VOLUME 1 CONTENTS GENETIC ALGORITHMS AND CLASSIFIER SYSTEMS Approaches Based on Genetic Algorithms for Multiobjective Optimization Problems Jose Aguilar and Pablo Miranda . . . . . . . . . . . . . . . . . . 3 A Genetic Programming­based Classifier System Manu Ahluwalia and Larry Bull . . . . . . . . . . . . . . . . 11 Aliasing in XCS and the Consecutive State Problem: 1 -- Effects Alwyn Barry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Aliasing in XCS and the Consecutive State Problem: 2 -- Solutions Alwyn Barry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Three Ways to Grow Designs: A Comparison of Embryogenies for an Evolutionary Design Problem Peter Bentley and Sanjeev Kumar . . . . . . . . . . . . . . . . 35 A Hybrid Genetic Algorithm for the Vehicle Routing Problem with Time Windows and Itinerary Constraints Jean Berger, Mourad Sassi, and Martin Salois . . . . . . . 44 Comparing Reinforcement Learning Algorithms Applied to Crisp and Fuzzy Learning Classifier Systems Andrea Bonarini . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 Linkage Information Processing In Distribution Estimation Algorithms Peter A. N. Bosman and Dirk Thierens . . . . . . . . . . . . 60 Reducing Genetic Drift in Steady State Evolutionary Algorithms Jürgen Branke, Massimo Cutaia, and Heinrich Dold . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 A Diversity Study in Genetic Algorithms for Job Shop Scheduling Problems Carlos A. Brizuela and Nobuo Sannomiya . . . . . . . . . . 75 On using ZCS in a Simulated Continuous Double­Auction Market Larry Bull . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 Topologies, Migration Rates, and Multi­Population Parallel Genetic Algorithms Erick Cantú­Paz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 Neuro­Genetic Based Method to the Classification of Acupuncture Needle: A Case Study Lijuan Cao and Tay Eng Hock (Francis) . . . . . . . . . . . 99 Minimum­Allele­Reserve­Keeper (MARK): A Fast and Effective Mutation Scheme for Genetic Algorithm (GA) Zeke S. H. Chan, H. W. Ngan, and A. B. Rad . . . . . 106 Genetic Algorithms, Trading Strategies and Stochastic Processes: Some New Evidence from Monte Carlo Simulations Shu­Heng Chen, Wei­Yuan Lin, and Chueh­Iong Tsao . . . . . . . . . . . . . . . . . . . . . . . . . 114 Introducing a New Advantage of Crossover: Commonality­Based Selection Stephen Chen and Stephen F. Smith . . . . . . . . . . . . . . 122 Non­Standard Crossover for a Standard Representation---Commonality­Based Feature Subset Selection Stephen Chen, César Guerra­Salcedo, and Stephen F. Smith . . . . . . . . . . . . . . . . . . . . . . . . . 129 Improving Genetic Algorithms by Search Space Reductions (with Applications to Flow Shop Scheduling) Stephen Chen and Stephen F. Smith . . . . . . . . . . . . . . 135 Dynamic Degree Constrained Network Design: A Genetic Algorithm Approach Chao­Hsien Chu, G. Premkumar, Carey Chou, and Jianzhong Sun . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 An Immunogenetic Approach to Spectra Recognition Dipankar Dasgupta, Yuehua Cao, and Congjun Yang . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 Towards a Simulation of Natural Mutation I. De Falco, A. Iazzetta, E. Tarantino, A. Della Cioppa, and A. Iacuelli. . . . . . . . . . . . . . . . . 156 Construction of Test Problems for Multi­Objective Optimization Kalyanmoy Deb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 Self­Adaptation in Real­Parameter Genetic Algorithms with Simulated Binary Crossover Kalyanmoy Deb and Hans­Georg Beyer . . . . . . . . . . . 172 Modular and Hierarchial Evolutionary Design of Fuzzy Systems Myriam Delgado, Fernando Von Zuben, and Fernando Gomide . . . . . . . . . . . . . . . . . . . . . . . . 180 On The Design of Genetic Algorithms for Geographical Applications S. van Dijk, D. Thierens, and M. de Berg . . . . . . . . . 188 Metamodeling Techniques For Evolutionary Optimization of Computationally Expensive Problems: Promises and Limitations Mohammed A. El­Beltagy, Prasanth B. Nair, and Andy J. Keane . . . . . . . . . . . . . . . . . . . . . . . . . . 196 Analytical Solutions for Infinite Population Genetic Algorithms on Multiplicative Landscape Hiroshi Furutani . