% GECCO-2005 Workshops bibliography % Created by Franz Rothlauf 17 Aug 2005 %WBL 6 Sep 2005 http://www.cs.essex.ac.uk/staff/W.Langdon/ %extracted by gecco2005_proc.awk $Revision: 1.00 $ http://www.cs.essex.ac.uk/staff/W.Langdon/ %created from gecco2005wsk.abstracts %created from gecco2005wsk.bib2 @InProceedings(lones:gecco05ws, author = {Michael A. Lones and Andy M. Tyrrell}, title = {The evolutionary computation approach to motif discovery in biological sequences}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {1--11}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0001.pdf}, abstract = {Finding motifs - patterns of conserved residues - within nucleotide and protein sequences is a key part of understanding function and regulation within biological systems. We present a review of current approaches to motif discovery, both evolutionary computation based and otherwise, and a speculative look at the advantages of the evolutionary computation approach and where it might lead us in the future. Particular attention is given to the problem of characterising regulatory DNA motifs and the value of expressive representations for providing accurate classification. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(abbott:gecco05ws, author = {Russ Abbott}, title = {Challenges for biologically-inspired computing}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {12--22}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0012.pdf}, abstract = {We discuss a number of fundamental areas in which biologically inspired computing has so far failed to mirror biological reality. These failures make it difficult for those who study biology (and many other scientific fields) to benefit from biologically inspired computing. (1) The apparent impossibility of finding a base level at which to model biological (or most other real-world) phenomena. Although most computer systems are stratified into disjoint and encapsulated levels of abstraction (sometimes known as layered hierarchies), the universe is not.(2) Our inability to characterise on an architectural level the processes that define biological entities in both enough detail and with sufficient abstraction to model them.(3) Our inability to model fitness except in terms of artificially defined functions or artificially defined fitness units. Fitness to an environment is not (a) a measure of an entity's conformance to an ideal, (b) an entity's accumulation of what might be called "fitness points," or even (c) a measure of reproductive success. Fitness to an environment is an entity's ability to acquire and use the resources available in that environment to sustain and perpetuate its life processes.(4) Our inability to build models that allow emergent phenomena to add themselves and their relationships to other phenomena back into our models as first class citizens.These failures arise out of our inability as yet to fully understand what we mean by emergence. As an initial step towards surmounting these hurdles, we attempt to clarify what the problems are and to offer a framework in terms of which we believe they may be understood. We also offer a definition of emergence as the appearance of a process that produces a persistent area of relatively reduced entropy. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Yang:gecco05ws, author = {Shengxiang Yang and J\"urgen Branke}, title = {Evolutionary Algorithms for Dynamic Optimization Problems: Workshop Preface}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {23--24}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0023.pdf}, abstract = {Introduction for EvoDOP }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(AbdunnaserYounes:gecco05ws, author = {Abdunnaser Younes and Paul Calamai and Otman Basir}, title = {Generalized Benchmark Generation for Dynamic Combinatorial Problems}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {25--31}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0025.pdf}, abstract = {Several general purpose benchmark generators are now available in the literature. They are convenient tools in dynamic continuous optimisation as they can produce test instances with controllable features. Yet, a parallel work in dynamic discrete optimization still lacks. In constructing benchmarks for dynamic combinatorial problems, two issues should be addressed: first, test cases that can effectively test an algorithm ability to adapt can be difficult to create; second, it might be necessary to optimise several instances of an NP-hard problem. Hence, we propose a method for generating benchmarks with known solutions without the need to re-optimize. Consequently, the method does not suffer the usual limitations on the problem size or the sequence length. The paper also proposes a general framework for the generation of test problems. It aims to unify existing approaches and to form a basis for designing newer benchmarks. Such a framework can be more appreciated knowing that combinatorial problems tend to assume very distinct structures, and hence, relevant benchmarks are basically too specific to be of interest to the general reader. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(rand:gecco05ws, author = {William Rand and Rick Riolo}, title = {Measurements for Understanding the Behavior of the Genetic Algorithm in Dynamic Environments: A Case Study using the Shaky Ladder Hyperplane-Defined Functions}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {32--38}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0032.pdf}, abstract = {We describe a set of measures to examine the behaviour of the Genetic Algorithm (GA) in dynamic environments. We describe how to use both average and best measures to look at performance, satisfiability,robustness, and diversity. We use these measures to examine GA behaviour with a recently devised dynamic test suite, the Shaky LadderHyperplane-Defined Functions (sl-hdf's). This test suite can generate random problems with similar levels of difficulty and provides a platform allowing systematic controlled observations of the GA in dynamic environments. e examine the results of these measures in two different versions of the sl-hdf's, one static and one regularly-changing. e provide explanations for the observations in these two different environments, and give suggestions as to future work. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(bosman:gecco05ws, author = {Peter A. N. Bosman}, title = {Learning, Anticipation and Time-Deception in Evolutionary Online Dynamic Optimization}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {39--47}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0039.pdf}, abstract = {we focus on an important source of problem-difficulty in (online) dynamic optimisation problems that has so far received significantly less attention than the traditional shifting of optima.Intuitively put, decisions taken now (i.e. setting the problem variables to certain values) may influence the score that can be obtained in the future. We indicate how such time-linkage can deceive an optimiser and cause it to find a suboptimal solution trajectory. We then propose a means to address time-linkage:predict the future by learning from the past. We formalise this means in an algorithmic framework. Also, we indicate why evolutionary algorithms are specifically of interest in this framework. We have performed experiments with two new benchmark problems that contain time-linkage. The results show, as a proof of principle,that in the presence of time-linkage EAs based upon this framework can obtain better results than classic EAs that do not predict the future. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(a_boumaza:gecco05ws, author = {Amine Boumaza}, title = {Learning Environment Dynamics From Self-Adaptation}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {48--54}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0048.pdf}, abstract = {We present an experimental study that shows a relationship between the dynamics of the environment and the adaptation of strategy parameters.Experiments conducted on two adaptive evolutionary strategies SA-ES and CMA-ES on the dynamic sphere function, show that the nature of the movements of the function's optimum are reflected in the evolution of the mutation steps. Three types of movements are presented: constant,linear and quadratic velocity, in all, the evolution of mutation steps during adaptation reflect distinctly the nature of the movements.Furthermore with CMA-ES, the direction of movement of the optimum can be extracted. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(DudyLim:gecco05ws, author = {Dudy Lim and Yew-Soon Ong and Bu-Sung Lee}, title = {Inverse Multi-Objective Robust Evolutionary Design Optimization in the Presence of Uncertainty}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {55--62}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0055.pdf}, abstract = {In many real-world design problems, uncertainties are often present and practically impossible to avoid. Many existing works on Evolutionary Algorithm (EA) for handling uncertainty have emphasised on introducing some prior structure of the uncertainty or noise to the variable domain and conducting sensitivity analysis based on the assumed information. we present an evolutionary design optimisation that handles the presence of uncertainty with respect to the desired robust performance in mind, which we call an inverse robust design. The scheme, unlike others developed to represent uncertainty does not assume any structure of the uncertainty involved; hence it is particularly useful when there is very little information about the uncertainties available. In our formulation, we model the clustering of uncertain events in families of nested sets using a multi-level optimization searches within the multi-objective evolutionary search. Empirical studies were conducted on synthetic functions to demonstrate that our algorithm converges to a set of designs with non-dominated nominal performances and robustness to the presence of uncertainties. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Gao:gecco05ws, author = {Yang Gao and Joshua Zhexue Huang and Hongqiang Rong and Daqian Gu}, title = {Learning Classifier System Ensemble for Data Mining}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {63--66}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0063.pdf}, abstract = {We propose LCSE, a learning classifier system ensemble, which is an extension of the classical learning classifier system(LCS). The classical LCS includes two major modules, a genetic algorithm module used to facilitate rule discovery, and a reinforcement learning module used to adjust the strength of the corresponding rules while it receives the rewards from the environment. In LCSE we build a two-level ensemble architecture to enhance the generalisation of LCS. In the first-level, new instances are first bootstrapped and sent to several LCSs for classification. Then, in the second-level, a plurality-vote method is used to combine the classification results of individual LCSs into a final decision. Experiments on some benchmark data sets from the UCI repository have shown that LCSE has better generalisation ability than the single LCS and other supervised learning methods. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Holmes:gecco05ws, author = {John H. Holmes}, title = {Detection of Sentinel Predictor-Class Associations With {XCS}:A Sensitivity Analysis}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {67--71}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0067.pdf}, abstract = {Knowledge discovery in databases has traditionally focused on classification, prediction, or in the case of unsupervised discovery, clusters and class definitions. Equally important, however, is the discovery of individual predictors along a continuum of some metric that indicates their association with a particular class. We report on the use of an XCS learning classifier system for this purpose. Conducted over a range of odds ratios for a fixed variable in synthetic data, it was found that XCS discovers rules that contain metric information about specific predictors and their relationship to a given class. In addition, EpiXCS performs qualitatively similarly to See5, and both methods are comparable to logistic regression. