh
International Conference on Genetic Algorithms
(ICGA-2002) and the seventh Annual Genetic Programming
Conference (GP-2002)",
}
@InProceedings{howard:2002:gecco,
author = "Daniel Howard and Simon C. Roberts and Conor Ryan",
title = "Machine Vision: Exploring Context With Genetic
Programming",
booktitle = "GECCO 2002: Proceedings of the Genetic and
Evolutionary Computation Conference",
editor = "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",
year = "2002",
pages = "756--763",
address = "New York",
publisher_address = "San Francisco, CA 94104, USA",
month = "9-13 " # jul,
publisher = "Morgan Kaufmann Publishers",
keywords = "genetic algorithms, genetic programming, automatically
defined functions, data crawler, image analysis,
machine vision, target detection",
ISBN = "1-55860-878-8",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/GP303.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/GP303.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf",
notes = "GECCO-2002. A joint meeting of the eleventh
International Conference on Genetic Algorithms
(ICGA-2002) and the seventh Annual Genetic Programming
Conference (GP-2002)",
}
@InProceedings{Howard:evowks03,
author = "Daniel Howard and Karl Benson",
title = "Promoter Prediction with a {GP}-Automaton",
booktitle = "Applications of Evolutionary Computing,
EvoWorkshops2003: Evo{BIO}, Evo{COP}, Evo{IASP},
Evo{MUSART}, Evo{ROB}, Evo{STIM}",
year = "2003",
editor = "G{\"u}nther 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",
volume = "2611",
series = "LNCS",
pages = "44--53",
address = "University of Essex, England, UK",
publisher_address = "Berlin",
month = "14-16 " # apr,
organisation = "EvoNet",
publisher = "Springer-Verlag",
keywords = "evolutionary computation, applications",
abstract = "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.",
notes = "EvoWorkshops2003",
}
@Article{howard:2003:JDS,
author = "Daniel Howard",
title = "Innovating with Automatic Programming",
journal = "Journal of Defence Science",
year = "2003",
volume = "8",
number = "2",
pages = "76--82",
month = may,
keywords = "genetic algorithms, genetic programming",
notes = "pixel fusion tirrs. midas linda m25",
}
@InProceedings{howard:2003:gecco,
author = "Daniel Howard and Karl Benson",
title = "Evolutionary Computation Method for Promoter Site
Prediction in {DNA}",
booktitle = "Genetic and Evolutionary Computation -- GECCO-2003",
editor = "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",
year = "2003",
pages = "1690--1701",
address = "Chicago",
publisher_address = "Berlin",
month = "12-16 " # jul,
volume = "2724",
series = "LNCS",
ISBN = "3-540-40603-4",
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
notes = "GECCO-2003. A joint meeting of the twelfth
International Conference on Genetic Algorithms
(ICGA-2003) and the eighth Annual Genetic Programming
Conference (GP-2003)",
}
@InCollection{howard:2003:GPTP,
author = "Daniel Howard",
title = "Modularization by Multi-Run Frequency Driven Subtree
Encapsulation",
booktitle = "Genetic Programming Theory and Practice",
publisher = "Kluwer",
year = "2003",
editor = "Rick L. Riolo and Bill Worzel",
chapter = "10",
pages = "155--172",
keywords = "genetic algorithms, genetic programming,
Modularization, Subtree Encapsulation, Multi-run, ADF,
Subtree Database, Subtree Frequency, Parity Problem",
ISBN = "1-4020-7581-2",
notes = "Part of \cite{RioloWorzel:2003}",
size = "18 pages",
}
@Article{Howard:2003:CIB,
author = "Daniel Howard and Karl Benson",
title = "Evolutionary computation method for pattern
recognition of cis-acting sites",
journal = "Biosystems",
year = "2003",
volume = "72",
number = "1-2",
pages = "19--27",
month = nov,
note = "Special Issue on Computational Intelligence in
Bioinformatics",
keywords = "genetic algorithms, genetic programming, Finite State
Automata, DNA, human genome, promoter, evolutionary
computation, bioinformatics",
ISSN = "0303-2647",
doi = "doi:10.1016/S0303-2647(03)00132-1",
URL = "http://www.sciencedirect.com/science/article/B6T2K-49NRT53-1/2/a695c769043ab9105da3bb6cf90fe774",
URL = "http://www.ncbi.nlm.nih.gov/PubMed/",
abstract = "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.",
}
@InProceedings{Howard:2004:ICKBIIESC,
author = "Daniel Howard",
title = "Top Down Modelling with Genetic Programming",
booktitle = "Proceedings of the 8th International Conference on
Knowledge-Based Intelligent Information and Engineering
Systems Conference, KES 2004, Part III",
year = "2004",
editor = "Mircea Gh. Negoita and Robert J. Howlett and Lakhmi C.
Jain",
series = "Lecture Notes in Artificial Intelligence",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, top down
modelling",
volume = "3215",
pages = "217--223",
month = sep # " 20-25",
ISBN = "3-540-23205-2",
URL = "http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3215&spage=217",
doi = "doi:10.1007/b100916",
size = "7 pages",
abstract = "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.",
}
@InCollection{howard:2004:GPTP,
author = "Daniel Howard and Simon C. Roberts",
title = "Incident Detection on Highways",
booktitle = "Genetic Programming Theory and Practice {II}",
year = "2004",
editor = "Una-May O'Reilly and Tina Yu and Rick L. Riolo and
Bill Worzel",
chapter = "16",
pages = "263--282",
address = "Ann Arbor",
month = "13-15 " # may,
publisher = "Springer",
keywords = "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",
ISBN = "0-387-23253-2",
doi = "doi:10.1007/0-387-23254-0_16",
abstract = "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.",
notes = "part of \cite{oreilly:2004:GPTP2}",
}
@Misc{BDS-TR-2005-001,
author = "Daniel Howard and Joseph Kolibal",
title = "Solution of differential equations with Genetic
Programming and the Stochastic Bernstein
Interpolation",
institution = "Biocomputing-Developmental Systems Group, University
of Limerick",
year = "2005",
number = "BDS-TR-2005-001",
address = "Ireland",
month = jun # " 19",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.genetic-programming.org/hc2005/bds.pdf",
abstract = "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.",
size = "37 pages",
notes = "Honorable Mention 2005 HUMIES GECCO-2005",
}
@Article{howard:2006:PRL,
author = "Daniel Howard and Simon C. Roberts and Conor Ryan",
title = "Pragmatic Genetic Programming strategy for the problem
of vehicle detection in airborne reconnaissance",
journal = "Pattern Recognition Letters",
year = "2006",
volume = "27",
number = "11",
pages = "1275--1288",
month = aug,
note = "Evolutionary Computer Vision and Image Understanding",
keywords = "genetic algorithms, genetic programming, Object
detection, Method of stages, Reconnaissance, Discrete
Fourier transform, Vehicle detection, Machine vision",
doi = "doi:10.1016/j.patrec.2005.07.025",
abstract = "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.",
}
@InProceedings{conf/rskt/Howard07,
author = "Daniel Howard",
title = "Multiple Solutions by Means of Genetic Programming:
{A} Collision Avoidance Example",
booktitle = "Proceedings of the Second International Conference on
Rough Sets and Knowledge Technology, RSKT 2007",
year = "2007",
editor = "Jingtao Yao and Pawan Lingras and Wei-Zhi Wu and
Marcin S. Szczuka and Nick Cercone and Dominik Slezak",
volume = "4481",
series = "Lecture Notes in Computer Science",
pages = "508--517",
address = "Toronto, Canada",
month = may # " 14-16",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, Multiple
Solutions",
isbn13 = "978-3-540-72457-5",
doi = "doi:10.1007/978-3-540-72458-2_63",
size = "10 pages",
abstract = "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.",
notes = "railway track, two train speeds, GP sets the points",
bibdate = "2007-07-05",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/rskt/rskt2007.html#Howard07",
}
@Article{Howard:2008:JBB,
author = "Daniel Howard and Simon C. Roberts and Conor Ryan and
Adrian Brezulianu",
title = "Textural Classification of Mammographic Parenchymal
Patterns with the {SONNET} Selforganizing Neural
Network",
journal = "Journal of Biomedicine and Biotechnology",
year = "2008",
volume = "2008",
pages = "526343",
month = jul # " 22",
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1155/2008/526343",
abstract = "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.",
notes = "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.{"}",
}
@InProceedings{Howard:2009:bliss,
author = "Daniel Howard",
title = "A Method of Project Evaluation and Review Technique
({PERT}) Optimization by Means of Genetic Programming",
booktitle = "2009 Symposium on Bio-inspired Learning and
Intelligent Systems for Security, BLISS '09",
year = "2009",
month = aug,
pages = "132--135",
keywords = "genetic algorithms, genetic programming, PERT
optimization, project control, project evaluation and
review technique, scheduling problems, PERT, project
management, scheduling",
abstract = "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.",
doi = "doi:10.1109/BLISS.2009.12",
notes = "Also known as \cite{5376803}",
}
@Article{Howard:2011:SC,
title = "Genetic programming of the stochastic interpolation
framework: convection-diffusion equation",
author = "Daniel Howard and Adrian Brezulianu and Joseph
Kolibal",
journal = "Soft Computing",
year = "2011",
number = "1",
volume = "15",
pages = "71--78",
bibdate = "2011-02-19",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/soco/soco15.html#HowardBK11",
URL = "http://dx.doi.org/10.1007/s00500-009-0520-3",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
affiliation = "Howard Science Limited, 24 Sunrise, Malvern, WR142NJ
UK",
}
@Article{Howard:2011a:SC,
author = "Daniel Howard and Adrian Brezulianu",
title = "Capturing expert knowledge of mesh refinement in
numerical methods of impact analysis by means of
genetic programming",
journal = "Soft Computing",
year = "2011",
publisher = "Springer Berlin / Heidelberg",
ISSN = "1432-7643",
keywords = "genetic algorithms, genetic programming",
pages = "103--110",
volume = "15",
issue = "1",
doi = "doi:10.1007/s00500-010-0684-x",
abstract = "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.",
affiliation = "Howard Science Limited, 24 Sunrise, Malvern, WR14 2NJ
UK",
}
@InProceedings{Howard:2011:ICHIT,
author = "Daniel Howard and Conor Ryan and J. J. Collins",
title = "Attribute Grammar Genetic Programming Algorithm for
Automatic Code Parallelization",
booktitle = "Proceedings of the 5th International Conference on
Convergence and Hybrid Information Technology, ICHIT
2011",
year = "2011",
editor = "Geuk Lee and Daniel Howard and Dominik Slezak",
volume = "6935",
series = "Lecture Notes in Computer Science",
pages = "250--257",
address = "Daejeon, Korea",
month = sep # " 22-24",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, Grammatical
Evolution, Context Free Grammar, Attribute Grammar,
Parallel Computing, Automatic Parallelisation,
Evolutionary Computation, SBSE",
isbn13 = "978-3-642-24081-2",
doi = "doi:10.1007/978-3-642-24082-9_31",
abstract = "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.",
affiliation = "Howard Science Limited, 24 Sunrise, Malvern, WR14 2NJ
United Kingdom",
}
@InProceedings{howard:2012:EuroGP,
author = "Gerard David Howard and Larry Bull and Andrew
Adamatzky",
title = "Cartesian Genetic Programming for Memristive Logic
Circuits",
booktitle = "Proceedings of the 15th European Conference on Genetic
Programming, EuroGP 2012",
year = "2012",
month = "11-13 " # apr,
editor = "Alberto Moraglio and Sara Silva and Krzysztof Krawiec
and Penousal Machado and Carlos Cotta",
series = "LNCS",
volume = "7244",
publisher = "Springer Verlag",
address = "Malaga, Spain",
pages = "37--48",
organisation = "EvoStar",
isbn13 = "978-3-642-29138-8",
doi = "doi:10.1007/978-3-642-29139-5_4",
keywords = "genetic algorithms, genetic programming, cartesian
genetic programming, Self-adaptation, Nanotechnology,
Boolean logic, Memristors, Robotics",
abstract = "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.",
notes = "Part of \cite{Moraglio:2012:GP} EuroGP'2012 held in
conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012
and EvoApplications2012",
}
@Article{howard:1995:GA-P,
author = "Les M. Howard and Donna J. D'Angelo",
title = "The {GA--P}: {A} Genetic Algorithm and Genetic
Programming hybrid",
journal = "IEEE Expert",
year = "1995",
volume = "10",
number = "3",
pages = "11--15",
month = jun,
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1109/64.393137",
size = "5 pages",
abstract = "The GA-P performs symbolic regression by combining the
traditional genetic algorithm's function optimization
strength with the genetic-programming paradigm to
evolve complex mathematical expressions capable of
handling numeric and symbolic data. This technique
should provide new insights into poorly understood data
relationships. Discovering relationships has been a
task troubling researchers since the dawn of modern
science. Discovering relationships between sets of data
is laborious and error prone, and it is highly subject
to researcher bias. Because many of today's research
problems are more complex than those of the past, it is
increasingly important that robust data analysis
methods be available to researchers. For a data
analysis method to be most useful, it must meet at
least three criteria: good predictive ability, insight
into the inner workings of the system being analyzed,
and unbiased results. Historically, researchers deduced
relationships solely by examining the data--a difficult
task if the relationship is complex, if many variables
are involved, or if the data are noisy (as often occurs
in real-world problems).",
abstract = "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.",
notes = "University of Georgia. IEEE Expert Special Track on
Evolutionary Programming (P. J. Angeline editor)
\cite{angeline:1995:er}",
}
@InProceedings{Howell:gecco06lbp,
author = "Abraham L. Howell and Roy T. R. McGrann and Richard R.
Eckert and Hiroki Sayama and Eileen Way",
title = "Using {RFID} and a Low Cost Robot to Evolve Foraging
Behavior",
booktitle = "Late breaking paper at Genetic and Evolutionary
Computation Conference {(GECCO'2006)}",
year = "2006",
month = "8-12 " # jul,
editor = "J{\"{o}}rn Grahl",
address = "Seattle, WA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006etc/papers/lbp131.pdf",
notes = "Distributed on CD-ROM at GECCO-2006",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
}
@InProceedings{4720346,
author = "Abraham L. Howell and Roy T. R. McGrann and Richard R.