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 Using Time Efficiently: Genetic­Evolutionary Algorithms and the Continuation Problem David E. Goldberg . . . . . . . . . . . . . . . . . . . . . . . . . . 212 Optimizing Global­Local Search Hybrids David E. Goldberg and Siegfried Voessner . . . . . . . . . 220 Terrain­Based Genetic Algorithm (TBGA): Modeling Parameter Space as Terrain V. Scott Gordon, Rebecca Pirie, Adam Wachter, and Scottie Sharp . . . . . . . . . . . . . . . . . . . . . . . . . . . 229 Genetic Approach to Feature Selection for Ensemble Creation César Guerra­Salcedo and Darrell Whitley . . . . . . . . 236 Coevolution for Problem Simplification Gary L. Haith, Silvano P. Colombano, Jason D. Lohn, and Dimitris Stassinopoulos . . . . . . . . 244 Coevolutionary Genetic Algorithms for Solving Dynamic Constraint Satisfaction Problems Hisashi Handa, Osamu Katai, Tadataka Konishi, and Mitsuru Baba . . . . . . . . . . . . . . . . . . . . . . . . . . 252 A parameter­less genetic algorithm Georges R. Harik and Fernando G. Lobo . . . . . . . . . . 258 Accuracy­based fitness allows similar performance to humans in static and dynamic classification environments Adrian R. Hartley . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 The Outlaw Method for Solving Multimodal Functions with Split Ring Parallel Genetic Algorithms K. Burton Harvey and Chrisila C. Pettey . . . . . . . . . 274 Polynomial Time Summary Statistics for a Generalization of MAXSAT Robert B. Heckendorn, Soraya Rana, and Darrell Whitley . . . . . . . . . . . . . . . . . . . . . . . . . 281 Intelligent Genetic Algorithm with a New Intelligent Crossover Using Orthogonal Arrays Shinn­Ying Ho, Li­Sun Shu, and Hung­Ming Chen . . . . . . . . . . . . . . . . . . . . . . . 289 Parental and Cyclic­Rate Mutation in Genetic Algorithms: An Initial Investigation Theodore P. Hoehn and Chrisila C. Pettey . . . . . . . . . 297 Controlling the Cooperative­Competitive Boundary in Niched Genetic Algorithms Jeffrey Horn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305 Function Induction, Gene Expression, And Evolutionary Representation Construction Hillol Kargupta and Kakali Sarkar . . . . . . . . . . . . . . 313 Iterated Local Search Approach using Genetic Transformation to the Traveling Salesman Problem Kengo Katayama and Hiroyuki Narihisa . . . . . . . . . . 321 Deletion Schemes for Classifier Systems Tim Kovacs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329 Extending the Representation of Classifier Conditions Part I: From Binary to Messy Coding Pier Luca Lanzi . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 Extending the Representation of Classifier Conditions Part II: From Messy Coding to S­Expressions Pier Luca Lanzi and Alessandro Perrucci. . . . . . . . . . . 345 An Extension to the XCS Classifier System for Stochastic Environments Pier Luca Lanzi and Marco Colombetti . . . . . . . . . . . 353 Swappers: Introns promote flexibility, diversity and invention James R. Levenick . . . . . . . . . . . . . . . . . . . . . . . . . . . 361 Chance­Constrained Genetic Algorithms Daniel H. Loughlin and S. Ranji Ranjithan . . . . . . . 369 A Sequential Similarity Metric for Case Injected Genetic Algorithms applied to TSPs Sushil J. Louis and Yongmian Zhang . . . . . . . . . . . . . 377 Interactive Genetic Algorithms for the Traveling Salesman Problem Sushil J. Louis and Rilun Tang . . . . . . . . . . . . . . . . . 385 A Flipping Genetic Algorithm for Hard 3­SAT Problems Elena Marchiori and Claudio Rossi . . . . . . . . . . . . . . 393 Declarative expression of biases in Genetic Programming Lionel Martin, Frédéric Moal, and Christel Vrain . . . 401 Probabilistic Crowding: Deterministic Crowding with Probabilisitic Replacement Ole J. Mengshoel and David E. Goldberg . . . . . . . . . 409 Genetic Algorithms for Binary Quadratic Programming Peter Merz and Bernd Freisleben . . . . . . . . . . . . . . . . 417 Randomness and GA Performance, Revisited Mark M. Meysenburg and James A. Foster . . . . . . . . . 425 Identifying Linkage Groups by Nonlinearity/Non­monotonicity Detection Masaharu Munetomo and David E. Goldberg . . . . . . 433 Specification of Local Search Directions in Genetic Local Search Algorithms for Multi­Objective Optimization Problems Tadahiko Murata, Hisao Ishibuchi, and Mitsuo Gen . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441 Mining the Space of Generality with Uncertainty­Concerned Cooperative Classifiers Jorge Muruzábal . . . . . . . . . . . . . . . . . . . . . . . . . . . . 449 Learning Bayesian Networks from Incomplete Data using Evolutionary Algorithms James W. Myers, Kathryn B. Laskey, and Kenneth A. DeJong . . . . . . . . . . . . . . . . . . . . . . . 458 Evolutionary Path Planning for Nonholonomic Robots Ana Neves, Arlindo Silva, and Ernesto Costa . . . . . . . 466 Adaptive Strategies and the Design of Evolutionary Applications José Neves, Miguel Rocha, Hugo Rodrigues, Miguel Biscaia, and José Alves . . . . . . . . . . . . . . . . . . 473 Variation in EA Performance Characteristics on the Adaptive Distributed Database Management Problem Martin Oates, David Corne, and Roger Loader . . . . . 480 On Recombination and Optimal Mutation Rates Gabriela Ochoa, Inman Harvey, and Hilary Buxton . . . . . . . . . . . . . . . . . . . . . . . . . . 488 A Robust Real­Coded Genetic Algorithm using Unimodal Normal Distribution Crossover Augmented by Uniform Crossover: Effects of Self­Adaptation of Crossover Probabilities Isao Ono, Hajime Kita, and Shigenobu Kobayashi . . . 496 The Shifting Balance Genetic Algorithm: Improving the GA in a Dynamic Environment Franz Oppacher and Mark Wineberg . . . . . . . . . . . . 504 Evolving Probabilistic Chromosomes in Genetic Algorithms Paolo Palazzari and Moreno Coli . . . . . . . . . . . . . . . 511 Adaptive Hexapod Gait Control Using Anytime Learning with Fitness Biasing Gary B. Parker and Jonathan W. Mills . . . . . . . . . . . . 519 BOA: The Bayesian Optimization Algorithm Martin Pelikan, David E. Goldberg, and Erick Cantú­Paz . . . . . . . . . . . . . . . . . . . . . . . . 525 Visualization of Evolutionary Algorithms---Set of Standard Techniques and Multidimensional Visualization Hartmut Pohlheim . . . . . . . . . . . . . . . . . . . . . . . . . . 533 Problem Solving: Search, Exploration and Co­evolution Josiah Poon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 541 The Distributional Biases of Crossover Operators Soraya Rana . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 549 Finding attractors for periodic fitness functions Jonathan E. Rowe . . . . . . . . . . . . . . . . . . . . . . . . . . . 557 Linkage Crossover For Genetic Algorithms Ayed A. Salman, Kishan Mehrotra, and Chilukuri K. Mohan . . . . . . . . . . . . . . . . . . . . . 564 The Behavior of Spatially Distributed Evolutionary Algorithms in Non­Stationary Environments Jayshree Sarma and Kenneth De Jong . . . . . . . . . . . . 572 Parallel Distributed Processing of a Parameter­free Hierarchical Migration Methods Hidefumi Sawai and Susumu Adachi . . . . . . . . . . . . 579 Designing Cellular Automata­based Scheduling Algorithms Franciszek Seredynski and Cezary Z. Janikow . . . . . . 587 Continuing Beyond NFL: Dissecting real world problems Oliver Sharpe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 595 A Diversity Control Oriented Genetic Algorithm (DCGA): Development and Experimental Results Hisashi Shimodaira . . . . . . . . . . . . . . . . . . . . . . . . . . 603 Transposition versus Crossover: An Empirical Study Anabela Borges Simões and Ernesto Costa . . . . . . . . . . 612 A Genetic Algorithm without Parameters Tuning and its Application on the Floorplan Design Problem Hiroshi Someya and Masayuki Yamamura . . . . . . . . . 620 Stochastic Evolution on the Hierarchical Population Koji Sugai . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 628 Evolution of Constraint Satisfaction Strategies in Examination Timetabling Hugo Terashima­Marín, Peter Ross, and Manuel Valenzuela­Rendón . . . . . . . . . . . . . . . . . 635 Estimating the Significant Non­Linearities in the Genome Problem­Coding Dirk Thierens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 643 On Corporate Classifier Systems: Increasing the Benefits of Rule Linkage Andy Tomlinson and Larry Bull . . . . . . . . . . . . . . . . . 649 Multi­parent Recombination with Simplex Crossover in Real Coded Genetic Algorithms Shigeyoshi Tsutsui, Masayuki Yamamura, and Takahide Higuchi . . . . . . . . . . . . . . . . . . . . . . . . 657 The Job Shop Problem Solved with Simple, Basic Evolutionary Search Elements Patrick Van Bael, Dirk Devogelaere, and M. Rijckaert . . . . . . . . . . . . . . . . . . . . . . . . . . . . 