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Gu:gecco05ws, author = {Daqian Gu and Yang Gao}, title = {Incremental Gradient Descent Imputation Method For Missing Data In Learning Classifier Systems}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {72--73}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0072.pdf}, abstract = {Learning with incomplete or missing data has been a major challenge in learning classifier system. One method for covering missing data is imputing missing values based on the statistic of known values. Another is marking them matching arbitrary case. A new approach using incremental gradient descent imputation model is proposed, which use the relationship among variables to estimate the missing value. And, some experiments are conducted in order to compare the performance of new approach and other classical covering methods. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Orriols:gecco05ws, author = {Albert Orriols and Ester Bernad{\'o}-Mansilla}, title = {The Class Imbalance Problem in Learning Classifier Systems:A Preliminary Study}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {74--78}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0074.pdf}, abstract = {The class imbalance problem has been said recently to hinder the performance of learning systems. In fact, many of them are designed with the assumption of well-balanced datasets. However, it is very common to find higher presence of one of the classes in real classification problems. Our aim is to make a preliminary analysis on the effect of the class imbalance problem in learning classifier systems. Particularly we focus our study on UCS, a supervised version of XCS classifier system. We analyse UCS's behaviour on unbalanced datasets and find that UCS is sensitive to high levels of class imbalance. We study strategies for dealing with class imbalances, acting either at the sampling level or at the classifier system's level. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Baronti:gecco05ws, author = {Flavio Baronti and Alessandro Passaro and Antonina Starita}, title = {Post-processing clustering to reduce {XCS} variability}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {79--81}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0079.pdf}, abstract = {XCS is a stochastic algorithm, so it does not guarantee to produce the same results when run with the same input. When interpretability matters, obtaining a single, stable result is important. We propose an algorithm to join the rules produced from many XCS runs, based on a measure of distance between rules. We also suggest a general definition for such a measure, and show the results obtained on a complex data set. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Mellor:gecco05ws, author = {Drew Mellor}, title = {Policy Transfer with a Relational Learning Classifier System}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {82--84}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0082.pdf}, abstract = {Policy transfer occurs when a system transfers a policy learnt for one task to another task with little or no retraining, and allows a system to perform robustly and learn efficiently, especially when the new task is more complex than the original task. we report on work in progress into policy transfer using a relational learning classifier system. The system, Fox-cs, uses a high level relational language (a subset first order logic) in combination with a $P$-learning technique adapted for Xcs and its derivatives. Fox-cs achieved successful policy transfer in two blocks world tasks, stacking and onab, by learning a policy that was independent of the number of blocks, thus avoiding the prohibitive training times that would normally arise due to the exponential explosion in the number of states as the number of blocks increases. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Dam:gecco05ws, author = {Hai Huong Dam and Hussein A. Abbass and Chris Lokan}, title = {Be Real! {XCS} with Continuous-Valued Inputs}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {85--87}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0085.pdf}, abstract = {XCS is widely accepted as one of the most reliable Michigan-style learning classifier system (LCS) for data mining. In order to handle real-valued inputs effectively, the traditional ternary representation has been replaced by the interval-based representation and the modified XCS has shown to work well. Existing interval-based representations still suffer from a few drawbacks which this paper address. we propose an alternative approach called the Min-Percentage representation which produces comparable results to other methods in the literature with the extra advantage of overcoming the drawbacks in these methods. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Llora:gecco05ws, author = {Xavier Llor{\`a} and Kumara Sastry and David E. Goldberg}, title = {Binary Rule Encoding Schemes: A Study Using The Compact Classifier System}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {88--89}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0088.pdf}, abstract = {Several binary rule encoding schemes have been proposed for Pittsburgh-style classifier systems. We focus on the analysis of how rule encoding may bias the scalability of learning maximally general and accurate rules by classifier systems. The theoretical analysis of maximally general and accurate rules using two different binary rule encoding schemes showed some theoretical results with clear implications to the scalability of any genetic-based machine learning system that uses the studied encoding schemes. Such results are clearly relevant since one of the binary representations studied is widely used on Pittsburgh-style classifier systems, and shows an exponential shrink of the useful rules available as the problem size increases. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Booker:gecco05ws, author = {Lashon B. Booker}, title = {Adaptive Value Function Approximations in Classifier Systems}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {90--91}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0090.pdf}, abstract = {Hyperplane coding is a new, closely related variation of tile coding in which classifier rule conditions fill the role of tiles, and there are few restrictions on the way those ``tiles'' are organised. The basic idea is to treat rules as features that collectively specify a linear gradient-descent function approximator. The hypothesis behind this idea is that classifier rules can be more effective as function approximators if they work together to implement a distributed, coarse-coded representation of the value function. One open question remaining about hyperplane coding is how the quality of the approximation is affected by the set of classifiers in the population. A random population of classifiers is sufficient to obtain high quality results. Would a more carefully chosen population do even better? The obvious next step in this research is to use the approximation resources available in a random population as a starting point for a more refined approach to approximation that reallocates resources adaptively to gain greater precision in those regions of the input space where it is needed. We show how to compute such an adaptive function approximation. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Wada1:gecco05ws, author = {Atsushi Wada and Keiki Takadama and Katsunori Shimohara}, title = {Learning Classifier System Equivalent with Reinforcement Learning with Function Approximation}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {92--93}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0092.pdf}, abstract = {We present an experimental comparison of the reinforcement process between Learning Classifier System (LCS) and Reinforcement Learning (RL) with function approximation (FA) method, regarding their generalisation mechanisms. To validate our previous theoretical analysis that derived equivalence of reinforcement process between LCS and RL, we propose a simple test environment named Gridworld, which can be applied to both LCS and RL with three different classes of generalization: (1) tabular representation; (2) state aggregation; and (3) linear approximation. From the simulation experiments comparing LCS with its GA-inactivated and corresponding RL method, all the cases regarding the class of generalization showed identical results with the criteria of performance and temporal difference (TD) error, thereby verifying the equivalence predicted from the theory. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Wada2:gecco05ws, author = {Atsushi Wada and Keiki Takadama and Katsunori Shimohara}, title = {Counter Example for Q-Bucket-Brigade under Prediction Problem}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {94--99}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0094.pdf}, abstract = {Aiming at clarifying the convergence or divergence conditions for Learning Classifier System (LCS), we explore: (1) an extreme condition where the reinforcement process of LCS diverges; and (2) methods to avoid such divergence. Based on our previous work that showed equivalence between LCS's reinforcement process and Reinforcement Learning (RL) with Function approximation (FA) method, we present a counter example for LCS with Q-bucket-brigade based on the 11-state star problem, a counter example originally proposed to show the divergence of Q-learning with linear FA. Furthermore, the empirical results applying the counter example to LCS verified the results predicted from the theory: (1) LCS with Q-bucket-brigade diverged under the prediction problem, where the action selection policy was fixed; and (2) such divergence was avoided by using implicit-bucket-brigade or applying residual gradient algorithm to Q-bucket-brigade. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Hamzeh:gecco05ws, author = {Ali Hamzeh and Adel Rahmani}, title = {Intelligent Exploration Method for {XCS}}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {100--102}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0100.pdf}, abstract = {Exploration/Exploitation equilibrium is one of the most challenging issues in reinforcement learning area as well as learning classifier systems such as XCS. An intelligent method is proposed to control exploration rate in XCS to improve its long-term performance. This method is called Intelligent Exploration Method (IEM) and is applied to some benchmark problems to show advantages of adaptive exploration rate for XCS. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(McMahon:gecco05ws, author = {Alex McMahon and Dan Scott and Will Neil Browne}, title = {An Autonomous Explore/Exploit Strategy}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {103--108}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0103.pdf}, abstract = {In reinforcement learning problems it has been considered that neither exploitation nor exploration can be pursued exclusively without failing at the task. The optimal balance between exploring and exploiting changes as the training progresses due to the increasing amount of learnt knowledge. This shift in balance is not known a priori so an autonomous online adjustment is sought. Human beings manage this balance through logic and explorations based on feedback from the environment. The XCS learning classifier system uses a fixed explore/exploit balance, but does keep multiple statistics about its performance and interaction in an environment. Using these statistics in a non-linear manner, autonomous adjustment of the explore/exploit balance was achieved. This resulted in reduced exploration in simple environments, which could increase with the complexity of the problem domain. It also prevented unsuccessful 'loop' exploit trials and suggests a method of dynamic choice in goal setting. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Inoue:gecco05ws, author = {Hiroyasu Inoue and Keiki Takadama and Katsunori Shimohara}, title = {Exploring {XCS} in Multiagent Environments}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {109--111}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0109.pdf}, abstract = {We investigate the adaptability of XCS in four different multiagent environments. The environments are realized in a simplified soccer game, and they include (1) single-agent environment, (2) multiagent environment with an opponent, (3) multiagent environment with a teammate, (4) multiagent environment with both an opponent and a teammate. Although XCS generally seems inferior to strength-based XCS in such stochastic environments, experimental results in a specific stochastic environment show that XCS is superior to strength-based XCS. Furthermore, XCS with profit sharing is more effective than one using the bucket brigade in multiagent environments. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Sood:gecco05ws, author = {Neera P Sood and Ashley G. Williams and Kenneth A. {De Jong}}, title = {Evaluating The {XCS} Learning Classifier System In Competitive Simultaneous Learning Environments}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {112--118}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0112.pdf}, abstract = {We would like to evaluate the XCS [1] Learning Classifier System (LCS [2]) to see if it can be applied to a specific aviation industry problem. We are interested in seeing whether it can offer an accessible representation model and evolve feasible strategies to predict future demand patterns endogenously, and in parallel with the supply side simulation. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Smith:gecco05ws, author = {Noah W. Smith and Clare Bates Congdon}, title = {{RCS}: A Learning Classifier Systems for Evolutionary Robotics}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {119--120}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0119.pdf}, abstract = {We introduce RCS, a learning classifier system designed for evolutionary robotics research. In addition, we present the results of RCS applied to a pursuit task. In this test, performance was good and has been improved in ongoing work. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Esterline:gecco05ws, author = {Albert Esterline and Chafic BouSaba and Abdollah Homaifar and Dan Rodgers}, title = {A Framework for Learning Coordinated Behavior}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {121--124}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0121.pdf}, abstract = {We sketch a framework for learning structured coordinated behavior, specifically the tactical behavior of Experimental Unmanned Vehicles (XUVs). We think of an XUV unit as a multiagent system (MAS) on which we impose a command structure to yield a holarchy, a hierarchy of holons, where a holon is both a whole and a part. The formalism used is a conservative extension of Statecharts, called a Parts/whole Statechart, which introduces a coordinating whole as a concurrent component on a par with the coordinated parts; wholes are related to common knowledge. We use X-classifier systems (XCSs). Exploiting Statechart semantics, we translate Statechart transitions into classifiers and define data structures that interact with an XCS. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Bosman:gecco05ws, author = {Peter A. N. Bosman and Tanja Alderliesten}, title = {Evolutionary Algorithms for Medical Simulations - A Case Study in Minimally-Invasive Vascular Interventions}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {125--132}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0125.pdf}, abstract = {To obtain the expertise to correctly perform minimally-invasive vascular interventions thorough training is required. Training using simulation systems are increasingly becoming an accepted methodology. Recently, a minimally-invasive vascular intervention simulation (MIVIS) system has been developed. At the heart of this system lies an optimisation problem to be solved repeatedly.we investigate the advantages and disadvantages of using an evolutionary algorithm (EA) to solve the optimisation problem instead of a problem-specific first-order analytical approximation algorithm. The results show that the use of the EA as optimization algorithm is favourable. A substantial reduction in time can be obtained while the RMS error associated with the simulation result differs only slightly. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Bourgeois:gecco05ws, author = {Claire Bourgeois-Republique and Bruno Frachet and Pierre Collet}, title = {Using an Interactive Evolutionary Algorithm to Help Fitting a Cochlear Implant}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {133--139}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0133.pdf}, abstract = {Cochlear implants are electronic devices that stimulate directly the auditory nerve to allow totally deaf patients to hear again.We presents an interactive evolutionary algorithm (IEA) designed to help finding the best parameters of a cochlear implant for a specific patient.If early cochlear implants only featured one electrode, modern devices now offer up to 22 electrodes, with the hope to be able to transmit more details and help the patient hear better. We show that having more electrodes is not necessarily better.Tests on a patient show surprisingly that some combinations of electrodes yield better results than others, with the problem that there is no real way to determine which electrode is beneficial to speech understanding and which is not.The best result obtained by the patient on a speech understanding evaluation protocol was 48.5/100 after 10 years of fitting sessions by an expert practitioner. For many reasons explained in this paper,the evaluation of the best parameter setting found by the IEA in one day was 91.5/100. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Manana:gecco05ws, author = {Gabriel Ma{\~n}ana and Fabio Gonz{\'a}lez and Eduardo Romero}, title = {Distributed Genetic Algorithm for Subtraction Radiography}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {140--146}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0140.pdf}, abstract = {Digital subtraction is a promising technique used in radiographic studies of periapical lesions and other dental disorders for which the treatment must be evaluated over time. We present a fast and reliable automated image registration method for subtracting two digitised radiographs where an unpredicted mismatch is present. An optimal affine transformation is found using an adaptive Genetic Algorithm (GA) as the optimisation strategy and a correlation ratio as the similarity measure. The parallel GA implemented takes advantage of the CPU idle cycles of a computational grid, resulting in an application with a computational time of less than three minutes when processing pairs of standard intra-oral radiographs. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Passaro:gecco05ws, author = {Alessandro Passaro and Flavio Baronti and Valentina Maggini}, title = {Exploring Relationships between Genotype and Oral Cancer Development through {XCS}}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {147--151}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0147.pdf}, abstract = {In medical research, being able to justify decisions is generally as important as taking the right ones. Interpretability is then one of the chief characteristics a learning algorithm must have,in order to be successfully applied to a medical data set. Other important features are seamless treatment of different data types,and ability to cope well with missing values. XCS and decision trees both appear to have this desirable characteristics; we compared them on a data set regarding Head and neck squamous cell carcinoma (HNSCC). This kind of oral cancer already been found to be associated with smoking and alcohol drinking habits. However the individual risk could be modified by genetic polymorphisms of enzymes involved in the metabolism of tobacco carcinogens and in the DNA repair mechanisms. To study this relationship, the dataset comprised demographic and life-style (age, gender, smoke and alcohol), and genetic data (the individual genotype of 11polymorphic genes), with the information on 124 HNSCC patients and231 healthy controls. Results with both algorithms are presented and analysed. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Petrovski:gecco05ws, author = {Andrei Petrovski and John McCall}, title = {Smart Problem Solving Environment for Medical Decision Support}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {152--158}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0152.pdf}, abstract = {We present a medical problem solving environment (PSE) designed for modelling, simulation, and optimisation of clinical cancer chemotherapy. In order to find optimal chemotherapeutic treatments, two population-based evolutionary algorithms - Genetic Algorithms and Particle Swarm Optimisation - have been applied, which can use web services and grid computing to evaluate potential solutions in a distributed and customisable manner. The versatility and robustness of these algorithms make the suggested problem solving environment scalable and adaptable to other problem domains. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Stephens:gecco05ws, author = {Christopher R. Stephens and Henri Waelbroeck and Susan L. Talley}, title = {Predicting Healthcare Costs using {GAs}}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {159--163}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0159.pdf}, abstract = {Predicting prospective health care costs is of increasing importance. Genetic search is used to discover attribute sets and associated posterior probability classifiers that predict the top 0.5% most costly individuals in year N+1 based on previous medical conditions and costs in year N. The predictive performance of single-variable classifiers (cost-drivers), found using statistical measures familiar from data mining, as well as Naive Bayesian analysis, are compared and contrasted with that of classifiers found using genetic search. Comparison is also made with two well known benchmarks from the healthcare literature. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Siccama:gecco05ws, author = {Ivar Siccama and Maarten Keijzer}, title = {Genetic Programming as a Method to Develop Powerful Predictive Models for Clinical Diagnosis}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {164--166}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0164.pdf}, abstract = {In the field of medicine it is of vital importance to accurately predict the presence of a disease (diagnostic prediction)or the future occurrence of a certain event (prognostic prediction). Genetic programming provides a method to develop such prediction models in an optimal way. We discuss as an example the diagnostic prediction of pulmonary embolism (PE),and compare the method of genetic programming with the logistic regression technique, which is well-known in the medical field. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(day:gecco05ws, author = {Richard O. Day and Abel S. Nunez and Gary B. Lamont}, title = {{MOEA} Design of Robust Digital Symbol Sets}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {167--169}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0167.pdf}, abstract = {Optimal constellation design is important in military digital communications for Quadrature Amplitude Modulation (QAM). Optimisation realizes a reduced probability of bit error (Pb) while keeping the same bandwidth and power for transmitting the signal. Constellation shapes currently used in QAM include rectangle, triangle, hexagonal, and concentric circles. Two dimensional (4, 8,..., and 256)-ary constellations at specific normalised signal to noise ratios Eb/No having lower Pb are sought. Various models are designed to provide a Multi-objective Evolutionary Algorithm (MOEA) with a near exact model to used as a fitness function. MOEA found solutions are tested for merit using a Monte Carlo simulator. Comparisons of Eb/No vs. Pb between the rectangular constellation and new designs are illustrated. New designs are shown to be different and comparable to the standard constellations used today }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(laroche:gecco05ws, author = {Patrick LaRoche and A. Nur Zincir-Heywood}, title = {802.11 Network Intrusion Detection using Genetic Programming}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {170--171}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0170.pdf}, abstract = {Genetic Programming (GP) based Intrusion Detection Systems (IDS) use connection state network data during their training phase. These connection states are recorded as a set of features that the GP uses to train and test solutions which allow for the efficient and accurate detection of given attack patterns. However, when applied to a 802.11 network that is faced with attacks specific to the 802.11 protocol, the GP's detection rate reduces dramatically. We discuss what causes this effect, and what can be done to improve GP's performance on 802.11 networks. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(oh:gecco05ws, author = {Jae C. Oh and Misty Blowers}, title = {Text-independent Open-set Speaker Identification for Military Missions Using Genetic Rule-based System}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {172--174}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0172.pdf}, abstract = {We present a genetic classifier system approach to the text-independent open-set speaker identification problem. Classifier systems are widely used in symbolic problem for dynamically changing open-ended learning. Signal processing problems require processing of real-valued parameters that classifier systems are not designed for. On the other hand, the approaches based on common cepstral encoding with clustering algorithms handle the closed-set speaker identification quite well. This research solves the open-set problem by hybridising these two approaches. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(ridder:gecco05ws, author = {Jeffrey P. Ridder}, title = {Evolutionary Computation Methods for Synchronization of Effects Based Operations}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {175--177}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0175.pdf}, abstract = {Effects based operations (EBO) is a concept based on the premise that a desired strategic outcome can be achieved through synergistic, multiplicative, and cumulative application of the full range of military and non military capabilities. Such synergism requires synchronising a large set of operations in order to achieve a set of effects. The synchronizer develops a set of alternative plans by solving a multi-objective optimisation problem subject to several constraints. The EBO synchronisation problem is also dynamic since planning is a continuous activity in a dynamically changing environment. We describe the operational concept for EBO synchronization, the requirements for algorithms to perform synchronization, and some algorithmic steps toward constructing a synchroniser. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(shapiro:gecco05ws, author = {Joseph M. Shapiro and Gary B. Lamont and Gilbert L. Peterson}, title = {An Evolutionary Algorithm to Generate Ellipsoid Network Intrusion Detectors}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {178--180}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0178.pdf}, abstract = {We introduce ellipsoids as geometric network intrusion detectors. A Negative selection algorithm is used to generate ellipsoid detectors against self network data. Experimentation performed with the 1999 MIT DARPA IDS data sets validates the usefulness of this approach. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(thie:gecco05ws, author = {Claire J. Thie and Darren M. Chitty and Colin M. Reed}, title = {Using Evolutionary Algorithms and Dynamic Programming to solve Uncertain Multi-Criteria Optimisation Problems with application to Lifetime Management for military Platforms}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {181--183}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0181.pdf}, abstract = {Microelectronics are typically critical components in a military platform, some of which may become obsolete, before the equipment life cycle end. Obsolete components may be required for a number of reasons. Components can become obsolete even before production of a platform commences. The selection of solutions for resolving obsolete components throughout a platform can be considered as a multi-criteria optimisation problem where the aim is to select the most cost effective solutions for resolving a portfolio of obsolescence arisings. We consider the case where the criteria with which the options are evaluated are not point values, but probability distributions generated by a Bayesian Belief Network. We propose the use of an evaluation technique called measures of effectiveness, that can capture and use the probabilistic information associated with potential solutions. This is used with two candidate optimisation techniques, dynamic programming and evolutionary algorithms, to identify cost effective solutions for resolving obsolescent components throughout a platform. Both optimization techniques were able to identify a number of solutions at different cost and MOE levels for two different scenarios; the solutions that form the DP Pareto front dominate (outperform in terms of cost and benefit) very slightly in places those that form the EA Pareto front. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Hussain:gecco05ws, author = {Talib S. Hussain and Daniel Cerys and David Montana and Gordon Vidaver and Jeffrey E. Berliner}, title = {Tactical {UGV} Navigation and Logistics Planning}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {184--186}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0184.pdf}, abstract = {The US Army's push towards developing highly flexible military teams that combine manned and unmanned units requires significant advances in the intelligence of the unmanned units and in the tools used to provide logistical support. BBN Technologies has recently completed a simulation-based project for the Army Research Lab in which we applied an evolutionary computation approach for determining the tactical responses of an unmanned ground vehicle, moderated by explicit rules of engagement. BBN is also currently developing a logistic analysis prototype that uses agent-based technology and evolutionary computation to enable rapid logistics planning and replanning for supporting an Army organisation encountering a diversity of planned and unplanned situations. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(McDonnell:gecco05ws, author = {John McDonnell and Aaron Rice}, title = {Rapid Asset Allocation for Dynamic {TACAIR} Decision Support}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {187--189}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0187.pdf}, abstract = {We address a decision support system that can be used for effectively re-tasking TACAIR assets under a variety of constraints. Analysis of the common operational picture provides augmented situational awareness. Automatic risk analysis keeps the user aware of current and planned risk levels to blue force assets. Options for reacting to changes in the battlefield environment are generated using an evolutionary search algorithm. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(moore:gecco05ws, author = {Frank Moore and Pat Marshall}, title = {Evolving Next Generation Signal Compression and Reconstruction Transforms via Genetic Algorithms}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {190--192}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0190.pdf}, abstract = {Ongoing research has established a new methodology for using genetic algorithms to evolve forward and inverse transforms that significantly reduce quantisation error in reconstructed signals and images. The approach promises to revolutionise the signal and image processing field, producing both higher quality images and higher compression ratios than is currently possible with wavelet-based techniques. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(yang:gecco05ws, author = {Ang Yang and Hussein A. Abbass and Ruhul Sarker}, title = {Evolving Agents for Network Centric Warfare}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {193--195}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0193.pdf}, abstract = {Network centric warfare (NCW) is a new theory for war information age. NCW advocates that networking battlefield entities will produce shared information, shared knowledge and shared understanding which produce information superiority. In turn information superiority dramatically increases the power of combat by speeding up the speed of command. Due to impossibility of evoking real wars to test the theory of NCW, artificial wars in simulated environments become more important. Since it is new, there do exist proponents and opponents. This poster presents the features of the second version of a multi-agent based distillation combat system called WISDOM(a Warfare Intelligent System for Dynamic Optimisation of Missions). By evolving the agents, we explore the mechanism of decision making in NCW. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Kleeman:gecco05ws, author = {Mark P. Kleeman and Gary B. Lamont}, title = {Solving the Aircraft Engine Maintenance Scheduling Problem using a Multi-objective Evolutionary Algorithm}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {196--198}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0196.pdf}, abstract = {We investigate the use of a multi-objective genetic algorithm (MOEA) to solve the scheduling problem for aircraft engine maintenance. The problem is a combination of a modified job shop problem and a flow shop problem. The goal is to minimise the time needed to return engines to mission capable status and to minimise the associated cost by limiting the number of times an engine has to be taken from the active inventory for maintenance. Our preliminary results show that the chosen MOEA called GENMOP effectively converges toward better scheduling solutions and our innovative chromosome design effectively handles the maintenance prioritisation of engines. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(muehlenbein:gecco05ws, author = {Heinz M\"uhlenbein and Robin H\"ons}, title = {Approximate Factorizations of Distributions and the Mimimum Relative Entropy Principle}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {199--211}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0199.pdf}, abstract = {Estimation of Distribution Algorithms (EDA) have been proposed as an extension of genetic algorithms. The major design issues of EDA's are discussed within a general interdisciplinary framework, the maximum entropy approximation. Our EDA algorithm FDA assumes that the function to be optimised is additively decomposed (ADF). The interaction graph G_{ADF} is used to create exact or approximate factorisations of the Boltzmann distribution. The relation between FDA factorisations and the MaxEnt solution is shown. We introduce a second algorithm, derived from the Bethe-Kikuchi approach developed in statistical physics. It tries to minimize the Kullback-Leibler divergence KLD(q|p_B) to the Boltzmann distribution p_B by solving a difficult constrained optimisation problem. We present in detail the concave-convex minimisation algorithm \CCCP to solve the optimisation problem. The two algorithms are compared using popular benchmark problems (2-d grid problems, 2-d Ising spin glasses, Kaufman's NK function.) We use instances up to 900 variables. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(samples:gecco05ws, author = {Michael E. Samples and Jason M. Daida and Matt Byom and Matt Pizzimenti}, title = {Parameter Sweeps For Exploring {GP} Parameters}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {212--219}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0212.pdf}, abstract = {We describe our procedure and a software application for conducting large parameter sweep experiments in genetic and evolutionary computation research. Both procedure and software allows a researcher to examine multivariate nonlinearities that are common in genetic and evolutionary computation. Experiments of this nature are well suited to distributed computing environments (such as Grids and clusters) and we present an automated system for conducting parameter sweep experiments on heterogeneous networks. Emphasis is placed on experimental sampling, distributed robustness, and data analysis. The parameter sweep experimental procedure is easily applicable to any experiment involving computer simulations but is particularly well suited for evolutionary computation experiments. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(piszcz:gecco05ws, author = {Alan Piszcz and Terence Soule}, title = {Genetic Programming: Parametric Analysis of Structure Altering Mutation Techniques}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {220--227}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0220.pdf}, abstract = {Abstract. We suggest that the relationship between parameter settings, ie parameters controlling mutation, and performance is non-linear in genetic programs. Genetic programming environments have few means for a priori determination of appropriate parameters values.The hypothesised nonlinear behaviour of genetic programming creates difficulty in selecting parameter values for many problems. we study three structure altering mutation techniques using parametric analysis on a problem with scalable complexity. We nd through parameter analysis that two of the three mutation types tested exhibit nonlinear behaviour. Higher mutation rates cause a larger degree of nonlinear behaviour as measured by fitness and computational effort. Characterisation of the mutation techniques using parametric analysis confirms the nonlinear behavior. In addition, we propose an extension to the existing parameter setting taxonomy to include commonly used structure altering mutation attributes. Finally we show that the proportion of mutations applied to internal nodes, instead of leaf nodes, has a significant effect on performance. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(lobo:gecco05ws, author = {Fernando G. Lobo and Cl\'{a}udio F. Lima}, title = {A Review of Adaptive Population Sizing Schemes in Genetic Algorithm}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {228--234}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0228.pdf}, abstract = {We review the topic of population sizing in genetic algorithms.It starts by revisiting theoretical models which rely on a facet wise decomposition of genetic algorithms, and then moves on to various self-adjusting population sizing schemes that have been proposed in the literature. The paper ends with recommendations for those who design and compare adaptive population sizing schemes for genetic algorithms. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(clune:gecco05ws, author = {Jeff Clune and Sherri Goings and Bill Punch and Eric Goodman}, title = {Investigations in Meta-{GAs}: Panaceas or Pipe Dreams?}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {235--241}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0235.pdf}, abstract = {A meta-GA (GA within a GA) is used to investigate evolving the parameter settings of genetic operators for genetic and evolutionary algorithms (GEA) in the hope of creating a self-adaptive GEA. We report three findings. First, the meta-GA can adapt its genetic operators to different problems and thereby perform well on average across diverse problems. Second, the meta-GA can change its parameters during the course of a run - seemingly a good idea - but this behaviour may actually decrease performance. Finally, the genetic operator configurations the meta-GA evolves are far from optimal. We conclude that, while meta-GAs show promise for automating some parameter configurations, they are not likely to replace manually configured genetic and evolutionary algorithms without innovative alteration. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(bidlosekanina:gecco05ws, author = {Michal Bidlo and Lukas Sekanina}, title = {Providing Information from the Environment for Growing Electronic Circuits Through Polymorphic Gates}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {242--248}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0242.pdf}, abstract = {We deal with the evolutionary design of programs (constructors) that are able to create (n+2)-input circuits from n-input circuits. The growing circuits are composed of polymorphic gates considered as building blocks. Therefore, the growing circuit can specialise its functionality according to environment which is sensed through polymorphic gates. The work was performed using a simple circuit simulator. We evolved constructors that are able to create arbitrarily large polymorphic even/odd parity circuits and polymorphic sorting networks. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(gallini:gecco05ws, author = {Alberto Gallini and C. Ferretti and G. Mauri}, title = {Bio Molecular Engine: A bio-inspired environment for models of growing and evolvable computation}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {249--256}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0249.pdf}, abstract = {Evolutionary computation has been often used by computer scientists to evolve the morphologies and control systems of artificial life. Artificial 'brains', behaviour strategies, methods of communication, distributed problem solving and many other topics are commonly explored by using genetic algorithms and other evolutionary search techniques. We think that this approach may provide the general guidelines to efficiently manage and "design" computation on large and homogeneous lattices of simple, asynchronously interacting processing elements. Because of their structural simplicity, this kind of substrates will be suitable architectural models for computational machines based on molecular scale devices. We present an environment named Bio-molecular Engine (BME), in which different substrates can be simulated and used as "artificial worlds" where computational entities can rise, grow and evolve. In particular we discuss how to use a grid to evolutionary find a good solution to a well defined design issue: how much parallelism is good for a given problem computed in our environment. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(reisinger:gecco05ws, author = {Joseph Reisinger and Kenneth Stanley and Risto Miikkulainen}, title = {Towards an Empirical Measure of Evolvability}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {257--264}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0257.pdf}, abstract = {Genetic representations that do not employ a one-to-one mapping of genotype to phenotype are known as indirect encodings, and can be much more efficient than direct encodings for complex problems. Increasing a representation's capacity to facilitate effective search, i.e. its evolvability, has long been a goal of Evolutionary Computation. However, currently no benchmarks exist to measure evolvability. One reason is that it is difficult to decouple a representation's capacity to evolve under any fitness function, i.e. the latent evolvability, and its performance on a specific benchmark. Towards this goal, a method is proposed that measures the representation's ability to extract invariant properties from a changing fitness function. The test is applied to three distinct representations and it is able to distinguish all three. Ultimately, this test can serve as the foundation for performing controlled experiments determining what factors contribute to evolvability. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(rieffel:gecco05ws, author = {John Rieffel and Jordan Pollack}, title = {Evolutionary Fabrication: The Emergence of Novel Assembly Methods in Artificial Ontogenies}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {265--272}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0265.pdf}, abstract = {Evolutionary Design Systems (EDSs) have demonstrated the ability to generate a wide array of novel objects, including robots, tables, and antennas. Often, the novelty of these evolved designs is due to their ability to discover and exploit important principles of the design space, such as the truss and the ratchet. One current obstacle to the real world application of such EDSs is that they often create purely descriptive representations, and are therefore capable of generating designs whose specific assembly is difficult, if not impossible, to infer. One solution that we offer is to evolve how to build, rather than what to build. When evolution occurs in assembly space rather than design space, only buildable objects are produced. Furthermore, as we demonstrate, doing so allows for the emergence not just of novel designs, but of novel means of assembly. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(viswanathan:gecco05ws, author = {Shivakumar Viswanathan and Jordan Pollack}, title = {How Artificial Ontogenies can retard evolution}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {273--280}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0273.pdf}, abstract = {Recently there has been much interest in the role of indirect genetic encodings as a means to achieve increased evolvability. From this perspective, artificial ontogenies have largely been seen as a vehicle to relate the indirect encodings to complex phenotypes. However, the introduction of a development phase does not come without other consequences. We show that the conjunction of the latent ontogenic structure and the common practice of only evaluating the final phenotype obtained from development can have a net retarding effect on evolution. Using a formal model of development, we show that this effect arises primarily due to the relation between the ontogenic structure to the fitness function, which in turn impacts the properties being evaluated and selected for during evolution. This effect is empirically demonstrated with a toy search problem using LOGO-turtle based embryogenic processes. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(wiles:gecco05ws, author = {Janet Wiles and Nic Geard and James Watson and Kai Willadsen and John Mattick and Daniel Bradley and Jennifer Hallinan}, title = {There's more to a model than code: understanding and formalizing in silico modeling experience}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {281--288}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0281.pdf}, abstract = {Mapping biology into computation has both a domain specific aspect - biological theory - and a methodological aspect - model development. Computational modellers have implicit knowledge that guides modelling in many ways but this knowledge is rarely communicated. We review the challenge of biological complexity and current practices in modeling genetic regulatory networks with the aim of understanding characteristics of the in silico modeling process and proposing directions for future improvements. Specifically, we contend that the modeling of complex biological systems can be made more efficient and more effective by the use of structured methodologies incorporating experience about modeling algorithms and implementation. We suggest that an appropriate formalism is Complex Systems Patterns, adopted from Design Patterns in software engineering. First steps towards building community resources for such patterns are described. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(bidlo:gecco05ws, author = {Michal Bidlo}, title = {A Benchmark for the Sorting Network Problem}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {289--291}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0289.pdf}, abstract = {no abstract available }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Ivan_Garibay:gecco05ws, author = {Ivan Garibay and Annie S. Wu and Ozlem Garibay}, title = {On location independent representations and self-organization}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {292--292}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0292.pdf}, abstract = {We study the self-organisation of genomic symbols on a genetic algorithm with a location independent representation·the Proportional Genetic Algorithm (PGA) [2]. Self-organisation of genomic symbols is possible because location independent representations ensure the absence of selective pressure for a particular order. We suggest that self-similarity emerges because self-similar genomes are more robust with respect to crossover and mutation and because it favours positive correlations between the form and quality of candidate solutions. The PGA is a Genetic Algorithm (GA) with a representation based on protein concentrations rather than on the usual gene ordering. A PGA translates strings of genes into multisets of proteins prior to fitness evaluation. As a result, there is no fitness pressure for any particular gene ordering and the order of the genes is free to evolve along with the candidate solutions that they encode. Previous studies have shown that genomic symbols under these circumstances are evenly distributed throughout the genome and that they appear to form building blocks of a peculiar type: coarse grained versions of the entire genome. We ask the fundamental questions: what is the emergent genomic ordering when there is no selective pressure for any particular ordering? and why? We use two very different methods to analyse the emergent genomic structure: standard equal symbol correlation analysis, and an experimental method of our own making to analyse the self-similarity of genomic segments with respect to fitness. Our results can be summarised as follows: 1. The equal-symbol correlation on completely location independent genomes, as implemented by the PGA, resembles white noise behaviour and the emergent genomic structure is self-similar with respect to fitness. 2. Emergent genomic self-similarity seems to produce the following effect: it favors positive correlations between form and quality of candidate solutions, a key property needed for stochastic search algorithms such as evolutionary algorithms; and it reduces schemata disruption caused by crossover. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Ingo_Mierswa:gecco05ws, author = {Ingo Mierswa and Katharina Morik}, title = {Method Trees: Building Blocks for Self-Organizable Representations of Value Series}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {293--300}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0293.pdf}, abstract = {We introduce a framework for automatic feature extraction from very large series. The extracted features build a new representation which is better suitable for a given learning task. The development of appropriate feature extraction methods is a tedious effort, particularly because every new classification task requires tailoring the feature set anew. Therefore, the simple building blocks defined in our framework can be combined to complex feature extraction methods. We employ a genetic programming approach guided by the performance of the learning classifier using the new representation. Our approach to evolve representations from series data requires a balance between the completeness of the methods on one side and the tractability of searching for appropriate methods on the other side. Some theoretical considerations illustrate the trade-off. After the feature extraction, a second process learns a classifier from the transformed data. The practical use of the methods is shown by two types of experiments in the domain of music data classification: classification of genres and classification according to user preferences. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Tim_Otter:gecco05ws, author = {Tim Otter}, title = {Genotype, Phenotype and Ontogeny}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {301--301}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0301.pdf}, abstract = {Building effective computational models of living systems requires both a sound conceptual basis and an accurate scheme of implementation. Basic biological principles must be clearly articulated to capture their essential features or logic but not reach beyond legitimate bounds. analyses the relationships among genotype, phenotype, and ontogeny, identifies pitfalls and gaps in these concepts, proposes to abandon the notion of simple mapping of genotype onto phenotype, and integrates them into a more complex, ontologically realistic model of development. The gene concept in biology embodies two distinct roles, inheritance and development: genes carry traits from one generation to the next and genes encode proteins that specify traits. As vehicles of inheritance genes are entirely passive. However, expression of traits suggests a more active role without specifying what genes do or what they produce. Resolving this ambiguity must focus on the cellular context in which genes operate during construction of the multicellular body. The phenotype (body) is a complex, highly ordered and temporally defined state, which requires energy to build and maintain. Genes provide templates for proteins necessary for harvesting energy, mediating exchange between a cell and its surroundings, repair, replication, and so forth. According to Harold [1], it is best to regard a cell ' a spatially structured self-organising system made of gene-specified elements´. In other words, genes specify the elements but not the system in which the elements operate. Ontogeny (Gk., onto-, existence, being + -geny, becoming) means literally 'coming into being´, constructing the multicellular body. As development progresses, the organism builds itself, and its phenotype (appearance, traits, organisation, function, etc.) changes rapidly. The modular body plan enables cell-by-cell control of gene expression. Each cell operates in a microenvironment of other cells, the protein meshwork of the extracellular matrix (ECM), and tissue fluids. Cell clusters organise into pockets, cavities or layers as cells divide, grow, change shape, move about, and pull on the fabric of the ECM or adjacent cells to produce an environment suitable for subsequent steps of development. Microenvironments and control of gene expression are the basis for differentiation.Some genes encode a single protein (or RNA molecule). Others encode several proteins (e.g., by alternative splicing), demonstrating that 1:1 mapping of genotype to phenotype is too simplistic. When extrapolated to a genome-wide perspective, genotype is clearly inadequate to specify phenotype, as recent cloning experiments confirm. The relationship between genotype and phenotype in a developing embryo is complex, recursive, and linked to the environment (Figure 1). Phenotype is a higher ontological category than genotype because phenotype includes both the structure and function of components needed to carry out metabolism, communication, replication, repair and other higher order (emergent) properties. Not only is the genotype a representation of a subset of the phenotypic properties, embryonic functions include control of gene expression, which frequently involves signals from other cells, signals not genetically encoded but crucial to survival, growth, and development of the embryo. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Joseph_Lewis:gecco05ws, author = {Joseph Lewis and Jamie Lawson}, title = {Behaviorally Coupled Emergent Representation}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {302--303}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0302.pdf}, abstract = {Traditionally, representation has been perceived as a necessity for producing intelligent behaviour. Once the right representation is in place to drive it, behaviour unfolds as the system's dynamics interact with what is usually a fixed, structural entity. For many kinds of systems this approach can be successful. However, as prescription for building increasingly complex adaptive systems,it often fails. An alternative perspective that is under investigation in our Starcat project suggests that representation is not what drives behaviour but rather what is left over by the system's dynamics after concepts have been activated and behaviour has emerged.There are numerous examples of patterns emerging fromunderlying dynamics. In an ant colony, for example, stigmergicbehavior arises from the colony's dynamics; but when viewedfrom outside the system, the pattern reveals the coupling between colony behaviour and the environment. We could, from that perspective, consider the pheromone and ant trails as a kind of representation. Particles in cellular automata offer another example that demonstrates how coupling with an external environment (here via the fitness function) draws particular behaviour out of the system's dynamics. Again, from an externalperspective, these appear to represent information about theenvironment. Prigogine's dissipative structures describe a similarphenomenon in physical systems far from equilibrium. Thefamiliar Bénard cells could be said to represent a certain level ofheat flow through viscous oil. The dynamic nature of these cellsfurther highlights the likelihood that emergent representationsare very fluid and likely to change as pressure from theenvironment changes. Representation in natural systems maywell be an emergent phenomenon, a consequence of the system'sdynamics, an echo of the coupling between the system's behaviorand the pressures from its environment.There is a family of cognitive architectures, related to Mitchelland Hofstadter's Copycat, which explores these possibilities. TheStarcat project attempts to generalize Copycat, bringing severalapplications under the same design. Starcat is intended toaddress problems in embodied cognition, where the systeminteracts with an environment and must produce behaviorindefinitely, in the face of changing pressures. It is anarchitecture for components that produce and consume codelets.The components swim in a virtual sea of different kinds ofcodelets. The components ignore some codelets and act uponothers, while frequently introducing new ones. Some Starcatcomponents couple to the environment, allowing the supply ofavailable codelets to be regulated externally. Each codelet is ashort-lived agent that may run and then die. Codelets are by theirnature small; and there are many different kinds associated withthe system. The primary job of a codelet is to build up or teardown perceptual structures. So codelet activity leaves an echobehind in the form of transient data structures. These datastructures 'represent´ Starcat's perceptions and applications ofconcepts.An interesting consequence of Starcat's emergentrepresentation is that the system's myriad micro-behaviors drivethe representation rather than, as in traditional systems,knowledge representation driving behavior. Additionally, thecoordinated aggregate behavior typical of complex adaptivesystems·arising from among the multitudes of interacting localagents·is coupled externally with the environment. In this way,viewed from the outside, the building up and tearing down ofmicrostructures looks like intentional representation.Knowledge representation in Starcat does not capture concepts,nor does it simply get in the way as Brooks² has asserted.Representation is what is leftover once concepts have emerged. Itallows the system to be affected by what it is already doing.Once a behavior is done, the representation can erode becausethe representation that had built up to support the recentlycompleted behavior is likely to have parts that are irrelevant tothe next behavior. New representation soon builds up as part ofthe next round of behavior, and the cycle continues.The system experiences pressure from outside, and thispressure changes what the system must do to continue tofunction, even though the specifics of those changes are dictatedentirely by its existing internal dynamics. The environmenttriggers behavior, but it does not specify behavior. This is thenotion of autopoeisis. We suspect that at an importantrelationship exists between autopoeisis, autonomy, and the kindof behaviorally coupled emergent representation that we havebeen describing. Our various applications and correspondingexperiments continue to examine if this is the case.We have developed a simulation of ant colony behavior thatuses a degenerate version of the Starcat architecture. Theexperiments reveal just how much the microbehaviors of anemergent system can produce macroscopic features that arecoupled to the environment. We are about to undertake a newexperiment in which the slipnet plays a shaping role for thecolony. This may be the lynchpin that ties the externalapprehension of representational behavior back into the system.We have an application that will use the perception of patterns inmusical input to shape the ongoing structural changes in theslipnet. We also have a set of agents that are each driven by anindividual slipnet but which interact with one another in acollective workspace. Finally, we are in the process ofreimplementing both Copycat and one of its successors, Madcat,using the Starcat framework. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Sanjeev_Kumar:gecco05ws, author = {Sanjeev Kumar}, title = {A Developmental Genetics-Inspired Approach to Robot Control}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {304--309}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0304.pdf}, abstract = {The need to build modular, scalable, and complex technologycapable of adaptation, self-assembly, and self-repair hasfuelled renewed interest in using approaches inspired by developmentalbiology. To meet this need, a new eld, calledComputational Development (CD), has emerged. Its focus ison adapting processes and mechanisms from developmentalbiology so as to help us build scalable, complex technology.Due to the embryonic nature of the eld, however, researchinvestigating the potential of such approaches for di erentproblem domains is crucial to its success. Theplausibility of applying a developmental biology-inspired approachto the demanding problem domain of reactive robotcontrol is explored. Using developmental genetics as a sourceof inspiration, a model of genetic regulatory networks is usedin conjunction with a spatially distributed evolutionary algorithmto evolve real-time robot controllers for tasks suchas general purpose obstacle avoidance. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(burjorjee:gecco05ws, author = {Keki Burjorjee and Jordan Pollack}, title = {Theme Preservation and the Evolution of Representation}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {310--320}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0310.pdf}, abstract = {In his thesis Toussaint calls for a "general project to develop theories on adaptationprocesses that account for the adaptation of representations". The theory developed is a contribution to this project. We first define the simple concept of agenotypic theme and define what it means for mutation operators to be theme preservingand theme altering. We use the idea of theme preservation to develop the concept ofsubrepresentation. Then we develop a theory that illuminates the behavior of amutation-only fitness proportional evolutionary algorithm in which mutation preservesgenotypic themes with high probability. Our theory shows that such evolutionaryalgorithms implicitly implement what we call subrepresentation evolvingmultithreaded evolution, i.e. such EAs conduct second-order search over a predeterminedset of representations and exploit promising representations for first order evolutionarysearch. We illuminate subrepresentaiton evolving multithreaded evolution by comparing andcontrasting it with the behavior of island model EAs. Our theory is immediately useful inunderstanding the significance of the low probability with which theme altering type 2mutations are applied to genotypes of the evolutionary systems in Toussaint's thesis. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(dejong:gecco05ws, author = {Edwin D. {de Jong} and Richard A. Watson and Dirk Thierens}, title = {A Generator for Hierarchical Problems}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {321--326}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0321.pdf}, abstract = {We describe a generator for hierarchical problems called theHierarchical Problem Generator (HPG). Hierarchical problems are ofinterest since they constitute a class of problems that can beaddressed efficiently, even though high-order dependencies betweenvariables may exist. The generator spans a wide ranges of hierarchicalproblems, and is limited to producing hierarchical problems. It istherefore expected to be useful in the study of hierarchical methods,as has already been demonstrated in experiments. The generator isfreely available for research use. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(janikow:gecco05ws, author = {Cezary Z. Janikow}, title = {Adaptable Representation in {GP}}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {327--331}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0327.pdf}, abstract = {Genetic Programming uses trees to represent chromosomes. The user defines the representation space by defining the set of functions and terminals to label the nodes in the trees. The sufficiency principle requires that the set be sufficient to label the desired solution trees, often forcing the user to enlarge the set, thus also enlarging the search space. Structure-preserving crossover, STGP, CGP, and CFG-based GP give the user the power to reduce the space by specifying rules for valid tree construction, based on types, syntax, and heuristics. These rules in effect change the representation. However, in general the user may not be aware of the best representation, including heuristics, to solve a particular problem. Last year, ACGP methodology was introduced for extracting local problem-specific heuristics, that is for learning a local model of the problem domain. ACGP discovers representation, in the space of probabilistic representations, one that improves the search itself and that provides the user with heuristics about the domain. We discuss and illustrate the probabilistic representation. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(moraglio:gecco05ws, author = {Alberto Moraglio and Riccardo Poli}, title = {Topological Crossover for the Permutation Representation}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {332--338}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0332.pdf}, abstract = {Topological crossovers are a class of representation-independent operators that are well-defined once a notion of distance over the solution space is defined. We explore how the topological framework applies to the permutation representation and in particular analyze the consequences of having more than one notion of distance available. Also, we study the interactions among distances and build a rational picture in which pre-existing recombination/crossover operators for permutation fit naturally. Lastly, we also analyze the application of topological crossover to TSP. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(toussaint:gecco05ws, author = {Marc Toussaint}, title = {Factorial Representations to Generate Arbitrary Search Distributions}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {339--345}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0339.pdf}, abstract = {A powerful approach to search is to try to learn a distribution ofgood solutions (in particular of the dependencies between theirvariables) and use this distribution as a basis to sample new searchpoints. Existing algorithms learn the search distribution directly onthe given problem representation. We ask how search distributions canbe modeled indirectly by a proper choice of factorial geneticcode. For instance, instead of learning a direct probabilistic modelof the dependencies between variables (like BOA does), one canalternatively learn a genetic representation of solutions on whichthese dependencies vanish. We consider questions like: Can everydistribution be induced indirectly by a proper factorialrepresentation? How can such representations be constructed from data?Are specific generative representations, like grammars or L-systems,universal w.r.t. inducing arbitrary distributions? We will considerlatent variable probabilistic models as a framework to address suchquestions and thereby also establish relations to machine learningconcepts like ICA. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(berntsson:gecco05ws, author = {Johan Berntsson}, title = {{G2DGA}: An Adaptive Framework for Internet-based Distributed Genetic Algorithms}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {346--349}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0346.pdf}, abstract = {The Internet is different from traditional parallel computing environments, and Distributed Genetic Algorithms (DGAs) for the Internet need to be designed to address these differences. We present a framework for Internet island-model DGAs that uses adaptation methods to maintain efficiency and robustness in a volatile and dynamic run-time environment. The applicability of the methods is demonstrated on benchmark tests, and a real-world optimisation problem in VLSI design. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @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 June}, 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(foong:gecco05ws, author = {Wai Kuan Foong and Holger R. Maier and Angus R. Simpson}, title = {Ant Colont Optimization for Power Plant Maintenance Scheduling Optimization}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {354--357}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0354.pdf}, abstract = {A formulation that enables ant colony optimization (ACO) algorithms to be applied to the power plant maintenance scheduling optimization (PPMSO) problem is developed and tested on a 21-unit case study. A heuristic formulation is introduced and its effectiveness in solving the problem is investigated. The results obtained indicate that the performance of ACO algorithms is significantly better than that of a number of other metaheuristics, such as genetic algorithms and simulated annealing, which have been applied to the same case study previously. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(hayes:gecco05ws, author = {Christina Savannah Maria Hayes and Tom\'{a}\v{s} Gedeon}, title = {Hyperbolic Fixed Points are Typical in the Space of Mixing Operators for the Infinite Population Genetic Algorithm}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {358--361}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0358.pdf}, abstract = {We study an infinite population model for the genetic algorithm, where the iteration of the algorithm corresponds to an iteration of a map $G$. The map $G$ is a composition of a selection operator and a mixing operator, where the latter models effects of both mutation and crossover. We examine the hyperbolicity of fixed points of this model. We show that for a typical mixing operator all the fixed points are hyperbolic. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(landa:gecco05ws, author = {Ricardo Landa Becerra and Carlos A. {Coello Coello}}, title = {Use of Domain Information to Improve the Performance of an Evolutionary Algorithm}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {362--365}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0362.pdf}, abstract = {Evolutionary The main goal of this thesis work is to explore the capacities of cultural algorithms to add domain knowledge in evolutionary computation. Within ourobjectives is to develop a cultural algorithm for constrained optimisation, and other for multiobjective optimisation. With a proper desing of the belief space we expect to obtain competitive results compared with other state-of-the-art evolutionary algorithms, but reducing the number of fitness function evaluations needed. We focus in the algorithm for constrained optimisation,because the development of the algorithm for multiobjective optimzation is anearly stage. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(lapointe:gecco05ws, author = {Fran\c{c}ois-Joseph Lapointe}, title = {Choreogenetics: the Generation of Choreographic Variants Through Genetic Mutations and Selection}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {366--369}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0366.pdf}, abstract = {A genetic algorithm is introduced to generate variants of a choreographic sequence, which are then selected using different criteria. The mutation phase of the algorithm applies six types of mutations on single sequences, as well as four types of mutations on multiple sequences. Six different selection criteria are also distinguishable to assess the fitness of the sequences. An application of Choreogenetics is presented to illustrate the performance of the method for the generation of an aesthetic choreography. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(lehmann:gecco05ws, author = {Katharina A. Lehmann}, title = {Why simulating evolutionary processes is just as interesting as applying them}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {370--373}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0370.pdf}, abstract = {Evolutionary algorithms are very efficient tools to find a near-optimum solution in many cases. Until now they have been mostly used to find results but we argue that evolutionary algorithms can also be used to simulate the evolution of complex systems. We model complex systems as networks in which agents are connected by edges if they interact with each other. It is known that many networks of this kind exhibit stable properties despite the dynamic processes they are subject to.We show here how evolutionary processes on complex systems can be modelled with a new kind of evolutionary algorithm which we have presented in \cite{lk-eaftsoeon-05}. We will show that some evolutionary processes within this framework yield networks with stable properties in reasonable time. An understanding of what kind of evolutionary processes will produce what kind of network properties in what time is vital to transfer evolutionary processes to technical ad-hoc networks in order to improve their flexibility and stability in quickly changing environments. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(loiacono:gecco05ws, author = {Daniele Loiacono and Pier Luca Lanzi}, title = {Improving Generalization in the {XCSF} Classifier System Using Linear Least-Squares}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {374--377}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0374.pdf}, abstract = {Least-Squares XCSF is an extension of XCS in which classifier prediction is computed as a linear combination of classifier inputs and a weight vector associated to each classifier. XCSF can adjustthe weight vector of classifiers to evolve accurate piecewiselinear approximations of functions. The Widrow-Hoff rule,used to update the weight vectors, prevents (when someconditions hold) XCSF from exploiting the expected piecewiselinear approximation. We replace theWidrow-Hoff rule with linear least-squares and we show thatwith this improvement XCSF can fully exploit its generalizationcapabilities. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(majeed:gecco05ws, author = {Hammad Majeed}, title = {A New Approach to Evaluate {GP} Schema in Context}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {378--381}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0378.pdf}, abstract = {Evaluating GP schema in context is considered to be a complex,and, at times impossible, task. The tightly linked nodes of a GP tree is the main reason behind its complexity. We present a new approach to evaluate GP schema in context. It is simple in its implementation with a potential to address well-known GP problems, such as identification of significant schema, dead code (introns) and module acquisition to name a few. It is based on the principle that the contribution of a schema can be evaluated by neutralising the effect of the schema in the tree containing it (container-tree) and then checking its effect on the container-tree's fitness. Its usefulness is empirically demonstrated along with its limitation. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(khemka:gecco05ws, author = {Namrata Khemka and Christian Jacob and Gerald Cole}, title = {Making Soccer Kicks Better: A Study in Particle Swarm Optimization}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {382--385}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0382.pdf}, abstract = {Computer simulation is a useful tool for investigatingmathematical models of human muscle movement. These, in turn, canbe used to help design equipment for sports activities. Onebiomechanics example is the simulation of a high-speed soccerkick. We used an evolutionary algorithm, based on a particle swarmoptimizer, to adjust muscle control parameters for a soccer kick. We describe our implementation of the soccer kickproject, followed by our successful experiments performed with thesoccer kick. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(skolicki:gecco05ws, author = {Zbigniew Skolicki}, title = {An Analysis of Island Models in Evolutionary Computation}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {386--389}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0386.pdf}, abstract = {A need for solving more and more complex problems drives the Evolutionary Computation community towards advanced models of Evolutionary Algorithms. One such model is the island model which, although the subject of a variety of studies, still needs additional fundamental research. In my Ph.D. thesis I am aiming at studying the behaviour of island models with regard to the amount of cooperation between islands, the level of heterogeneity and the difficulty of the problem being solved. We present the main ideas and gathers preliminary results. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Kahraman:gecco05ws, author = {Aynur Kahraman and H. Aydolu Seven}, title = {Healthy Daily Meal Planner}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {390--393}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0390.pdf}, abstract = {The purpose of this project is to develop a program that solves a bi-objective diet problem to propose the user a 'healthy´ daily meal according to some parameters specified by the user. The program will interact with the user via a graphical interface to receive information such as age and gender that is necessary to determine daily nutritional and energy requirements and also information on the preferences of the user among the dishes available. The user will be presented all the dishes and is expected to rate some of them on a scale from 1 to 10. The main goal is to present the user a combination of dishes that satisfies the daily nutritional requirements, minimizes the cost of the daily meal and maximizes the total rating of the meal. In this paper, first the diet problem is going to be introduced, then a genetic algorithm to solve the problem will be presented. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Karpuzcu:gecco05ws, author = {Ulya Rahmet Karpuzcu}, title = {Automatic Verilog Code Generation through Grammatical Evolution}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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 = {394--397}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0394.pdf}, abstract = {We investigate the automatic generation of Verilog code, representing digital circuits through Grammatical Evolution (GE). Preliminary tests using a simple full adder generation problem have been performed. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Kowall:gecco05ws, author = {Correy Allen Kowall}, title = {Braitenberg Simulations as Vehicles of Evolution}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {398--401}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0398.pdf}, abstract = {We describe a series of simulations that serve as a verification of the abstract similarity between vehicular and animal navigation. Valentino Braitenberg used this similarity to illustrate that vehicles controlled by very simple biologically inspired circuits, manifest a wonderful diversity of complex animal behaviors. By constructing a series of experiments that are designed around the possibility of interchanging phenomena that affect vehicles or animals, we hope to show that this analogical similarity is a useful tool. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Kriplean:gecco05ws, author = {Travis L. Kriplean}, title = {Evolving an Ecology of Two-Tiered Organizations}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {402--406}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0402.pdf}, abstract = {Evolutionary models typically rely on a single level of evolution for training a team of cooperating agents. I present a model that evolves at two levels·an 'organizational´ level and the more traditional 'individual´ level. Each organization contains an embedded agent population that goes through a full evolutionary process every organizational time-step. The organization's genetic code is essentially a policy that specifies the training process for its embedded agents. It also defines the creation of a representative team that is compiled after each organizational time-step. An organization's fitness is based on the performance of this representative team. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Suarez:gecco05ws, author = {David Enrique {Suarez Pinzon} and Julian Yezid {Olarte Ramos} and Sergio Andres {Rojas Galeano}}, title = {Evolving Object Oriented Agent Programs in Robocup Domain}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {407--410}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0407.pdf}, abstract = {We describe the application of object oriented genetic programming to the automatic generation of agents under the Object Oriented Paradigm. To generate the agent programs code, we evolve concurrently the methods that represent the agent-environment interaction. We use like terminals and operations the objects that correspond to the context elements. This study uses the simulation league of the Robot World Cup (Robocup) like a testing environment. The fitness function used evaluates the behavior of agent player in several levels that indicates the learning progress. The experimental results indicate that is possible the agent programs evolution under the Object Oriented Paradigm. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, ) @InProceedings(Vishakh:gecco05ws, author = {Vishakh and Nicholas John Urrea and Tadashi Nakano and Tatsuya Suda}, title = {A Resource-Allocation Mechanism for Multiagent Networks}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2005)} workshop program}, year = {2005}, month = {25-29 June}, 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}, pages = {411--414}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005wks/papers/0411.pdf}, abstract = {The nature of computer networks and the manner in which network services are provided are changing dramatically. Network architectures that employ virtual mobile agents to provide services are under development. We describe Price Propagation, which is based on pricing in market economies. It is a decentralized adaptive mechanism for regulating agent behavior, with the goal of allocating computational resources optimally. }, notes = {Distributed on CD-ROM at GECCO-2005. ACM 1-59593-097-3/05/0006}, )