Eckert",
title = "Teaching concepts in fuzzy logic using low cost
robots, {PDA}s, and custom software",
booktitle = "38th Annual Frontiers in Education Conference, FIE
2008",
year = "2008",
month = oct,
pages = "T3H-7--T3H-11",
keywords = "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",
doi = "doi:10.1109/FIE.2008.4720346",
ISSN = "0190-5848",
notes = "not on GP",
}
@InProceedings{Howlett:2010:AISB,
author = "Andrew Howlett and Simon Colton and Cameron Browne",
title = "Evolving Pixel Shaders for the Prototype Video Game
Subversion",
booktitle = "The Thirty Sixth Annual Convention of the Society for
the Study of Artificial Intelligence and Simulation of
Behaviour (AISB'10)",
year = "2010",
address = "De Montfort University, Leicester, UK",
month = "30th " # mar,
note = "AI \& Games Symposium",
keywords = "genetic algorithms, genetic programming, GPU, OpenGL
GLSL",
URL = "http://www.doc.ic.ac.uk/~sgc/papers/howlett_aisb10.pdf",
size = "6 pages",
abstract = "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.",
notes = "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",
}
@InProceedings{howley:1996:GPsam,
author = "Brian Howley",
title = "Genetic Programming of Near-Minimum-Time Spacecraft
Attitude Maneuvers",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "98--106",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
size = "9 pages",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96 see also \cite{howley:1996:samAIAA}",
}
@InProceedings{howley:1996:samAIAA,
author = "Brian Howley",
title = "Genetic Programming of Spacecraft Attitude Maneuvers
Under Reaction Wheel Control",
booktitle = "AIAA Guidance Navigation and Control Conference",
year = "1996",
month = "29--31 " # jul,
keywords = "genetic algorithms, genetic programming",
address = "San Diego, CA, USA",
publisher_address = "1801 Alexander Bell Crive, Suite 500, Reston, VA
22091, USA",
organisation = "American Institute of Aeronautics and Astronautics",
URL = "http://www.aiaa.org/content.cfm?pageid=406&gTable=mtgpaper&gID=10524",
size = "11 pages",
abstract = "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",
notes = "AIAA 1996-3849 see also \cite{howley:1996:GPsam}",
}
@InProceedings{Howley:1997:GPps,
author = "Brian Howley",
title = "Genetic Programming and Parametric Sensitivity: a Case
Study In Dynamic Control of a Two Link Manipulator",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "180--185",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
broken = "http://cdr.stanford.edu/~bhowley/AAAIGP97.pdf",
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.40.9214",
abstract = "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.",
notes = "GP-97",
}
@InCollection{howley:1995:GPNMTSAM,
author = "Brian Howley",
title = "Genetic Programming of Near Minimum Time Spacecraft
Attitude Maneuvers",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
1995",
year = "1995",
editor = "John R. Koza",
pages = "96--106",
address = "Stanford, California, 94305-3079 USA",
month = "11 " # dec,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-195720-5",
notes = "part of \cite{koza:1995:gagp}",
}
@Article{DBLP:journals/air/HowleyM05,
author = "Tom Howley and Michael G. Madden",
title = "The Genetic Kernel Support Vector Machine: Description
and Evaluation",
journal = "Artificial Intelligence Review",
volume = "24",
number = "3-4",
year = "2005",
pages = "379--395",
bibsource = "DBLP, http://dblp.uni-trier.de",
keywords = "genetic algorithms, genetic programming,
classification, genetic Kernel SVM, Mercer Kernel,
model selection, support vector machine",
ISSN = "0269-2821",
doi = "doi:10.1007/s10462-005-9009-3",
abstract = "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",
}
@InProceedings{Hrytsyshyn:2007:CADSM,
author = "Yarema Hrytsyshyn and Rostyslav Kryvyy and Sergiy
Tkatchenko",
title = "Genetic Programming For Solving Cutting Problem",
booktitle = "9th International Conference on the Experience of
Designing and Applications or CAD Systems in
Microelectronics, CADSM '07",
year = "2007",
pages = "280--282",
address = "Polyana, Ukraine",
month = "20-24 " # feb,
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming, CAD system,
arbitrary shape platforms, automated arbitrary shape
object arrangement, material cutting task, optimal
cutting problem, CAD/CAM, cutting",
doi = "doi:10.1109/CADSM.2007.4297550",
size = "3 pages",
abstract = "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.",
notes = "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 \cite{4297550}",
}
@Article{Hsu:2011:ESA,
author = "Chih-Ming Hsu",
title = "A hybrid procedure for stock price prediction by
integrating self-organizing map and genetic
programming",
journal = "Expert Systems with Applications",
volume = "In Press, Uncorrected Proof",
year = "2011",
ISSN = "0957-4174",
doi = "doi:10.1016/j.eswa.2011.04.210",
URL = "http://www.sciencedirect.com/science/article/B6V03-52T13T7-7/2/c2626c201c0da6cbc20628185936eaf3",
keywords = "genetic algorithms, genetic programming, Stock price
prediction, Self-organising map",
abstract = "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.",
}
@InProceedings{conf/icnc/HsuCKWC09,
title = "Estimating Strength of Concrete Using a Grammatical
Evolution",
author = "Hsun-Hsin Hsu and Li Chen and Chang-Huan Kou and
Tai-Sheng Wang and Sing-Han Chen",
booktitle = "Fifth International Conference on Natural Computation,
2009. ICNC '09",
year = "2009",
editor = "Haiying Wang and Kay Soon Low and Kexin Wei and
Junqing Sun",
month = "14-16 " # aug,
address = "Tianjian, China",
publisher = "IEEE Computer Society",
isbn13 = "978-0-7695-3736-8",
keywords = "genetic algorithms, genetic programming, Grammatical
Evolution",
bibdate = "2010-01-21",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/icnc/icnc2009-3.html#RaoWY09",
pages = "134--138",
doi = "doi:10.1109/ICNC.2009.492",
abstract = "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.",
}
@InProceedings{hsu:1999:GAASLDM,
author = "William H. Hsu and William M. Pottenger and Michael
Welge and Jie Wu and Ting-Hao Yang",
title = "Genetic Algorithms for Attribute Synthesis in
Large-Scale Data Mining",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1783",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "real world applications, poster papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-754.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-754.ps",
notes = "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",
}
@InProceedings{hsu:2001:waptmaoGP,
author = "William H. Hsu and Steven M. Gustafson",
title = "Wrappers for Automatic Parameter Tuning in Multi-Agent
Optimization by Genetic Programming",
booktitle = "IJCAI-2001 Workshop on Wrappers for Performance
Enhancement in Knowledge Discovery in Databases (KDD)",
year = "2001",
address = "Seattle, Washington, USA",
month = "4 " # aug,
keywords = "genetic algorithms, genetic programming, robotic
soccer",
abstract = "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.",
notes = "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 \cite{gustafson:mastersthesis}",
}
@InProceedings{hsu:2001:gpllmt,
author = "William H. Hsu and Steven M. Gustafson",
title = "Genetic Programming for Layered Learning of
Multi-Agent Tasks",
booktitle = "2001 Genetic and Evolutionary Computation Conference
Late Breaking Papers",
year = "2001",
editor = "Erik D. Goodman",
pages = "176--182",
address = "San Francisco, California, USA",
month = "9-11 " # jul,
keywords = "genetic algorithms, genetic programming, soccer,
RoboCup",
URL = "http://www.cs.nott.ac.uk/~smg/research/publications/gecco-2001.ps",
URL = "http://www.cs.nott.ac.uk/~smg/research/publications/gecco-2001.pdf",
notes = "GECCO-2001LB. Luke's ECJ, teambots. See also
\cite{gustafson:mastersthesis}",
}
@InProceedings{hsu3:2002:gecco,
author = "William H. Hsu and Steven M. Gustafson",
title = "Genetic Programming And Multi-agent Layered Learning
By Reinforcements",
booktitle = "GECCO 2002: Proceedings of the Genetic and
Evolutionary Computation Conference",
editor = "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",
year = "2002",
pages = "764--771",
address = "New York",
publisher_address = "San Francisco, CA 94104, USA",
URL = "http://www.cs.nott.ac.uk/~smg/research/publications/gecco-llgp-2002.pdf",
month = "9-13 " # jul,
publisher = "Morgan Kaufmann Publishers",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-878-8",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/GP004.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf",
notes = "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",
}
@InProceedings{hsu:2004:lbp,
author = "William H. Hsu and Scott J. Harmon and Edwin Rodriguez
and Christopher Zhong",
title = "Empirical Comparison of Incremental Reuse Strategies
in Genetic Programming for Keep-Away Soccer",
booktitle = "Late Breaking Papers at the 2004 Genetic and
Evolutionary Computation Conference",
year = "2004",
editor = "Maarten Keijzer",
address = "Seattle, Washington, USA",
month = "26 " # jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2004/LBP010.pdf",
abstract = "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.",
notes = "Part of \cite{keijzer:2004:GECCO:lbp}",
}
@InProceedings{hu:2002:thfcmfpea,
author = "Jianjun Hu and Erik D. Goodman",
title = "The Hierarchical Fair Competition ({HFC}) Model for
Parallel Evolutionary Algorithms",
booktitle = "Proceedings of the 2002 Congress on Evolutionary
Computation CEC2002",
editor = "David B. Fogel and Mohamed A. El-Sharkawi and Xin Yao
and Garry Greenwood and Hitoshi Iba and Paul Marrow and
Mark Shackleton",
pages = "49--54",
year = "2002",
publisher = "IEEE Press",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
organisation = "IEEE Neural Network Council (NNC), Institution of
Electrical Engineers (IEE), Evolutionary Programming
Society (EPS)",
ISBN = "0-7803-7278-6",
URL = "http://garage.cse.msu.edu/papers/GARAGe02-05-01.pdf",
month = "12-17 " # may,
notes = "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)",
keywords = "genetic algorithms, genetic programming",
abstract = "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.",
}
@InProceedings{hu2:2002:gecco,
author = "Jianjun Hu and Kisung Seo and Shaobo Li and Zhun Fan
and Ronald C. Rosenberg and Erik D. Goodman",
title = "Structure Fitness Sharing ({SFS}) For Evolutionary
Design By Genetic Programming",
booktitle = "GECCO 2002: Proceedings of the Genetic and
Evolutionary Computation Conference",
editor = "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",
year = "2002",
pages = "780--787",
address = "New York",
publisher_address = "San Francisco, CA 94104, USA",
month = "9-13 " # jul,
publisher = "Morgan Kaufmann Publishers",
keywords = "genetic algorithms, genetic programming, evolutionary
design, fitness sharing, mechatronic system, premature
convergence, topology and parameter search",
ISBN = "1-55860-878-8",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/GP195.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf",
notes = "GECCO-2002. A joint meeting of the eleventh
International Conference on Genetic Algorithms
(ICGA-2002) and the seventh Annual Genetic Programming
Conference (GP-2002)",
}
@InProceedings{hu:2002:gecco,
author = "Jianjun Hu and Erik D. Goodman and Kisung Seo and Min
Pei",
title = "Adaptive Hierarchical Fair Competition ({AHFC}) Model
For Parallel Evolutionary Algorithms",
booktitle = "GECCO 2002: Proceedings of the Genetic and
Evolutionary Computation Conference",
editor = "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",
year = "2002",
pages = "772--779",
address = "New York",
publisher_address = "San Francisco, CA 94104, USA",
month = "9-13 " # jul,
publisher = "Morgan Kaufmann Publishers",
keywords = "genetic algorithms, genetic programming, adaptive
evolutionary algorithm, fair competition principle,
hierarchical topology, parallel evolutionary
algorithms, premature convergence",
ISBN = "1-55860-878-8",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/GP186.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf",
notes = "GECCO-2002. A joint meeting of the eleventh
International Conference on Genetic Algorithms
(ICGA-2002) and the seventh Annual Genetic Programming
Conference (GP-2002)",
}
@InProceedings{Jianjun-Hu:2003:AAAI,
author = "Jianjun Hu and Erik D. Goodman and Kisung Seo and Zhun
Fan and Ronald C. Rosenberg",
title = "{HFC:} {A} Continuing {EA} Framework for Scalable
Evolutionary Synthesis",
booktitle = "Proceedings of the 2003 {AAAI} Spring Symposium -
Computational Synthesis: From Basic Building Blocks to
High Level Functionality",
year = "2003",
pages = "106--113",
address = "Stanford, California",
publisher_address = "445 Burgess Drive. Menlo park, CA, 94025, USA",
publisher = "AAAI press",
month = "24" # Mar,
organisation = "AAAI",
email = "hujianju@msu.edu, goodman@egr.msu.edu",
keywords = "genetic algorithms, genetic programming, scalability,
sustainability, HFC",
URL = "http://www-rcf.usc.edu/~jianjunh/paper/stanford_hfc.pdf",
abstract = "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.",
}
@InCollection{Jianjun-Hu:2003:GPTP,
author = "Jianjun Hu and Erik D. Goodman and Kisung Seo",
title = "Continuous Hierarchical Fair Competition Model for
Sustainable Innovation in Genetic Programming",
booktitle = "Genetic Programming Theory and Practice",
publisher = "Kluwer",
year = "2003",
editor = "Rick L. Riolo and Bill Worzel",
chapter = "6",
pages = "81--98",
keywords = "genetic algorithms, genetic programming, sustainable
innovation, HFC, fair competition principle",
abstract = "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.",
notes = "Part of \cite{RioloWorzel:2003}",
size = "pages",
}
@InCollection{hu:2004:GPTP,
author = "Jianjun Hu and Erik Goodman",
title = "Topological Synthesis of Robust Dynamic Systems by
Sustainable Genetic Programming",
booktitle = "Genetic Programming Theory and Practice {II}",
year = "2004",
editor = "Una-May O'Reilly and Tina Yu and Rick L. Riolo and
Bill Worzel",
chapter = "9",
pages = "143--157",
address = "Ann Arbor",
month = "13-15 " # may,
publisher = "Springer",
note = "pages missing",
keywords = "genetic algorithms, genetic programming, sustainable
genetic programming, automated synthesis, dynamic
systems, robust design, bond graphs, analog filter",
ISBN = "0-387-23253-2",
doi = "doi:10.1007/0-387-23254-0_9",
abstract = "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.",
notes = "part of \cite{oreilly:2004:GPTP2}",
}
@InProceedings{hu:2004:wapcbgp,
title = "Wireless Access Point Configuration by Genetic
Programming",
author = "Jianjun Hu and Erik Goodman",
pages = "1178--1184",
booktitle = "Proceedings of the 2004 IEEE Congress on Evolutionary
Computation",
year = "2004",
publisher = "IEEE Press",
month = "20-23 " # jun,
address = "Portland, Oregon",
ISBN = "0-7803-8515-2",
keywords = "genetic algorithms, genetic programming, Evolutionary
design \& evolvable hardware, Real-world applications,
Combinatorial \& numerical optimization, STGP",
URL = "http://www-rcf.usc.edu/~jianjunh/paper/cec2004_wireless.pdf",
language = "en",
oai = "oai:CiteSeerXPSU:10.1.1.134.6950",
abstract = "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",
notes = "CEC 2004 - A joint meeting of the IEEE, the EPS, and
the IEE.
\cite{cordella:evocop05} claims to outperform this",
}
@InProceedings{jianjunHu:2004:ACC,
author = "Jianjun Hu and Erik Goodman and Ronald Rosenberg",
title = "Topological search in automated mechatronic system
synthesis using bond graphs and genetic programming",
booktitle = "Proceedings of American Control Conference ACC 2004",
year = "2004",
month = jul,
organisation = "American Control Conference",
email = "hujianju@msu.edu",
keywords = "genetic algorithms, genetic programming",
}
@PhdThesis{JianjunHu:thesis,
author = "Jianjun Hu",
title = "Sustainable Evolutionary Algorithms and Scalable
Evolutionary Synthesis of Dynamic Systems",
school = "Michigan State University",
year = "2004",
address = "East Lancing, Michigan, 48823, USA",
month = "18 " # aug,
keywords = "genetic algorithms, genetic programming, HFC",
URL = "http://www-rcf.usc.edu/~jianjunh/paper/Hu_thesis_print.pdf",
size = "269 pages",
abstract = "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.",
notes = "Related research http://www.egr.msu.edu/~hujianju/HFC
http://www.egr.msu.edu/~hujianju/gpbg",
}
@InCollection{hu:2005:GPTP,
author = "Jianjun Hu and Ronald C. Rosenberg and Erik D.
Goodman",
title = "Domain Specificity of Genetic Programming based
Automated Synthesis: a Case Study with Synthesis of
Mechanical Vibration Absorbers",
booktitle = "Genetic Programming Theory and Practice {III}",
year = "2005",
editor = "Tina Yu and Rick L. Riolo and Bill Worzel",
volume = "9",
series = "Genetic Programming",
chapter = "18",
pages = "275--290",
address = "Ann Arbor",
month = "12-14 " # may,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming, Automated
synthesis, passive vibration absorber, bond graphs,
mechatronic systems, domain knowledge",
ISBN = "0-387-28110-X",
size = "16 pages",
abstract = "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.",
notes = "part of \cite{yu:2005:GPTP} Published Jan 2006 after
the workshop",
}
@Article{hu:2005:EC,
author = "Jianjun Hu and Erik Goodman and Kisung Seo and Zhun
Fan and Rondal Rosenberg",
title = "The Hierarchical Fair Competition Framework for
Sustainable Evolutionary Algorithms",
journal = "Evolutionary Computation",
year = "2005",
volume = "13",
number = "2",
pages = "241--277",
month = "Summer",
keywords = "genetic algorithms, genetic programming, sustainable
evolutionary algorithms, building blocks, premature
convergence, diversity, fair competition, hierarchical
problem solving",
ISSN = "1063-6560",
publisher = "MIT Press",
URL = "http://www.ingentaconnect.com/content/mitpress/evco/2005/00000013/00000002/art00005",
doi = "doi:10.1162/1063656054088530",
size = "37 pages",
abstract = "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.",
notes = "http://mitpress.mit.edu/catalog/item/default.asp?ttype=4&tid=25",
}
@InProceedings{1068283,
author = "Jianjun Hu and Xiwei Zhong and Erik D. Goodman",
title = "Open-ended robust design of analog filters using
genetic programming",
booktitle = "{GECCO 2005}: Proceedings of the 2005 conference on
Genetic and evolutionary computation",
year = "2005",
editor = "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",
volume = "2",
ISBN = "1-59593-010-8",
pages = "1619--1626",
address = "Washington DC, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p1619.pdf",
doi = "doi:10.1145/1068009.1068283",
publisher = "ACM Press",
publisher_address = "New York, NY, 10286-1405, USA",
month = "25-29 " # jun,
organisation = "ACM SIGEVO (formerly ISGEC)",
keywords = "genetic algorithms, genetic programming, analog filter
synthesis, automated design, bond graph, design, robust
design",
notes = "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",
}
@InCollection{hu:2007:ECdue,
author = "Jianjun Hu and Shaobo Li and Erik D. Goodman",
title = "Evolutionary Robust Design of Analog Filters Using
Genetic Programming",
booktitle = "Evolutionary Computation in Dynamic and Uncertain
Environments",
publisher = "Springer",
year = "2007",
editor = "Shengxiang Yang and Yew-Soon Ong and Yaochu Jin",
volume = "51",
series = "Studies in Computational Intelligence",
pages = "479--496",
chapter = "21",
email = "hujianju@gmail.com",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-540-49772-1",
URL = "http://www.springerlink.com/content/1w71041124712n78/",
doi: = "doi:10.1007/978-3-540-49774-5_21",
abstract = "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.",
notes = "http://www.cse.sc.edu/~jianjunh/",
}
@Article{hu:2008:AIEDAM,
author = "Jianjun Hu and Erik D. Goodman and Shaobo Li and
Ronald Rosenberg",
title = "Automated Synthesis of Mechanical Vibration Absorbers
Using Genetic Programming",
journal = "Artificial Intelligence for Engineering Design,
Analysis and Manufacturing",
year = "2008",
volume = "22",
number = "3",
pages = "207--217",
keywords = "genetic algorithms, genetic programming, Automated
Design, Bond Graphs, Conceptual Design, Evolutionary
Design",
URL = "http://journals.cambridge.org/action/displayAbstract;jsessionid=7665C0F109E52E12771D5DFCBD27C245.tomcat1?fromPage=online&aid=1903160",
doi = "doi:10.1017/S0890060408000140",
size = "11 pages",
abstract = "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.",
notes = "AIEDAM also known as \cite{CambridgeJournals:1903160}
and \cite{DBLP:journals/aiedam/HuGLR08}",
}
@InCollection{hu:2008:DbE,
author = "Jianjun Hu and Zhun Fan and Jiachuan Wang and Shaobo
Li and Kisung Seo and Xiangdong Peng and Janis Terpenny
and Ronald Rosenberg and Erik Goodman",
title = "{GPBG}: {A} Framework for Evolutionary Design of
Multi-domain Engineering Systems Using Genetic
Programming and Bond Graphs",
booktitle = "Design by Evolution",
publisher = "Springer",
year = "2008",
editor = "Philip F. Hingston and Luigi C. Barone and Zbigniew
Michalewicz",
series = "Natural Computing Series",
chapter = "14",
pages = "319--345",
address = "Berlin, Heidelberg",
email = "hujianju@gmail.com",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-540-74109-1",
URL = "http://www.springerlink.com/content/h373616x71445700/",
doi = "doi:10.1007/978-3-540-74111-4_18",
abstract = "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.",
}
@InProceedings{Hu:2010:ICCET,
author = "Jiaojiao Hu and Mei Xie",
title = "Fingerprint classification based on genetic
programming",
booktitle = "2nd International Conference on Computer Engineering
and Technology (ICCET), 2010",
year = "2010",
month = "16-18 " # apr,
volume = "6",
pages = "V6--193--V6--196",
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/ICCET.2010.5486315",
notes = "School of Electronic Engineering, University of
Electronic Science and Technology of China Chengdu,
China. Also known as \cite{5486315}",
}
@TechReport{MUN-CS-2008-04,
author = "Ting Hu and Wolfgang Banzhaf",
title = "Evolvability and Acceleration in Evolutionary
Computation",
institution = "Department of Computer Science, Memorial University of
Newfoundland",
year = "2008",
number = "2008-04",
address = "St. John's, NL, Canada A1B 3X5",
month = oct,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.mun.ca/computerscience/research/MUN-CS-2008-04.pdf",
abstract = "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.",
notes = "cited by \cite{Weise:2011:ieeeTEC}",
size = "72 pages",
}
@InProceedings{Hu:2008:gecco,
author = "Ting Hu and Wolfgang Banzhaf",
title = "Measuring rate of evolution in genetic programming
using amino acid to synonymous substitution ratio
ka/ks",
booktitle = "GECCO '08: Proceedings of the 10th annual conference
on Genetic and evolutionary computation",
year = "2008",
editor = "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",
isbn13 = "978-1-60558-130-9",
pages = "1337--1338",
address = "Atlanta, GA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1337.pdf",
doi = "doi:10.1145/1389095.1389352",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "12-16 " # jul,
keywords = "genetic algorithms, genetic programming, ka/ks Ratio,
Rate of evolution: Poster",
notes = "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 \cite{1389352}",
}
@InProceedings{Hu:2008:PPSN,
author = "Ting Hu and Wolfgang Banzhaf",
title = "Nonsynonymous to Synonymous Substitution Ratio ka/ks:
Measurement for Rate of Evolution in Evolutionary
Computation",
booktitle = "Parallel Problem Solving from Nature - PPSN X",
year = "2008",
editor = "Gunter Rudolph and Thomas Jansen and Simon Lucas and
Carlo Poloni and Nicola Beume",
volume = "5199",
series = "LNCS",
pages = "448--457",
address = "Dortmund",
month = "13-17 " # sep,
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-87699-5",
doi = "doi:10.1007/978-3-540-87700-4_45",
size = "pages",
abstract = "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.",
notes = "PPSN X",
}
@InProceedings{Hu:2009:eurogp,
author = "Ting Hu and Wolfgang Banzhaf",
title = "The Role of Population Size in Rate of Evolution in
Genetic Programming",
booktitle = "Proceedings of the 12th European Conference on Genetic
Programming, EuroGP 2009",
year = "2009",
editor = "Leonardo Vanneschi and Steven Gustafson and Alberto
Moraglio and Ivanoe {De Falco} and Marc Ebner",
volume = "5481",
series = "LNCS",
pages = "85--96",
address = "Tuebingen",
month = apr # " 15-17",
organisation = "EvoStar",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-01180-1",
doi = "doi:10.1007/978-3-642-01181-8_8",
notes = "Part of \cite{conf/eurogp/2009} EuroGP'2009 held in
conjunction with EvoCOP2009, EvoBIO2009 and
EvoWorkshops2009",
}
@InProceedings{DBLP:conf/gecco/HuB09,
author = "Ting Hu and Wolfgang Banzhaf",
title = "Neutrality and variability: two sides of evolvability
in linear genetic programming",
booktitle = "GECCO '09: Proceedings of the 11th Annual conference
on Genetic and evolutionary computation",
year = "2009",
editor = "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",
pages = "963--970",
address = "Montreal",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "8-12 " # jul,
organisation = "SigEvo",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-60558-325-9",
bibsource = "DBLP, http://dblp.uni-trier.de",
doi = "doi:10.1145/1569901.1570033",
abstract = "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.",
notes = "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.",
}
@PhdThesis{TingHu:thesis,
author = "Ting Hu",
title = "Evolvability and Rate of Evolution in Evolutionary
Computation",
school = "Department of Computer Science, Memorial University of
Newfoundland",
year = "2010",
address = "ST. John's, Newfoundland, Canada",
month = May,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.mun.ca/computerscience/graduate/thesis_TingHU.pdf",
size = "173 pages",
abstract = "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.",
notes = "http://www.mun.ca/computerscience/graduate/grad_thesis.php",
}
@Article{hu:2010:GPEM,
author = "Ting Hu and Simon Harding and Wolfgang Banzhaf",
title = "Variable population size and evolution acceleration: a
case study with a parallel evolutionary algorithm",
journal = "Genetic Programming and Evolvable Machines",
year = "2010",
volume = "11",
number = "2",
pages = "205--225",
month = jun,
keywords = "genetic algorithms, genetic programming, Variable
population size, Population bottleneck, Evolution
acceleration, Parallel computing, GPU",
ISSN = "1389-2576",
doi = "doi:10.1007/s10710-010-9105-2",
size = "21 pages",
abstract = "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.",
notes = "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'",
}
@InProceedings{hu:2011:EuroGP,
author = "Ting Hu and Joshua Payne and Jason Moore and Wolfgang
Banzhaf",
title = "Robustness, Evolvability, and Accessibility in Linear
Genetic Programming",
booktitle = "Proceedings of the 14th European Conference on Genetic
Programming, EuroGP 2011",
year = "2011",
month = "27-29 " # apr,
editor = "Sara Silva and James A. Foster and Miguel Nicolau and
Mario Giacobini and Penousal Machado",
series = "LNCS",
volume = "6621",
publisher = "Springer Verlag",
address = "Turin, Italy",
pages = "13--24",
organisation = "EvoStar",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-20406-7",
doi = "doi:10.1007/978-3-642-20407-4_2",
abstract = "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.",
notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in
conjunction with EvoCOP2011 EvoBIO2011 and
EvoApplications2011",
}
@InProceedings{hu:1998:GPci,
author = "Yuh-Jyh Hu",
title = "A Genetic Programming Approach to Constructive
Induction",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "146--151",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-548-7",
notes = "GP-98",
}
@InProceedings{hu:1998:bdGP,
author = "Yuh-Jyh Hu",
title = "Biopattern Discovery by Genetic Programming",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "152--157",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-548-7",
size = "6 pages",
notes = "GP-98. Cited by \cite{ross: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).",
}
@InProceedings{Hu:2000:GECCO,
author = "Yuh-Jyh Hu",
title = "Global Gene Expression Analysis with Genetic
Programming",
pages = "753",
year = "2000",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2000)",
editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz
and Lee Spector and Ian Parmee and Hans-Georg Beyer",
address = "Las Vegas, Nevada, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "10-12 " # jul,
keywords = "genetic algorithms, genetic programming, Poster",
ISBN = "1-55860-708-0",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/RW010.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/RW010.ps",
notes = "A joint meeting of the ninth International Conference
on Genetic Algorithms (ICGA-2000) and the fifth Annual
Genetic Programming Conference (GP-2000) Part of
\cite{whitley:2000:GECCO}",
}
@Article{Yuh-JyhHu:2002:NAR,
author = "Yuh-Jyh Hu",
title = "Prediction of consensus structural motifs in a family
of coregulated {RNA} sequences",
journal = "Nucleic Acids Research",
year = "2002",
volume = "30",
number = "17",
pages = "3886--3893",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.ingentaconnect.com/content/oup/nar/2002/00000030/00000017/art03886",
URL = "http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=137409.pdf",
URL = "http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=137409",
doi = "doi:10.1093/nar/gkg521",
size = "8 pages",
abstract = "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/.",
notes = "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",
}
@Article{Yuh-JyhHu:2003:NAR,
author = "Yuh-Jyh Hu",
title = "{GPRM}: a genetic programming approach to finding
common {RNA} secondary structure elements",
journal = "Nucleic Acids Research",
year = "2003",
volume = "31",
number = "13",
pages = "3446--3449",
month = "1 " # jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=168928.pdf",
URL = "http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=168928",
doi = "doi:10.1093/nar/gkg521",
size = "4 pages",
abstract = "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.",
notes = "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 \cite{Kawaguchi:2005:NAR}",
}
@Article{Huang:2007:ESA,
author = "Cheng-Lung Huang and Mu-Chen Chen and Chieh-Jen Wang",
title = "Credit scoring with a data mining approach based on
support vector machines",
journal = "Expert Systems with Applications",
year = "2007",
volume = "33",
number = "4",
pages = "847--856",
month = nov,
keywords = "genetic algorithms, genetic programming, Credit
scoring, Support vector machine, Neural networks,
Decision tree, Data mining, Classification",
doi = "doi:10.1016/j.eswa.2006.07.007",
abstract = "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.",
}
@InProceedings{Huang:2009:IEEE,
author = "Chia-Hui Huang and Han-Ying Kao",
title = "An effective linear approximation method for geometric
programming problems",
booktitle = "IEEE International Conference on Industrial
Engineering and Engineering Management, IEEM 2009",
year = "2009",
month = dec,
pages = "1743--1747",
abstract = "A geometric program (GP) is a type of mathematical
optimisation problem characterised by objective and
constraint functions, where",
keywords = "geometric programming, constraint functions, effective
linear approximation method, geometric programming
problems, mathematical optimisation problem, objective
functions, posynomial form, approximation theory",
doi = "doi:10.1109/IEEM.2009.5373154",
notes = "Not GP. Also known as \cite{5373154}",
}
@InProceedings{huang2:2001:GECCO,
title = "Independent Sampling Genetic Algorithms",
author = "Chien-Feng Huang",
pages = "367--374",
year = "2001",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2001)",
editor = "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",
address = "San Francisco, California, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "7-11 " # jul,
keywords = "genetic algorithms, independent sampling genetic
algorithms, idealized genetic algorithms, building
block detecting strategy, mate selection, Royal Road
functions, bounded deception problem",
ISBN = "1-55860-774-9",
URL = "http://www.c3.lanl.gov/~cfhuang/reprints/ISGA_033101.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2001/d03a.pdf",
abstract = "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.",
notes = "A joint meeting of the tenth International Conference
on Genetic Algorithms (ICGA-2001) and the sixth Annual
Genetic Programming Conference (GP-2001) Part of
\cite{spector:2001:GECCO}",
}
@InProceedings{Huang:2003:gecco,
author = "Chien-Feng Huang",
title = "Using an Immune System Model to Explore Mate Selection
in Genetic Algorithms",
booktitle = "Genetic and Evolutionary Computation -- GECCO-2003",
editor = "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",
year = "2003",
pages = "1041--1052",
address = "Chicago",
publisher_address = "Berlin",
month = "12-16 " # jul,
volume = "2723",
series = "LNCS",
ISBN = "3-540-40602-6",
publisher = "Springer-Verlag",
email = "cfhuang@lanl.gov",
keywords = "Genetic Algorithms, AIS, immune system, mate
selection",
URL = "http://www.springerlink.com/app/home/contribution.asp?wasp=804ttvxwwp6x8clrrvv3&referrer=parent&backto=issue,114,130;journal,175,1398;linkingpublicationresults,id:105633,1",
abstract = "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.",
notes = "GECCO-2003. A joint meeting of the twelfth
International Conference on Genetic Algorithms
(ICGA-2003) and the eights Annual Genetic Programming
Conference (GP-2003)",
}
@InProceedings{C-FHuang:2003:CEC1,
author = "Chien-Feng Huang and Luis M. Rocha",
title = "Exploration of {RNA} Editing and Design of Robust
Genetic Algorithms",
booktitle = "Proceedings of the 2003 Congress on Evolutionary
Computation CEC2003",
editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass
and Kay Chen Tan and Bob McKay and Daryl Essam and Tom
Gedeon",
pages = "2799--2806",
year = "2003",
publisher = "IEEE Press",
address = "Canberra",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "8-12 " # dec,
organisation = "IEEE Neural Network Council (NNC), Engineers Australia
(IEAust), Evolutionary Programming Society (EPS),
Institution of Electrical Engineers (IEE)",
email = "cfhuang@lanl.gov rocha@lanl.gov",
keywords = "genetic algorithms",
ISBN = "0-7803-7804-0",
size = "8 pages",
abstract = "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.",
notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust,
the EPS, and the IEE.",
}
@InProceedings{C-FHuang:2003:CEC2,
author = "Chien-Feng Huang",
title = "The Role of Crossover in an Immunity Based Genetic
Algorithm for Multimodal Function Optimization",
booktitle = "Proceedings of the 2003 Congress on Evolutionary
Computation CEC2003",
editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass
and Kay Chen Tan and Bob McKay and Daryl Essam and Tom
Gedeon",
pages = "2807--2814",
year = "2003",
publisher = "IEEE Press",
address = "Canberra",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "8-12 " # dec,
organisation = "IEEE Neural Network Council (NNC), Engineers Australia
(IEAust), Evolutionary Programming Society (EPS),
Institution of Electrical Engineers (IEE)",
email = "cfhuang@lanl.gov",
keywords = "genetic algorithms, mate selection, immune systems",
ISBN = "0-7803-7804-0",
size = "8 pages",
abstract = "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.",
notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust,
the EPS, and the IEE.",
}
@InProceedings{Huang:IMECS:fir,
author = "Ching-Ya Huang and Shih-Yen Tsai and Te-Jen Su",
title = "{FIR} Equalizer using Genetic Programming",
booktitle = "Proceedings of the International MultiConference of
Engineers and Computer Scientists, IMECS 2008",
year = "2008",
volume = "II",
pages = "1440--1443",
address = "Hong Kong",
month = "19-21 " # mar,
keywords = "genetic algorithms, genetic programming, Finite
Impulse Response equalizer",
isbn13 = "978-988-17012-1-3",
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.3713",
URL = "http://www.iaeng.org/publication/IMECS2008/IMECS2008_pp1440-1443.pdf",
bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
contributor = "CiteSeerX",
language = "en",
oai = "oai:CiteSeerXPSU:10.1.1.149.3713",
abstract = "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.",
}
@InProceedings{Huang:2007:cec,
author = "Haoming Huang and Michel Pasquier and Chai Quek",
title = "Hi{CEFS} - {A} Hierarchical Coevolutionary Approach
for the Dynamic Generation of Fuzzy System",
booktitle = "2007 IEEE Congress on Evolutionary Computation",
year = "2007",
editor = "Dipti Srinivasan and Lipo Wang",
pages = "3426--3433",
address = "Singapore",
month = "25-28 " # sep,
organization = "IEEE Computational Intelligence Society",
publisher = "IEEE Press",
ISBN = "1-4244-1340-0",
file = "1911.pdf",
doi: = "doi:10.1109/CEC.2007.4424915",
abstract = "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.",
notes = "CEC 2007 - A joint meeting of the IEEE, the EPS, and
the IET.
IEEE Catalog Number: 07TH8963C",
}
@PhdThesis{Haoming_Huang:thesis,
author = "Haoming Huang",
title = "Coevolutionary synthesis of fuzzy decision support
systems.",
school = "School of Computer Engineering, Nanyang Technological
University",
year = "2009",
address = "Singapore 639798",
URL = "http://repository.ntu.edu.sg/handle/10356/19087",
URL = "http://repository.ntu.edu.sg/bitstream/10356/19087/1/Haoming-Final%20Thesis%20v1.5.6-for%20print.pdf",
abstract = "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.",
notes = "Not GP? Centre for Computational Intelligence,
Supervisor: Michel B Pasquier (SCE), URLs broken June
2010",
}
@InProceedings{huang:1999:AESSSSP,
author = "Hsien-Da Huang and Jih Tsung Yang and Shu Fong Shen
and Jorng-Tzong Horng",
title = "An Evolution Strategy to Solve Sports Scheduling
Problems",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "1",
pages = "943",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "evolution strategies and evolutionary programming,
poster papers",
ISBN = "1-55860-611-4",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@Article{Huang:Tgp:06,
author = "Jih-Jeng Huang and Gwo-Hshiung Tzeng and Chorng-Shyong
Ong",
title = "Two-stage genetic programming {(2SGP)} for the credit
scoring model",
journal = "Applied Mathematics and Computation",
year = "2006",
volume = "174",
number = "2",
pages = "1039--1053",
month = "15 " # mar,
keywords = "genetic algorithms, genetic programming, Credit
scoring model, Artificial neural network (ANN),
Decision trees, Rough sets, Two-stage genetic
programming (2SGP)",
URL = "http://www.scorto.ru/downloads/Two-stage%20genetic%20programming%20(2SGP)%20for%20the%20credit%20scoring%20model.pdf",
doi = "doi:10.1016/j.amc.2005.05.027",
abstract = "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.",
}
@InProceedings{Huang:2009:WISM,
author = "Jiangtao Huang and Chuang Deng",
title = "A Novel Multiclass Classification Method with Gene
Expression Programming",
booktitle = "International Conference on Web Information Systems
and Mining, WISM 2009",
year = "2009",
month = nov,
pages = "139--143",
keywords = "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)",
doi = "doi:10.1109/WISM.2009.36",
abstract = "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.",
notes = "Also known as \cite{5369449}",
}
@InProceedings{Huelsbergen:1996:tsemli,
author = "Lorenz Huelsbergen",
title = "Toward Simulated Evolution of Machine Language
Iteration",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "315--320",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
URL = "http://cm.bell-labs.com/cm/cs/who/lorenz/papers/gp96.pdf",
URL = "http://cm.bell-labs.com/cm/cs/who/lorenz/papers/gp96.ps",
size = "6 pages",
notes = "GP-96
Cites \cite{koza:book}, \cite{icnn93:kinnear},
\cite{brave: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.",
}
@InProceedings{Huelsbergen:1997:lrsemlp,
author = "Lorenz Huelsbergen",
title = "Learning Recursive Sequences via Evolution of
Machine-Language Programs",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "186--194",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
URL = "http://cm.bell-labs.com/cm/cs/who/lorenz/papers/gp97.ps",
notes = "GP-97. Comparison with random search",
}
@InProceedings{huelsbergen:1998:fgsppemlr,
author = "Lorenz Huelsbergen",
title = "Finding General Solutions to the Parity Problem by
Evolving Machine-Language Representations",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "158--166",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
email = "lorenz@research.bell-labs.com",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-548-7",
URL = "http://cm.bell-labs.com/cm/cs/who/lorenz/papers/gp98.ps",
notes = "GP-98",
}
@InProceedings{huelsbergen:2005:CEC,
author = "Lorenz Huelsbergen",
title = "Fast Evolution of Custom Machine Representations",
booktitle = "Proceedings of the 2005 IEEE Congress on Evolutionary
Computation",
year = "2005",
editor = "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",
volume = "1",
pages = "97--104",
address = "Edinburgh, UK",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "2-5 " # sep,
organisation = "IEEE Computational Intelligence Society, Institution
of Electrical Engineers (IEE), Evolutionary Programming
Society (EPS)",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-9363-5",
URL = "http://netlib.bell-labs.com/who/lorenz/papers/huelsbergen-cec2005.pdf",
abstract = "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.",
notes = "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.",
}
@InProceedings{Hugosson:2007:pliks,
author = "Jonatan Hugosson and Erik Hemberg and Anthony Brabazon
and Michael O'Neill",
title = "An investigation of the mutation operator using
different representations in Grammatical Evolution",
booktitle = "2nd International Symposium {"}Advances in Artificial
Intelligence and Applications{"}",
year = "2007",
volume = "2",
pages = "409--419",
address = "Wisla, Poland",
month = oct # " 15-17",
keywords = "genetic algorithms, genetic programming, grammatical
evolution",
ISSN = "1896 7094",
URL = "http://www.proceedings2007.imcsit.org/pliks/45.pdf",
abstract = "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.",
}
@Article{Hugosson2009,
author = "Jonatan Hugosson and Erik Hemberg and Anthony Brabazon
and Michael O'Neill",
title = "Genotype representations in grammatical evolution",
journal = "Applied Soft Computing",
volume = "10",
number = "1",
pages = "36--43",
year = "2010",
month = jan,
keywords = "genetic algorithms, genetic programming, Grammatical
evolution, Representation",
doi = "doi:10.1016/j.asoc.2009.05.003",
URL = "http://www.sciencedirect.com/science/article/B6W86-4WGK6J4-1/2/69a04787be7085909d54edcef2d4d45a",
abstract = "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.",
}
@InCollection{hui:2003:UGPPTFSP,
author = "Anthony Hui",
title = "Using Genetic Programming to Perform Time-Series
Forecasting of Stock Prices",
booktitle = "Genetic Algorithms and Genetic Programming at Stanford
2003",
year = "2003",
editor = "John R. Koza",
pages = "83--90",
address = "Stanford, California, 94305-3079 USA",
month = "4 " # dec,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.genetic-programming.org/sp2003/Hui.pdf",
notes = "part of \cite{koza:2003:gagp}",
}
@InProceedings{Hulse:1997:dgpj,
author = "Paul Hulse and Richard Gerber and Jenanne Price",
title = "Distributed Genetic Programming In {Java}",
booktitle = "Late Breaking Papers at the 1997 Genetic Programming
Conference",
year = "1997",
editor = "John R. Koza",
pages = "81--86",
address = "Stanford University, CA, USA",
publisher_address = "Stanford University, Stanford, California,
94305-3079, USA",
month = "13--16 " # jul,
publisher = "Stanford Bookstore",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-18-206995-8",
notes = "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",
}
@Article{Hung:2006:NA,
author = "Chun-Min Hung and Yueh-Min Huang and Ming-Shi Chang",
title = "Alignment using genetic programming with causal trees
for identification of protein functions",
journal = "Nonlinear Analysis",
year = "2006",
volume = "65",
number = "5",
pages = "1070--1093",
month = "1 " # sep,
keywords = "genetic algorithms, genetic programming",
doi = "doi:10.1016/j.na.2005.09.048",
abstract = "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.",
notes = "Hybrid Systems and Applications",
}
@InProceedings{Hung:2010:IEEM,
author = "Ching-Tsung Hung and Shih-Huang Chen",
title = "A comparison of three forecasting methods to establish
a flexible pavement serviceability index",
booktitle = "2010 IEEE International Conference on Industrial
Engineering and Engineering Management (IEEM)",
year = "2010",
month = dec,
pages = "926--929",
abstract = "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.",
keywords = "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",
doi = "doi:10.1109/IEEM.2010.5674216",
ISSN = "2157-3611",
notes = "Also known as \cite{5674216}",
}
@InProceedings{Hunt:2010:ACAI,
author = "Rachel Hunt and Mark Johnston and Will N. Browne and
Mengjie Zhang",
title = "Sampling Methods in Genetic Programming for
Classification with Unbalanced Data",
booktitle = "Australasian Conference on Artificial Intelligence",
year = "2010",
editor = "Jiuyong Li",
volume = "6464",
series = "Lecture Notes in Computer Science",
pages = "273--282",
publisher = "Springer",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-3-642-17431-5",
doi = "doi:10.1007/978-3-642-17432-2_28",
size = "10 pages",
bibdate = "2010-11-30",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/ausai/ausai2010.html#HuntJBZ10",
abstract = "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.",
affiliation = "School of Mathematics, Statistics and Operations
Research, Victoria University of Wellington, P.O. Box
600, Wellington, New Zealand",
}
@InProceedings{hunter:2002:ECAI,
author = "Andrew Hunter",
title = "Using multiobjective genetic programming to infer
logistic polynomial regression models",
booktitle = "15th European Conference on Artificial Intelligence",
year = "2002",
editor = "Frank {Van Harmelen}",
pages = "193--197",
address = "Lyon, France",
month = "21-26 " # jul,
publisher = "IOS Press",
keywords = "genetic algorithms, genetic programming",
notes = "ECAI 2002
http://ecai2002.univ-lyon1.fr/show_en.pl?page=en/program/ecai.html",
}
@InProceedings{Huo:2007:ICMA,
author = "Limin Huo and Xinqiao Fan and Yunfang Xie and Jinliang
Yin",
title = "Short-Term Load Forecasting Based on the Method of
Genetic Programming",
booktitle = "International Conference on Mechatronics and
Automation, ICMA 2007",
year = "2007",
pages = "839--843",
address = "Harbin, China",
month = "5-8 " # aug,
publisher = "IEEE",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-0828-3",
doi = "doi:10.1109/ICMA.2007.4303654",
abstract = "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.",
notes = "Department of Mechanical and Electronic Engineering,
Agricultural University of Hebei, Baoding 071001,
China.",
}
@InProceedings{Huo:2008:ICICTA,
author = "Limin Huo and Jinliang Yin and Yao Yu and Liguo
Zhang",
title = "Distribution Network Reconfiguration Based on Load
Forecasting",
booktitle = "International Conference on Intelligent Computation
Technology and Automation, ICICTA 2008",
year = "2008",
month = oct,
volume = "1",
pages = "1039--1043",
keywords = "genetic algorithms, genetic programming, decision
making, distribution network reconfiguration, line loss
calculation data, load forecasting, partheno-genetic
programming algorithm, distribution networks, load
forecasting",
doi = "doi:10.1109/ICICTA.2008.206",
abstract = "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.",
notes = "Also known as \cite{4659648}",
}
@InProceedings{Hussain:2000:GECCO,
author = "Daniar Hussain and Steven Malliaris",
title = "Evolutionary Techniques Applied to Hashing: An
efficient data retrieval method",
pages = "760",
year = "2000",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2000)",
editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz
and Lee Spector and Ian Parmee and Hans-Georg Beyer",
address = "Las Vegas, Nevada, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "10-12 " # jul,
keywords = "genetic algorithms, genetic programming, hashing,
poster",
ISBN = "1-55860-708-0",
URL = "http://www.insanemath.com/hash/",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/RW054.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/RW054.ps",
size = "1 page",
abstract = "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.",
notes = "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
\cite{whitley:2000:GECCO}",
}
@InProceedings{hussain:1998:bpage,
author = "Talib S. Hussain and Roger A. Browse",
title = "Basic Properties of Attribute Grammar Encoding",
booktitle = "Late Breaking Papers at the Genetic Programming 1998
Conference",
year = "1998",
editor = "John R. Koza",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "Stanford, CA, USA",
month = "22-25 " # jul,
publisher = "Stanford University Bookstore",
keywords = "genetic algorithms, genetic programming",
URL = "http://openmap.bbn.com/~thussain/publications/1998_gp98paper.pdf",
notes = "GP-98LB, GP-98PhD Student Workshop",
}
@Misc{oai:CiteSeerPSU:397503,
title = "Genetic Encoding of Neural Networks using Attribute
Grammars",
author = "Talib S. Hussain and Roger A. Browse",
year = "1998",
booktitle = "CITO Researcher Retreat",
address = "Hamilton, Ontario, Canada",
month = may # " 12-14",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.queensu.ca/RPL/Publications/Talib_Browse/1998_cito98paper.ps.gz",
URL = "http://citeseer.ist.psu.edu/397503.html",
citeseer-isreferencedby = "oai:CiteSeerPSU:54190",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:397503",
size = "4 pages",
abstract = "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.",
}
@InProceedings{oai:CiteSeerPSU:393107,
title = "Attribute Grammars for Genetic Representations of
Neural Networks and Syntactic Constraints of Genetic
Programming",
author = "Talib S. Hussain and Roger A. Browse",
year = "1998",
booktitle = "Workshop on Evolutionary Computation. Held at the 12
Canadian Conference on Artificial Intelligence",
address = "Vancouver, Canada",
month = "17 " # jun,
keywords = "genetic algorithms, genetic programming, grammar",
URL = "http://www.cs.queensu.ca/RPL/Publications/Talib_Browse/1998_aivigi98workshop.ps.gz",
URL = "http://citeseer.ist.psu.edu/393107.html",
size = "3 pages",
citeseer-isreferencedby = "oai:CiteSeerPSU:53251",
annote = "The Pennsylvania State University CiteSeer Archives",
language = "en",
oai = "oai:CiteSeerPSU:393107",
rights = "unrestricted",
abstract = "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.",
}
@InProceedings{hussain:1999:W,
author = "Talib S. Hussain",
title = "Workshop on advanced grammar techniques within genetic
programming and evolutionary computation",
booktitle = "Advanced Grammar Techniques Within Genetic Programming
and Evolutionary Computation",
year = "1999",
editor = "Talib S. Hussain",
pages = "72",
address = "Orlando, Florida, USA",
month = "13 " # jul,
keywords = "genetic algorithms, genetic programming, grammar,
ANN",
URL = "http://openmap.bbn.com/~thussain/publications/1999_gecco99bofworkshop.pdf",
notes = "GECCO-99WKS Part of wu:1999:GECCOWKS",
}
@InProceedings{hussain:1999:G,
author = "Talib S. Hussain and Roger A. Browse",
title = "Genetic operators with dynamic biases that operate on
attribute grammar representations of neural networks",
booktitle = "Advanced Grammar Techniques Within Genetic Programming
and Evolutionary Computation",
year = "1999",
editor = "Talib S. Hussain",
pages = "83--86",
address = "Orlando, Florida, USA",
month = "13 " # jul,
keywords = "genetic algorithms, genetic programming, grammar,
ANN",
notes = "GECCO-99WKS Part of wu:1999:GECCOWKS",
}
@InProceedings{hussian:2000:mwmugp,
author = "Abo El-Abbass Hussian and Alaa Sheta and Mahmoud Kamel
and Mohamed Telbaney and Ashraf Abdelwahab",
title = "Modeling of a Winding Machine Using Genetic
Programming",
booktitle = "Proceedings of the 2000 Congress on Evolutionary
Computation CEC00",
year = "2000",
pages = "398--402",
address = "La Jolla Marriott Hotel La Jolla, California, USA",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "6-9 " # jul,
organisation = "IEEE Neural Network Council (NNC), Evolutionary
Programming Society (EPS), Institution of Electrical
Engineers (IEE)",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, control
system design",
ISBN = "0-7803-6375-2",
abstract = "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.",
notes = "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",
}
@InProceedings{hwang:2003:ICICS,
author = "Wen-Jyi Hwang and Chien-Min Ou and Rui-Chuan Lin and
Wen-Wei Hu",
title = "Genetic Programming for Robust Video Transmission",
booktitle = "International Conference on Informatics, Cybernetics,
and Systems, ICICS 2003",
year = "2003",
editor = "Xuemin Chen",
address = "I-Shou University, Kaohsiung, Taiwan",
month = dec # " 14-16",
organisation = "I-Shou University, IEEE Taipei Section",
keywords = "genetic algorithms, genetic programming",
notes = "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",
}
@Article{Hwang:2004:N,
author = "Wen-Jyi Hwang and Chien-Min Ou and Rui-Chuan Lin and
Wen-Wei Hu",
title = "Layered video transmission based on genetic
programming for lossy channels",
journal = "Neurocomputing",
year = "2004",
volume = "57",
pages = "361--372",
owner = "wlangdon",
note = "New Aspects in Neurocomputing: 10th European Symposium
on Artificial Neural Networks 2002",
keywords = "genetic algorithms, genetic programming, Genetic
algorithm, Video transmission, Wavelet transform",
ISSN = "0925-2312",
URL = "http://www.sciencedirect.com/science/article/B6V10-4BJ23B3-1/2/4d871f85b5d703962a9dd8745bac3672",
doi = "doi:10.1016/j.neucom.2003.10.013",
abstract = "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.",
}
@InProceedings{DBLP:conf/gecco/HydeBK09,
author = "Matthew R. Hyde and Edmund K. Burke and Graham
Kendall",
title = "Evolving human-competitive reusable 2{D} strip packing
heuristics",
booktitle = "GECCO-2009 Workshop on Automated heuristic design:
crossing the chasm for search methods",
year = "2009",
editor = "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",
pages = "2189--2192",
address = "Montreal",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "8-12 " # jul,
organisation = "SigEvo",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-60558-325-9",
bibsource = "DBLP, http://dblp.uni-trier.de",
doi = "doi:10.1145/1570256.1570299",
abstract = "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.",
notes = "Distributed on CD-ROM at GECCO-2009.
ACM Order Number 910092.",
}
@InProceedings{Hyde:2010:cec,
author = "Edmund K. Burke and Matthew R. Hyde and Graham
Kendall",
title = "Providing a memory mechanism to enhance the
evolutionary design of heuristics",
booktitle = "IEEE Congress on Evolutionary Computation (CEC 2010)",
year = "2010",
address = "Barcelona, Spain",
month = "18-23 " # jul,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4244-6910-9",
abstract = "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.",
doi = "doi:10.1109/CEC.2010.5586388",
notes = "WCCI 2010. Also known as \cite{5586388}",
}
@Article{Hyde:2011:ieeeTEC,
author = "Edmund K. Burke and Matthew Hyde and Graham Kendall
and John Woodward",
title = "A Genetic Programming Hyper-Heuristic Approach for
Evolving 2-{D} Strip Packing Heuristics",
journal = "IEEE Transactions on Evolutionary Computation",
year = "2010",
volume = "14",
number = "6",
pages = "942--958",
month = dec,
keywords = "genetic algorithms, genetic programming, volutionary
computation, evolving 2D strip packing heuristics,
genetic programming hyper heuristic approach, search
methodologies, computational complexity, search
problems",
ISSN = "1089-778X",
doi = "doi:10.1109/TEVC.2010.2041061",
abstract = "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.",
notes = "also known as \cite{5491153}",
}
@PhdThesis{Hyde:thesis,
author = "Matthew Hyde",
title = "A genetic programming hyper-heuristic approach to
automated packing",
school = "School of Computer Science, University of Nottingham",
year = "2010",
address = "UK",
month = mar,
keywords = "genetic algorithms, genetic programming",
URL = "http://etheses.nottingham.ac.uk/1625/1/mvh_corrected_thesis.pdf",
URL = "http://etheses.nottingham.ac.uk/1625/",
size = "226 pages",
bibsource = "OAI-PMH server at etheses.nottingham.ac.uk",
oai = "oai:etheses.nottingham.ac.uk:1625",
abstract = "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.",
}
@Article{Hyde:2011:EC,
author = "Edmund K. Burke and Matthew R. Hyde and Graham Kendall
and John Woodward",
title = "Automating the Packing Heuristic Design Process with
Genetic Programming",
journal = "Evolutionary Computation",
year = "2012",
volume = "20",
number = "1",
pages = "63--89",
month = "Spring",
keywords = "genetic algorithms, genetic programming, evolutionary
design, cutting and packing, hyper-heuristicsn",
ISSN = "1063-6560",
doi = "doi:10.1162/EVCO_a_00044",
size = "25 pages",
abstract = "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.",
}
@InProceedings{ga96aHyotyniemi,
author = "Heikki Hy{\"o}tyniemi and Heikki Koivo",
title = "Genes, codes, and dynamic systems",
pages = "225--232",
year = "1996",
editor = "Jarmo T. Alander",
booktitle = "Proceedings of the Second Nordic Workshop on Genetic
Algorithms and their Applications (2NWGA)",
series = "Proceedings of the University of Vaasa, Nro. 13",
publisher = "University of Vaasa",
address = "Vaasa (Finland)",
month = "19.-23.~" # aug,
organisation = "Finnish Artificial Intelligence Society",
URL = "ftp://ftp.uwasa.fi/cs/2NWGA/Hyotyniemi.ps.Z",
notes = "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 \cite{hyotyniemi:1996:STeP}",
}
@InProceedings{hyotyniemi:1996:STeP,
author = "Heikki Hy{\"o}tyniemi",
title = "Turing Machines are Recurrent Neural Networks",
booktitle = "Proceedings of STeP'96",
year = "1996",
editor = "Jarmo Alander and Timo Honkela and Matti Jakobsson",
pages = "13--24",
publisher = "Finnish Artificial Intelligence Society",
URL = "http://www.uwasa.fi/stes/step96/step96/hyotyniemi1/",
URL = "http://www.hut.fi/~hhyotyni/HH1/HH1.ps",
abstract = "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.",
}
@InProceedings{Iba:1993:elpbsc,
author = "Hitoshi Iba and Hugo {de Garis} and Tetsuya Higuchi",
title = "Evolutionary learning of predatory behaviors based on
structured classifiers",
booktitle = "From Animals to Animats 2: Proceedings of the Second
International Conference on Simulation of Adaptive
Behavior",
year = "1993",
editor = "Jean-Arcady Meyer and Herbert L. Roitblat and Stewart
W. Wilson",
pages = "356--363",
publisher = "MIT Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-262-63149-0",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Iba_1993_elpbsc.pdf",
URL = "http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=3971",
URL = "http://citeseerx.ist.psu.edu/showciting?cid=38619",
size = "8 pages",
notes = "SAB'92 http://www.isab.org/confs/sab92.php",
}
@TechReport{Iba:1993:sipsGA,
author = "Hitoshi Iba and Hugo {de Garis} and Taisuke Sato",
title = "Solving identification problems by structured genetic
algorithms",
institution = "Electrotechnical Laboratory",
year = "1993",
type = "Technical report",
number = "ETL-TR-93-17",
address = "1-1-4 Umezono, Tsukuba-city, Ibaraki, 305, Japan",
month = "10 " # aug,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Iba_1993_sipsGA.pdf",
notes = "This paper is based on our earlier results presented
at ICGA93 \cite{icga93:iba}",
size = "26 pages Iba:1993:sipsGA.pdf missing figure 9",
}
@TechReport{etl-tr-93-25,
author = "Hitoshi Iba and Tatsuya Niwa and Taisuke Sato",
title = "Evolutionary Learning of {Boolean} Concepts: An
empirical Study",
institution = "Electrotechnical Laboratory",
year = "1993",
number = "ETL-TR-93-25",
address = "1-1-4 Umezono, Tsukuba-city, Ibaraki, 305, Japan",
month = "18 " # oct,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/etl-tr-93-25.pdf",
size = "6 pages",
}
@InProceedings{icga93:iba,
author = "Hitoshi Iba and Takio Karita and Hugo {de Garis} and
Taisuke Sato",
title = "System Identification Using Structured Genetic
Algorithms",
year = "1993",
booktitle = "Proceedings of the 5th International Conference on
Genetic Algorithms, ICGA-93",
editor = "Stephanie Forrest",
publisher = "Morgan Kaufmann",
address = "University of Illinois at Urbana-Champaign",
month = "17-21 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "279--286",
size = "8 pages",
notes = "Hierarchical tree GA, used for learning sequence of
multiple variables and then predicting, STOGANOFF. See
also \cite{Iba:1993:sipsGA}",
}
@InCollection{kinnear:iba,
title = "Genetic Programming Using a Minimum Description Length
Principle",
author = "Hitoshi Iba and Hugo {de Garis} and Taisuke Sato",
booktitle = "Advances in Genetic Programming",
publisher = "MIT Press",
editor = "Kenneth E. {Kinnear, Jr.}",
year = "1994",
pages = "265--284",
chapter = "12",
size = "15 pages",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/etl-tr-93-15.pdf",
URL = "http://citeseer.ist.psu.edu/327857.html",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262111888",
keywords = "genetic algorithms, genetic programming",
abstract = "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...",
notes = "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?
",
}
@InProceedings{Iba:1992:mlslsGA,
author = "Hitoshi Iba and Taisuke Sato",
title = "Meta-level strategy learning for {GA} based on
structured representation",
booktitle = "Proceedings of the Second Pacific Rim International
Conference on Artificial Intelligence",
year = "1992",
editor = "Jin-Hyung Kim",
pages = "548--554",
address = "Seoul, Korea",
month = "15-18 " # sep,
organisation = "Center for Artificial Intelligence Research, Kaist",
broken_isbn = "89-85368-00-093560",
keywords = "genetic algorithms, genetic programming",
size = "7 pages",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Iba_1992_mlslsGA.pdf",
notes = "ETL-TR92-12 http://www.pricai.org/pricai-92.html",
}
@TechReport{Iba:1992:eSsp,
author = "H. Iba and T. Sato",
title = "Extension of {STROGANOFF} for symbolic problems",
institution = "Electrotechnical Laboratory",
year = "1992",
type = "Technical report",
number = "ETL-TR-94-1",
address = "1-1-4 Umezono, Tsukuba-city, Ibaraki, 305, Japan",
keywords = "genetic algorithms, genetic programming",
}
@InProceedings{DBLP:conf/ijcai/IbaHGS93,
author = "Hitoshi Iba and Tetsuya Higuchi and Hugo {de Garis}
and Taisuke Sato",
title = "Evolutionary Learning Strategy using Bug-Based
Search",
booktitle = "Proceedings of the 13th International Joint Conference
on Artificial Intelligence",
year = "1993",
bibsource = "DBLP, http://dblp.uni-trier.de",
editor = "Ruzena Bajcsy",
volume = "1",
pages = "960--966",
address = "Chambery, France",
month = aug # " 28 - " # sep # " 3",
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms",
ISBN = "1-55860-300-X",
URL = "http://ijcai.org/Past%20Proceedings/IJCAI-93-VOL2/PDF/018.pdf",
notes = "IJCAI",
}
@InProceedings{Iba:1994:siGP,
author = "Hitoshi Iba and Taisuke Sato and Hugo {de Garis}",
title = "System identification approach to genetic
programming",
booktitle = "Proceedings of the 1994 IEEE World Congress on
Computational Intelligence",
year = "1994",
pages = "401--406",
volume = "1",
address = "Orlando, Florida, USA",
month = "27-29 " # jun,
publisher = "IEEE Press",
keywords = "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",
size = "6 pages",
doi = "doi:10.1109/ICEC.1994.349917",
abstract = "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",
}
@TechReport{Iba:1994:GPlHC,
author = "Hitoshi Iba and Taisuke Sato",
title = "Genetic Programming with Local Hill-Climbing",
institution = "Electrotechnical Laboratory",
year = "1994",
number = "ETL-TR-94-4",
address = "1-1-4 Umezono, Tsukuba-city, Ibaraki, 305, Japan",
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Iba_1994_GPlHC.pdf",
notes = "Also published in PPSN-94, see
\cite{iba:1994:GPlHCppsn3}
",
size = "16 pages",
}
@InProceedings{iba:1994:GPlHCppsn3,
author = "Hitoshi Iba and Hugo {de Garis} and Taisuke Sato",
title = "Genetic Programming with Local Hill-Climbing",
booktitle = "Parallel Problem Solving from Nature III",
year = "1994",
editor = "Yuval Davidor and Hans-Paul Schwefel and Reinhard
M{\"a}nner",
series = "LNCS",
volume = "866",
pages = "334--343",
address = "Jerusalem",
publisher_address = "Berlin, Germany",
month = "9-14 " # oct,
publisher = "Springer-Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-58484-6",
URL = "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-540-58484-6",
doi = "doi:10.1007/3-540-58484-6_274",
size = "10 pages",
abstract = "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.",
notes = "'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 \cite{Iba:1994:GPlHC}",
}
@Book{iba:1994:GA,
author = "Hitoshi Iba",
title = "Introduction to Genetic Algorithms",
publisher = "Ohm-sha",
year = "1994",
keywords = "genetic algorithms",
notes = "in Japanese",
size = "pages",
}
@InProceedings{iba:1995:nGPsi,
author = "Hitoshi Iba and Taisuke Sato and Hugo {de Garis}",
title = "Numerical Genetic Programming for System
Identification",
booktitle = "Proceedings of the Workshop on Genetic Programming:
From Theory to Real-World Applications",
year = "1995",
editor = "Justinian P. Rosca",
pages = "64--75",
address = "Tahoe City, California, USA",
month = "9 " # jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/iba_1995_nGPsi.pdf",
size = "12 pages",
notes = "This paper based on earlier results (\cite{icga93:iba}
\cite{Iba:1994:siGP} and ETL-TR-94-20 1994 (submitted
to ICEC-95, see \cite{iba:1885:rgn})). part of
\cite{rosca:1995:ml}",
}
@InProceedings{Iba:1995:tdpGP,
author = "Hitoshi Iba and Hugo {de Garis} and Taisuke Sato",
title = "Temporal Data Processing Using Genetic Programming",
booktitle = "Genetic Algorithms: Proceedings of the Sixth
International Conference (ICGA95)",
year = "1995",
editor = "Larry J. Eshelman",
pages = "279--286",
address = "Pittsburgh, PA, USA",
publisher_address = "San Francisco, CA, USA",
month = "15-19 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-370-0",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/iba_1995_tdpgp.pdf",
size = "8 pages",
abstract = "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'
",
}
@InProceedings{iba:1885:rgn,
author = "Hitoshi Iba and Taisuke Sato and Hugo {de Garis}",
title = "Recombination Guidance for Numerical Genetic
Programming",
booktitle = "1995 IEEE Conference on Evolutionary Computation",
year = "1995",
volume = "1",
pages = "97--102",
address = "Perth, Australia",
publisher_address = "Piscataway, NJ, USA",
month = "29 " # nov # " - 1 " # dec,
publisher = "IEEE Press",
keywords = "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",
ISBN = "0-7803-2759-4",
doi = "doi:10.1109/ICEC.1995.489292",
size = "6 pages",
abstract = "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.",
notes = "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.",
}
@InCollection{iba:1996:aigp2,
author = "Hitoshi Iba and Hugo {de Garis}",
title = "Extending Genetic Programming with Recombinative
Guidance",
booktitle = "Advances in Genetic Programming 2",
publisher = "MIT Press",
year = "1996",
editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}",
pages = "69--88",
chapter = "4",
address = "Cambridge, MA, USA",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-262-01158-1",
URL = "http://cisnet.mit.edu/Advances-in-Genetic-Programming/92",
abstract = "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.",
}
@InProceedings{iba:1996:ecma,
author = "Hitoshi Iba",
title = "Emergent Cooperation for Multiple Agents using Genetic
Programming",
booktitle = "Late Breaking Papers at the Genetic Programming 1996
Conference Stanford University July 28-31, 1996",
year = "1996",
editor = "John R. Koza",
pages = "66--74",
address = "Stanford University, CA, USA",
publisher_address = "Stanford University, Stanford, California
94305-3079, USA",
month = "28--31 " # jul,
publisher = "Stanford Bookstore",
ISBN = "0-18-201031-7",
keywords = "genetic algorithms, genetic programming",
notes = "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
\cite{iba:1996:ecmaPPSN}",
}
@TechReport{iba:1995:rtgTR,
author = "Hitoshi Iba",
title = "Random Tree Generation for Genetic Programming",
institution = "ElectroTechnical Laboratory (ETL)",
year = "1995",
number = "ETL-TR-95-35",
address = "1-1-4 Umezono, Tsukuba-city, Ibaraki, 305, Japan",
month = "14 " # nov,
keywords = "genetic algorithms, genetic programming",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/iba_1995_rtgTR.pdf",
size = "24 pages",
}
@InProceedings{iba:1996:rtg,
author = "Hitoshi Iba",
title = "Random Tree Generation for Genetic Programming",
booktitle = "Late Breaking Papers at the Genetic Programming 1996
Conference Stanford University July 28-31, 1996",
year = "1996",
editor = "John R. Koza",
pages = "75--82",
address = "Stanford University, CA, USA",
publisher_address = "Stanford University, Stanford, California
94305-3079, USA",
month = "28--31 " # jul,
publisher = "Stanford Bookstore",
ISBN = "0-18-201031-7",
keywords = "genetic algorithms, genetic programming",
notes = "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",
}
@InProceedings{iba:1996:rtgGP,
author = "Hitoshi Iba",
title = "Random Tree Generation for Genetic Programming",
booktitle = "Parallel Problem Solving from Nature IV, Proceedings
of the International Conference on Evolutionary
Computation",
year = "1996",
editor = "Hans-Michael Voigt and Werner Ebeling and Ingo
Rechenberg and Hans-Paul Schwefel",
series = "LNCS",
volume = "1141",
pages = "144--153",
address = "Berlin, Germany",
publisher_address = "Heidelberg, Germany",
month = "22-26 " # sep,
publisher = "Springer Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-61723-X",
doi = "doi:10.1007/3-540-61723-X_978",
size = "10 pages",
abstract = "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",
notes = "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)",
affiliation = "Electrotechnical Laboratory (ETL) Machine Inference
Section 1-1-4 Umezono, Tsukuba Science City 305 Ibaraki
Japan 1-1-4 Umezono, Tsukuba Science City 305 Ibaraki
Japan",
}
@InProceedings{iba:1996:ecmaPPSN,
author = "Hitoshi Iba",
title = "Emergent Cooperation for Multiple Agents Using Genetic
Programming",
booktitle = "Parallel Problem Solving from Nature IV, Proceedings
of the International Conference on Evolutionary
Computation",
year = "1996",
editor = "Hans-Michael Voigt and Werner Ebeling and Ingo
Rechenberg and Hans-Paul Schwefel",
series = "LNCS",
volume = "1141",
pages = "32--41",
address = "Berlin, Germany",
publisher_address = "Heidelberg, Germany",
month = "22-26 " # sep,
publisher = "Springer Verlag",
keywords = "genetic algorithms, genetic programming",
ISBN = "3-540-61723-X",
doi = "doi:10.1007/3-540-61723-X_967",
size = "10 pages",
abstract = "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.",
notes = "http://lautaro.fb10.tu-berlin.de/ppsniv.html PPSN4
Comparison of homogeneous, heterogeneous and
co-evolutionary breeding on 'Tile world' simulated
environment problem.",
affiliation = "Electrotechnical Laboratory (ETL) Machine Inference
Section 1-1-4 Umezono, Tsukuba Science City 305 Ibaraki
Japan 1-1-4 Umezono, Tsukuba Science City 305 Ibaraki
Japan",
}
@Book{iba:1996:GP,
author = "Hitoshi Iba",
title = "Genetic Programming",
publisher = "Tokyo Denki University Press",
year = "1996",
keywords = "genetic algorithms, genetic programming",
notes = "in Japanese",
size = "pages",
}
@InProceedings{iba:1997:eca,
author = "Hitoshi Iba and Tishihide Nozoe and Kanji Ueda",
title = "Evolving Communicating Agents based on Genetic
Programming",
booktitle = "Proceedings of the 1997 {IEEE} International
Conference on Evolutionary Computation",
year = "1997",
pages = "297--302",
address = "Indianapolis, IN, USA",
publisher_address = "Piscataway, NJ, USA",
month = "13-16 " # apr,
publisher = "IEEE Press",
keywords = "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",
ISBN = "0-7803-3949-5",
doi = "doi:10.1109/ICEC.1997.592321",
size = "6 pages",
abstract = "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",
notes = "ICEC-97",
}
@InProceedings{iba:1997:malrntGP,
author = "Hitoshi Iba",
title = "Multiple-Agent Learning for a Robot Navigation Task by
Genetic Programming",
booktitle = "Genetic Programming 1997: Proceedings of the Second
Annual Conference",
editor = "John R. Koza and Kalyanmoy Deb and Marco Dorigo and
David B. Fogel and Max Garzon and Hitoshi Iba and Rick
L. Riolo",
year = "1997",
month = "13-16 " # jul,
keywords = "genetic algorithms, genetic programming",
pages = "195--200",
address = "Stanford University, CA, USA",
publisher_address = "San Francisco, CA, USA",
publisher = "Morgan Kaufmann",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gp1997/iba_1997_malrntGP.pdf",
size = "6 pages",
notes = "GP-97",
}
@Unpublished{iba:1997:cfevlr,
author = "Hitoshi Iba",
title = "Complexity-based Fitness Evaluation for Variable
Length Representation",
note = "Position paper at the Workshop on Evolutionary
Computation with Variable Size Representation at
ICGA-97",
month = "20 " # jul,
year = "1997",
address = "East Lansing, MI, USA",
keywords = "genetic algorithms, genetic programming, bloat,
variable size representation",
URL = "http://coblitz.codeen.org:3125/citeseer.ist.psu.edu/cache/papers/cs/16452/http:zSzzSzwww.miv.t.u-tokyo.ac.jpzSz~ibazSztmpzSzagp94.pdf/iba94genetic.pdf",
URL = "http://citeseer.ist.psu.edu/327857.html",
abstract = "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...",
size = "3 pages",
}
@InCollection{Iba:1997:HEC,
author = "Hitoshi Iba",
title = "Complexity-based fitness evaluation",
booktitle = "Handbook of Evolutionary Computation",
publisher = "Oxford University Press",
publisher_2 = "Institute of Physics Publishing",
year = "1997",
editor = "Thomas Baeck and David B. Fogel and Zbigniew
Michalewicz",
chapter = "section C4.4",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7503-0392-1",
doi = "doi:10.1201/9781420050387.ptc",
}
@InCollection{iba:1997:HECa,
author = "Hitoshi Iba",
title = "Identification",
booktitle = "Handbook of Evolutionary Computation",
publisher = "Oxford University Press",
publisher_2 = "Institute of Physics Publishing",
year = "1997",
editor = "Thomas Baeck and David B. Fogel and Zbigniew
Michalewicz",
chapter = "section F1.4",
keywords = "genetic algorithms, genetic programming, stroganoff,
gmdh",
ISBN = "0-7503-0392-1",
URL = "http://www.crcnetbase.com/isbn/9780750308953",
size = "4 pages",
}
@InCollection{iba:1997:HECb,
author = "Hitoshi Iba",
title = "System identification using structured genetic
algorithms",
booktitle = "Handbook of Evolutionary Computation",
publisher = "Oxford University Press",
publisher_2 = "Institute of Physics Publishing",
year = "1997",
editor = "Thomas Baeck and David B. Fogel and Zbigniew
Michalewicz",
chapter = "section G1.4",
keywords = "genetic algorithms, genetic programming, stroganoff,
gmdh, sgpc version 1.1",
ISBN = "0-7503-0392-1",
doi = "doi:10.1201/9781420050387.ptg",
size = "11 pages",
}
@InProceedings{iba:1998:marlGP,
author = "Hitoshi Iba",
title = "Multi-Agent Reinforcement Learning with Genetic
Programming",
booktitle = "Genetic Programming 1998: Proceedings of the Third
Annual Conference",
year = "1998",
editor = "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",
pages = "167--172",
address = "University of Wisconsin, Madison, Wisconsin, USA",
publisher_address = "San Francisco, CA, USA",
month = "22-25 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-548-7",
notes = "GP-98",
}
@Article{Iba:1998:ISJ,
author = "Hitoshi Iba",
title = "Evolutionary learning of communicating agents",
journal = "Information Sciences",
year = "1998",
volume = "108",
number = "1-4",
pages = "181--205",
month = jul,
keywords = "genetic algorithms, genetic programming, Multi-agent
system, Distributed artificial intelligence",
ISSN = "0020-0255",
doi = "doi:10.1016/S0020-0255(97)10055-X",
URL = "http://www.sciencedirect.com/science/article/B6V0C-3TKS65B-F/2/ecac160ea272b4818c97d3aab09527d4",
abstract = "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.",
notes = "Information Sciences
http://www.elsevier.com/inca/publications/store/5/0/5/7/3/0/505730.pub.htt",
}
@InCollection{iba:1999:aigp3,
author = "Hitoshi Iba",
title = "Evolving Multiple Agents by Genetic Programming",
booktitle = "Advances in Genetic Programming 3",
publisher = "MIT Press",
year = "1999",
editor = "Lee Spector and William B. Langdon and Una-May
O'Reilly and Peter J. Angeline",
chapter = "19",
pages = "447--466",
address = "Cambridge, MA, USA",
month = jun,
keywords = "genetic algorithms, genetic programming, QGP",
ISBN = "0-262-19423-6",
URL = "http://www.cs.bham.ac.uk/~wbl/aigp3/ch19.pdf",
abstract = "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.",
notes = "AiGP3",
}
@Book{iba:1999:EC,
author = "Hitoshi Iba",
title = "Evolutionary Computing",
publisher = "Tokyo University Press",
year = "1999",
keywords = "genetic algorithms, genetic programming",
notes = "in Japanese",
size = "pages",
}
@InProceedings{iba:1999:BBBGP,
author = "Hitoshi Iba",
title = "Bagging, Boosting, and Bloating in Genetic
Programming",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1053--1060",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming, classifier
ensembles",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-407.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-407.ps",
abstract = "subpopulations",
notes = "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",
}
@InProceedings{iba:1999:UGPPFD,
author = "Hitoshi Iba and Takashi Sasaki",
title = "Using Genetic Programming to Predict Financial Data",
booktitle = "Proceedings of the Congress on Evolutionary
Computation",
year = "1999",
editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc
Schoenauer and Xin Yao and Ali Zalzala",
volume = "1",
pages = "244--251",
address = "Mayflower Hotel, Washington D.C., USA",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "6-9 " # jul,
organisation = "Congress on Evolutionary Computation, IEEE / Neural
Networks Council, Evolutionary Programming Society,
Galesia, IEE",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, time series",
ISBN = "0-7803-5536-9 (softbound)",
ISBN = "0-7803-5537-7 (Microfiche)",
notes = "CEC-99 - A joint meeting of the IEEE, Evolutionary
Programming Society, Galesia, and the IEE.
Library of Congress Number = 99-61143",
}
@InProceedings{iba:2000:CEF,
author = "Hitoshi Iba and Nikolay Nikolaev",
title = "Financial data prediction by means of genetic
programming",
booktitle = "Computing in Economics and Finance",
year = "2000",
address = "Universitat Pompeu Fabra, Barcelona, Spain",
month = "6-8 " # jul,
keywords = "genetic algorithms, genetic programming",
URL = "http://enginy.upf.es/SCE/papers/paper330.ps.gz
broken",
notes = "http://enginy.upf.es/SCE/index2.html
http://ideas.repec.org/p/sce/scecf0/z101.html
number Z101",
}
@InProceedings{Iba:2000:GECCO,
author = "Hitoshi Iba and Makoto Terao",
title = "Controlling Effective Introns for Multi-Agent Learning
by Genetic Programming",
pages = "419--426",
year = "2000",
publisher = "Morgan Kaufmann",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference (GECCO-2000)",
editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz
and Lee Spector and Ian Parmee and Hans-Georg Beyer",
address = "Las Vegas, Nevada, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "10-12 " # jul,
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-708-0",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/GP191.pdf",
URL = "http://citeseer.ist.psu.edu/478951.html",
abstract = "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.",
notes = "A joint meeting of the ninth International Conference
on Genetic Algorithms (ICGA-2000) and the fifth Annual
Genetic Programming Conference (GP-2000) Part of
\cite{whitley:2000:GECCO}",
}
@InProceedings{iba:2000:gppmfds,
author = "Hitoshi Iba and Nikolay Nikolaev",
title = "Genetic Programming Polynomial Models of Financial
Data Series",
booktitle = "Proceedings of the 2000 Congress on Evolutionary
Computation CEC00",
year = "2000",
pages = "1459--1466",
address = "La Jolla Marriott Hotel La Jolla, California, USA",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "6-9 " # jul,
organisation = "IEEE Neural Network Council (NNC), Evolutionary
Programming Society (EPS), Institution of Electrical
Engineers (IEE)",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, time series,
stroganoff",
ISBN = "0-7803-6375-2",
notes = "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",
}
@InProceedings{iba:2002:gecco,
author = "Hitoshi Iba and Erina Sakamoto",
title = "Inference Of Differential Equation Models By Genetic
Programming",
booktitle = "GECCO 2002: Proceedings of the Genetic and
Evolutionary Computation Conference",
editor = "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",
year = "2002",
pages = "788--795",
address = "New York",
publisher_address = "San Francisco, CA 94104, USA",
month = "9-13 " # jul,
publisher = "Morgan Kaufmann Publishers",
keywords = "genetic algorithms, genetic programming,
bioinformatics, differential equation, E-cell, genome
informatics, Lotka-Volterra model, S-systems",
ISBN = "1-55860-878-8",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/GP042.ps",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2002/GP042.pdf",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf",
abstract = "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.",
notes = "GECCO-2002. A joint meeting of the eleventh
International Conference on Genetic Algorithms
(ICGA-2002) and the seventh Annual Genetic Programming
Conference (GP-2002)",
}
@Article{Iba:2008:IS,
author = "Hitoshi Iba",
title = "Inference of differential equation models by genetic
programming",
journal = "Information Sciences",
year = "2008",
volume = "178",
number = "23",
pages = "4453--4468",
month = "1 " # dec,
note = "Special Section: Genetic and Evolutionary Computing",
keywords = "genetic algorithms, genetic programming, Ordinary
differential equations, Genome informatics",
ISSN = "0020-0255",
doi = "doi:10.1016/j.ins.2008.07.029",
size = "16 pages",
abstract = "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.",
notes = "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.",
}
@Book{Iba:2009:AGPML,
author = "Hitoshi Iba and Yoshihiko Hasegawa and Topon Kumar
Paul",
title = "Applied Genetic Programming and Machine Learning",
publisher = "CRC",
year = "2009",
series = "CRC Complex and Enterprise Systems Engineering",
keywords = "genetic algorithms, genetic programming",
ISBN = "1439803692",
abstract = "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.",
notes = "Book review in
\cite{Veeramachaneni: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.",
size = "349 pages",
}
@InCollection{Iba:2010:GPTP,
author = "Hitoshi Iba and Claus Aranha",
title = "Composition of Music and Financial Strategies via
Genetic Programming",
booktitle = "Genetic Programming Theory and Practice VIII",
year = "2010",
editor = "Rick Riolo and Trent McConaghy and Ekaterina
Vladislavleva",
series = "Genetic and Evolutionary Computation",
volume = "8",
address = "Ann Arbor, USA",
month = "20-22 " # may,
publisher = "Springer",
chapter = "13",
pages = "211--226",
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-1-4419-7746-5",
URL = "http://www.springer.com/computer/ai/book/978-1-4419-7746-5",
notes = "part of \cite{Riolo:2010:GPTP}",
}
@InProceedings{ibarra:2002:EuroGP,
title = "Transformation of Equational Specification by Means of
Genetic Programming",
author = "Aitor Ibarra and J. Lanchares and J. Mendias and J. I.
Hidalgo and R. Hermida",
editor = "James A. Foster and Evelyne Lutton and Julian Miller
and Conor Ryan and Andrea G. B. Tettamanzi",
booktitle = "Genetic Programming, Proceedings of the 5th European
Conference, EuroGP 2002",
volume = "2278",
series = "LNCS",
pages = "248--257",
publisher = "Springer-Verlag",
address = "Kinsale, Ireland",
publisher_address = "Berlin",
month = "3-5 " # apr,
year = "2002",
keywords = "genetic algorithms, genetic programming, FRESH",
ISBN = "3-540-43378-3",
URL = "http://link.springer-ny.com/link/service/series/0558/bibs/2278/22780248.htm",
URL = "http://link.springer-ny.com/link/service/series/0558/papers/2278/22780248.pdf",
abstract = "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.",
notes = "EuroGP'2002, part of \cite{lutton:2002:GP} Algebraic
mutation. No crossover. gpcc++ 0.5.2 Two example
equations simplified. Pop size 4. 60 percent
improvement.
",
}
@InCollection{Ichimura:2010:SOM,
title = "A Knowledge Acquisition Method of Judgment Rules for
Spam {E-mail} by using Self Organizing Map and
Automatically Defined Groups by Genetic Programming",
author = "Takumi Ichimura and Kazuya Mera and Akira Hara",
booktitle = "Self-Organizing Maps",
publisher = "InTech",
year = "2010",
editor = "George K Matsopoulos",
chapter = "24",
month = apr,
keywords = "genetic algorithms, genetic programming",
isbn13 = "978-953-307-074-2",
bibsource = "OAI-PMH server at www.intechopen.com",
language = "eng",
oai = "oai:intechopen.com:10468",
URL = "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",
abstract = "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",
notes = "http://www.intechopen.com/books/show/title/self-organizing-maps",
size = "14 pages",
}
@InProceedings{ichise:1998:ilpGP,
author = "R. Ichise",
title = "Inductive Logic Programming and Genetic Programming",
booktitle = "European Conference on Artificial Intelligence",
year = "1998",
editor = "Henri Prade",
address = "Brighton",
month = "23-28 " # aug,
keywords = "genetic algorithms, genetic programming",
notes = "ECAI-98 young researcher paper
",
}
@InProceedings{Icke:2010:geccocomp,
author = "Ilknur Icke and Andrew Rosenberg",
title = "Dimensionality reduction using symbolic regression",
booktitle = "GECCO 2010 Late breaking abstracts",
year = "2010",
editor = "Daniel Tauritz",
isbn13 = "978-1-4503-0073-5",
keywords = "genetic algorithms, genetic programming",
pages = "2085--2086",
month = "7-11 " # jul,
organisation = "SIGEVO",
address = "Portland, Oregon, USA",
doi = "doi:10.1145/1830761.1830874",
publisher = "ACM",
publisher_address = "New York, NY, USA",
abstract = "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",
notes = "Flubber, ECJ, WEKA, UCI wisconsin breast,
leptographsus crabs. Compare with PCA, MDS and random
projections. no significant improvement.
Also known as \cite{1830874} Distributed on CD-ROM at
GECCO-2010.
ACM Order Number 910102.",
}
@InProceedings{Icke:2010:WiML,
author = "Ilknur Icke and Andrew Rosenberg",
title = "Multi-Objective Genetic Programming Projection Pursuit
for Exploratory Data Modeling",
booktitle = "Workshop for Women in Machine Learning",
year = "2010",
editor = "Diane Oyen",
address = "Canada",
month = "6 " # dec,
keywords = "genetic algorithms, genetic programming, MOG3P",
URL = "http://arxiv.org/abs/1010.1888",
URL = "http://pami.uwaterloo.ca/~ealee/wiml/2010/program/WiML2010_IlknurIcke.pdf",
size = "2 pages",
abstract = "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.",
notes = "WiML 2010
http://pami.uwaterloo.ca/~ealee/wiml/2010/index.php",
}
@InProceedings{icke:2011:EuroGP,
author = "Ilknur Icke and Andrew Rosenberg",
title = "Multi-Objective Genetic Programming for Visual
Analytics",
booktitle = "Proceedings of the 14th European Conference on Genetic
Programming, EuroGP 2011",
year = "2011",
month = "27-29 " # apr,
editor = "Sara Silva and James A. Foster and Miguel Nicolau and
Mario Giacobini and Penousal Machado",
series = "LNCS",
volume = "6621",
publisher = "Springer Verlag",
address = "Turin, Italy",
pages = "322--334",
organisation = "EvoStar",
keywords = "genetic algorithms, genetic programming: poster",
doi = "doi:10.1007/978-3-642-20407-4_28",
abstract = "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.",
notes = "Part of \cite{Silva:2011:GP} EuroGP'2011 held in
conjunction with EvoCOP2011 EvoBIO2011 and
EvoApplications2011",
}
@InProceedings{igel:98,
author = "Christian Igel",
title = "Causality of Hierarchical Variable Length
Representations",
booktitle = "Proceedings of the 1998 IEEE World Congress on
Computational Intelligence",
year = "1998",
pages = "324--329",
address = "Anchorage, Alaska, USA",
month = "5-9 " # may,
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-4869-9",
URL = "http://www.neuroinformatik.ruhr-uni-bochum.de/PEOPLE/igel/CoHVLR.ps.gz",
size = "6 pages",
abstract = "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.",
notes = "ICEC-98 Held In Conjunction With WCCI-98 --- 1998 IEEE
World Congress on Computational Intelligence",
}
@InCollection{igel:1999:aigp3,
author = "Christian Igel and Kumar Chellapilla",
title = "Fitness Distributions: Tools for Designing Efficient
Evolutionary Computations",
booktitle = "Advances in Genetic Programming 3",
publisher = "MIT Press",
year = "1999",
editor = "Lee Spector and William B. Langdon and Una-May
O'Reilly and Peter J. Angeline",
chapter = "9",
pages = "191--216",
address = "Cambridge, MA, USA",
month = jun,
keywords = "genetic algorithms, genetic programming",
ISBN = "0-262-19423-6",
URL = "http://www.cs.bham.ac.uk/~wbl/aigp3/ch09.pdf",
URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.867",
abstract = "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.",
notes = "AiGP3",
}
@InProceedings{igel:1999:UFDIELS,
author = "Christian Igel and Martin Kreutz",
title = "Using Fitness Distributions to Improve the Evolution
of Learning Structures",
booktitle = "Proceedings of the Congress on Evolutionary
Computation",
year = "1999",
editor = "Peter J. Angeline and Zbyszek Michalewicz and Marc
Schoenauer and Xin Yao and Ali Zalzala",
volume = "3",
pages = "1902--1909",
address = "Mayflower Hotel, Washington D.C., USA",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "6-9 " # jul,
organisation = "Congress on Evolutionary Computation, IEEE / Neural
Networks Council, Evolutionary Programming Society,
Galesia, IEE",
publisher = "IEEE Press",
keywords = "genetic algorithms, genetic programming, fitness
distributions, density estimation, gradient-based
operators",
ISBN = "0-7803-5536-9 (softbound)",
ISBN = "0-7803-5537-7 (Microfiche)",
URL = "http://www.neuroinformatik.ruhr-uni-bochum.de/ini/PEOPLE/igel/UFDtItEoLS.ps.gz",
URL = "http://citeseer.ist.psu.edu/294668.html",
abstract = "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.",
notes = "CEC-99 - A joint meeting of the IEEE, Evolutionary
Programming Society, Galesia, and the IEE.
Library of Congress Number = 99-61143",
}
@InProceedings{igel:1999:IIDDGCFGP,
author = "Christian Igel and Kumar Chellapilla",
title = "Investigating the Influence of Depth and Degree of
Genotypic Change on Fitness in Genetic Programming",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1061--1068",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "genetic algorithms, genetic programming",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-422.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/GP-422.ps",
abstract = "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.",
notes = "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...",
}
@Article{Igel:2003:NC,
author = "Christian Igel and Marc Toussaint",
title = "Neutrality and Self-Adaptation",
journal = "Natural Computing",
year = "2003",
volume = "2",
number = "2",
URL = "http://www.neuroinformatik.ruhr-uni-bochum.de/PEOPLE/igel/NaSA.pdf",
URL = "http://ipsapp009.kluweronline.com/content/getfile/5030/5/1/abstract.htm",
doi = "doi:10.1023/A:1024906105255",
pages = "117--132",
keywords = "genetic algorithms, genetic programming, evolutionary
computation, genotype-phenotype mapping, neutrality,
No-Free-Lunch theorem, redundancy, self-adaptation",
abstract = "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.",
notes = "Article ID: 5126729",
}
@InProceedings{iima:1999:GALSPEWPP,
author = "Hitoshi Iima and Nobuo Sannomiya",
title = "Genetic Algorithm for a Large-Scale Scheduling Problem
in an Electric Wire Production Process",
booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
year = "1999",
editor = "Wolfgang Banzhaf and Jason Daida and Agoston E. Eiben
and Max H. Garzon and Vasant Honavar and Mark Jakiela
and Robert E. Smith",
volume = "2",
pages = "1784",
address = "Orlando, Florida, USA",
publisher_address = "San Francisco, CA 94104, USA",
month = "13-17 " # jul,
publisher = "Morgan Kaufmann",
keywords = "real world applications, poster papers",
ISBN = "1-55860-611-4",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-707.pdf",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco1999/RW-707.ps",
notes = "GECCO-99 A joint meeting of the eighth international
conference on genetic algorithms (ICGA-99) and the
fourth annual genetic programming conference (GP-99)",
}
@InProceedings{Iio:2008:SICE,
author = "Takamasa Iio and Ivan Tanev and Katsunori Shimohara",
title = "Evolutionary adaptive behavior in noisy multi-agent
system",
booktitle = "SICE Annual Conference",
year = "2008",
month = "20-22 " # aug,
address = "Japan",
pages = "1506--1509",
keywords = "genetic algorithms, genetic programming, environmental
information, evolutionary adaptive behavior,
multi-agent system, perceptual noise, multi-agent
systems",
doi = "doi:10.1109/SICE.2008.4654898",
abstract = "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.",
notes = "Also known as \cite{4654898}",
}
@PhdThesis{ijspeert:thesis,
author = "Auke Jan Ijspeert",
title = "Design of artificial neural oscillatory circuits for
the control of lamprey- and salamander-like locomotion
using evolutionary algorithms",
school = "Department of Artificial Intelligence, University of
Edinburgh",
year = "1998",
address = "UK",
keywords = "genetic algorithms, artificial life, CPG",
URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/ftp.io.com/papers/ijspeert",
size = "200+ pages",
abstract = "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.",
}
@Article{oai:CiteSeerPSU:317384,
author = "Auke Jan Ijspeert and Jerome Kodjabachian",
title = "Evolution and Development of a Central Pattern
Generator for the Swimming of a Lamprey",
journal = "Artificial Life",
year = "1999",
volume = "5",
number = "3",
pages = "247--269",
month = "Summer",
keywords = "genetic algorithms, genetic programming, neural
control, developmental encoding, SGOCE, simulation,
central pattern generator, CPG, swimming, lamprey",
doi = "doi:10.1162/106454699568773",
abstract = "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.",
notes = "http://alife.tuke.sk/projekty/abstract/abstract99.html#a34
Also available as University of Edinburgh Technical
report
IngentaPDF version crashes my acrobat reader",
}
@Article{Ikeda00,
author = "Yoshikazu Ikeda and Shozo Tokinaga",
title = "Approximation of Chaotic Dynamics by Using Smaller
Number of Data Based upon the Genetic Programming and
Its Applications",
journal = "IEICE Transactions on fundamentals of electronics,
communications and computer sciences",
volume = "E83A",
number = "8",
pages = "1599--1607",
year = "2000",
keywords = "genetic algorithms, genetic programming, nonlinear
dynamics, system identification, Nonlinear Signal
Processing, chaotic dynamics,
economics,identification,prediction",
organisation = "The Institute of Electronics, Information and
Communication Engineers. JAPAN",
publisher = "Oxford University Press",
ISSN = "0916-8524",
URL = "http://search.ieice.or.jp/2000/files/e000a08.htm#e83-a,8,1599",
URL = "http://www.ee.psu.ac.th/ieice/2000/pdf/e83-a_8_1599.pdf",
abstract = "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.",
}
@Article{journals/ieicet/IkedaT07,
author = "Yoshikazu Ikeda and Shozo Tokinaga",
title = "Analysis of Price Changes in Artificial Double Auction
Markets Consisting of Multi-Agents Using Genetic
Programming for Learning and Its Applications",
journal = "IEICE Transactions on Fundamentals of Electronics,
Communications and Computer Sciences",
year = "2007",
volume = "90-A",
number = "10",
pages = "2203--2211",
keywords = "genetic algorithms, genetic programming, artificial
double auction market, multi-agents, electricity
market, control of chaos",
ISSN = "0916-8508",
doi = "doi:10.1093/ietfec/e90-a.10.2203",
abstract = "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.",
bibdate = "2008-01-15",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/ieicet/ieicet90a.html#IkedaT07",
}
@Article{journals/ieicet/IkedaT07a,
author = "Yoshikazu Ikeda and Shozo Tokinaga",
title = "Multi-Fractality Analysis of Time Series in Artificial
Stock Market Generated by Multi-Agent Systems Based on
the Genetic Programming and Its Applications",
journal = "IEICE Transactions on Fundamentals of Electronics,
Communications and Computer Sciences",
year = "2007",
volume = "90-A",
number = "10",
pages = "2212--2222",
keywords = "genetic algorithms, genetic programming,
multi-fractal, artificial stock market,
multi-agent-based modeling",
ISSN = "0916-8508",
doi = "doi:10.1093/ietfec/e90-a.10.2212",
abstract = "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.",
bibdate = "2008-01-15",
bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/ieicet/ieicet90a.html#IkedaT07a",
}
@InProceedings{PDPTA96b,
author = "I. M. Ikram",
title = "An occam Library for Genetic Programming on Transputer
Networks",
booktitle = "Proceedings of the International Conference on
Parallel and Distributed Processing Techniques and
Applications",
year = "1996",
editor = "Hamid R. Arabnia",
pages = "1186--1189",
address = "Sunnyvale, California",
month = "9-11 " # aug,
publisher = "CSREA",
keywords = "genetic algorithms, genetic programming, occam,
Transputers",
ISBN = "0-9648666-4-1",
bibsource = "DBLP, http://dblp.uni-trier.de",
abstract = "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.",
notes = "Ismail Ikram http://cs.ru.ac.za/homes/g93i0527/",
}
@InProceedings{ilakovac:1996:GANNrsvp,
author = "Tin Ilakovac and Zeljka Perkovic and Strahil Ristov",
title = "The Use of Genetic Algorithms in the Optimization of
Competitive Neural Networks which Resolve the Stuck
Vectors Problem",
booktitle = "Genetic Programming 1996: Proceedings of the First
Annual Conference",
editor = "John R. Koza and David E. Goldberg and David B. Fogel
and Rick L. Riolo",
year = "1996",
month = "28--31 " # jul,
keywords = "Genetic Algorithms",
pages = "499",
address = "Stanford University, CA, USA",
publisher = "MIT Press",
size = "1 page",
URL = "http://cognet.mit.edu/library/books/view?isbn=0262611279",
notes = "GP-96 GA paper",
}
@InProceedings{iles:2002:RDS,
author = "Michael Iles and Dwight Deugo",
title = "A search for routing strategies in a peer-to-peer
network using genetic programming",
booktitle = "Proceedings 21st IEEE Symposium on Reliable
Distributed Systems",
year = "2002",
pages = "341--346",
month = "13-16 " # oct,
keywords = "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",
ISSN = "1060-9857",
abstract = "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.",
notes = "Inspec Accession Number: 7516795.
Carleton Univ., Ottawa, Ont., Canada",
}
@Article{Ilgin2010563,
title = "Environmentally conscious manufacturing and product
recovery ({ECMPRO}): {A} review of the state of the
art",
journal = "Journal of Environmental Management",
volume = "91",
number = "3",
pages = "563--591",
year = "2010",
ISSN = "0301-4797",
doi = "doi:10.1016/j.jenvman.2009.09.037",
URL = "http://www.sciencedirect.com/science/article/B6WJ7-4XHC6JT-5/2/d21573d2beec024e5b27fd2fdb11b653",
author = "Mehmet Ali Ilgin and Surendra M. Gupta",
keywords = "genetic algorithms, genetic programming, Closed-loop
supply chains, Disassembly, Environmentally conscious
manufacturing, Environmentally conscious product
design, Product recovery, Remanufacturing, Reverse
logistics",
abstract = "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.",
notes = "survey",
}
@PhdThesis{ilich:2000:thesis,
author = "Nesa Ilich",
title = "A Strongly Feasible Evolution Program for non-linear
optimization of Network Flows",
school = "Department of Civil and Geological Sciences,
University of Manitoba",
year = "2000",
address = "Winnipeg, Canada",
month = oct,
email = "NIlich@mail.com",
keywords = "genetic algorithms, genetic programming, Evolution
Programs, Network Flows, Non-Linear Constraints",
size = "pages",
abstract = "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.",
notes = "
",
}
@InProceedings{Imada:2008:geccocomp,
author = "Janine H. Imada and Brian J. Ross",
title = "Using feature-based fitness evaluation in symbolic
regression with added noise",
year = "2008",
editor = "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",
isbn13 = "978-1-60558-131-6",
booktitle = "GECCO-2008 Late-Breaking Papers",
pages = "2153--2158",
address = "Atlanta, GA, USA",
URL = "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p2153.pdf",
doi = "doi:10.1145/1388969.1389039",
publisher = "ACM",
publisher_address = "New York, NY, USA",
month = "12-16 " # jul,
keywords = "genetic algorithms, genetic programming, noisy
signals, symbolic regression",
notes = "Distributed on CD-ROM at GECCO-2008
ACM Order Number 910081. Also known as \cite{1389039}",
}
@MastersThesis{Imada:mastersthesis,
author = "Janine Imada",
title = "Evolutionary synthesis of stochastic gene network
models using feature-based search spaces",
school = "Department of Computer Science, Brock University",
year = "2009",
type = "M.Sc. Computer Science",
address = "St. Catharines, Ontario, Canada",
month = "28 " # jan,
keywords = "genetic algorithms, genetic programming",
URL = "http://dr.library.brocku.ca/bitstream/handle/10464/2853/Brock_Imada_Janine_2009.pdf",
URL = "http://hdl.handle.net/10464/2853",
size = "138 pages",
abstract = "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.",
notes = "cited by \cite{Ross:2011:GPEM}",
}
@Article{Imada:2011:NGC,
author = "Janine Imada and Brian J. Ross",
title = "Evolutionary Synthesis of Stochastic Gene Network
Models Using Feature-based Search Spaces",
journal = "New Generation Computing",
publisher = "Ohmsha, Ltd. and Springer",
year = "2011",
pages = "365--390",
volume = "29",
issue = "4",
month = oct,
keywords = "genetic algorithms, genetic programming, Stochastic,
Statistical Features, Gene Regulatory Networks, Time
Series",
ISSN = "0288-3635",
doi = "doi:10.1007/s00354-009-0115-7",
size = "26 pages",
abstract = "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.",
affiliation = "Brock University, 500 Glenridge Ave., St. Catharines,
ON, Canada L2S 3A1",
}
@InProceedings{imae:2003:amcdfnsbogpjcua,
author = "Joe Imae and Nobuyuki Ohtsuki and Yoshiteru Kikuchi
and Tomoaki Kobayashi",
title = "A minimax control design for nonlinear systems based
on genetic programming: Jung's collective unconscious
approach",
booktitle = "Proceedings of the 2003 Congress on Evolutionary
Computation CEC2003",
editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass
and Kay Chen Tan and Bob McKay and Daryl Essam and Tom
Gedeon",
pages = "1702--1707",
year = "2003",
publisher = "IEEE Press",
address = "Canberra",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "8-12 " # dec,
organisation = "IEEE Neural Network Council (NNC), Engineers Australia
(IEAust), Evolutionary Programming Society (EPS),
Institution of Electrical Engineers (IEE)",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-7804-0",
abstract = "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.",
notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust,
the EPS, and the IEE.",
}
@InProceedings{imae:2003:ancsdbohevadgpa,
author = "Joe Imae and Yoshiteru Kikuchi and Nobuyuki Ohtsuki
and Tomoaki Kobayashi",
title = "A nonlinear control system design based on
{HJB/HJI/FBI} equations via a differential genetic
programming approach",
booktitle = "Proceedings of the 2003 Congress on Evolutionary
Computation CEC2003",
editor = "Ruhul Sarker and Robert Reynolds and Hussein Abbass
and Kay Chen Tan and Bob McKay and Daryl Essam and Tom
Gedeon",
pages = "763--769",
year = "2003",
publisher = "IEEE Press",
address = "Canberra",
publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
month = "8-12 " # dec,
organisation = "IEEE Neural Network Council (NNC), Engineers Australia
(IEAust), Evolutionary Programming Society (EPS),
Institution of Electrical Engineers (IEE)",
keywords = "genetic algorithms, genetic programming",
ISBN = "0-7803-7804-0",
abstract = "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.",
notes = "CEC 2003 - A joint meeting of the IEEE, the IEAust,
the EPS, and the IEE.",
}
@InProceedings{Imae:2008:SICE,
author = "Joe Imae and Yasuhiko Morita and Guisheng Zhai and