665 Biases Introduced by Adaptive Recombination Operators Kanta Vekaria and Chris Clack . . . . . . . . . . . . . . . . . 670 Evolutionary Algorithm For Structural Optimization Mark S. Voss and Christopher M. Foley . . . . . . . . . . . 678 Genetic Programming Operators Applied to Genetic Algorithms Dana Vrajitoru . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 686 Habitat, Communication and Cooperative Strategies Kyle Wagner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 694 A Performance Assessment of Modern Niching Methods for Parameter Optimization Problems Jean­Paul Watson . . . . . . . . . . . . . . . . . . . . . . . . . . . . 702 Incremental Commitment in Genetic Algorithms Richard A. Watson and Jordan B. Pollack . . . . . . . . . . 710 Timeweaver: a Genetic Algorithm for Identifying Predictive Patterns in Sequences of Events Gary M. Weiss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 718 A Free Lunch Proof for Gray versus Binary Encodings D. Whitley . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 726 Markov Chain Models of Genetic Algorithms Alden H. Wright and Yong Zhao . . . . . . . . . . . . . . . . 734 A Group Encoding Technique for Set Partitioning Problems Congjun Yang, Dipankar Dasgupta, and Yuehua Cao . . . . . . . . . . . . . . . . . . . . . . . . . . . . 742 A Supervisory Simplex­GA Approach for Metabolic Model Optimization John Yen, Linyu Yang, Bogju Lee, and James C. Liao . . . . . . . . . . . . . . . . . . . . . . . . . . 750 A Computational Model of a Viewpoint­Forming Process in a Hierarchical Classifier System Takahiro Yoshimi and Toshiharu Taura . . . . . . . . . . . 758 GENETIC ALGORITHMS AND CLASSIFIER SYSTEMS, POSTER PAPERS Scout Algorithms and Genetic Algorithms: A Comparative Study Fabio Abbattista, Valeria Carofiglio, and Mario Köppen . . . . . . . . . . . . . . . . . . . . . . . . . . 769 Random Systems with Complete Connections Alexandru Agapie . . . . . . . . . . . . . . . . . . . . . . . . . . . 770 Three Geometric Approaches for representing Decision Rules in a Supervised Learning System Jesús Aguilar, José Riquelme, and Miguel Toro . . . . . . 771 Cooperative Crossover and Mutation Operators in Genetic Algorithms Hernán E. Aguirre, Kiyoshi Tanaka, and Tatsuo Sugimura . . . . . . . . . . . . . . . . . . . . . . . . 772 Entropic and Real­Time Analysis of the Search with Panmictic, Structured, and Parallel Distributed Genetic Algorithms Enrique Alba, Carlos Cotta, and José M. Troya . . . . . 773 Using an Adaptive Agent to Bid in a Simplified Model of the UK Market in Electricity A. J. Bagnall and G. D. Smith . . . . . . . . . . . . . . . . . 774 Migration Policies and Takeover Times in Genetic Algorithms Erick Cantú­Paz . . . . . . . . . . . . . . . . . . . . . . . . . . . . 775 Classification of the Market States Using Neural Network Lijuan Cao, Tay Eng Hock (Francis), Ma Lawrence, and Wai Cheong Yeong . . . . . . . . . . . . . . . . . . . . . . . . 776 Collaborative Learning Agents with Structural Classifier Systems Maezawa Chikara and Atsumi Masayasu . . . . . . . . 777 Rule Acquisition with a Genetic Algorithm Robert Cattral, Franz Oppacher, and Dwight Deugo . . . . . . . . . . . . . . . . . . . . . . . . . . 778 Comparing Performance of the Learnable Evolution Model and Genetic Algorithms Mark Coletti, Thomas D. Lash, Ryszard Michalski, Craig Mandsager, and Rida Moustafa . . . . . . . . . . . . 779 A Comparison of Search Space Visualization Techniques Trevor D. Collins . . . . . . . . . . . . . . . . . . . . . . . . . . . 780 Non­stationary Function Optimization using Polygenic Inheritance J. J. Collins and Conor Ryan . . . . . . . . . . . . . . . . . . . 781 Genetic Planner for a Mobile Robot Navigation System J. J. Collins, Lucia Sheehan, and Conor Casey . . . . . . 782 Improving the Scalability of Dynastically Optimal Forma Recombination by Tuning the Granularity of the Representation Carlos Cotta, Enrique Alba, and José M a Troya . . . . . 783 Efficient Calculation of Compute­Intensive Fitness In Genetic Computations Using A Survival Indicator For Popu l at ion Members William Edelson and Michael L. Gargano . . . . . . . . . 784 Representation of Music in a Learning Classifier System Utilizing Bach Chorales Francine Federman, Gayle Sparkman, and Stephanie Watt . . . . . . . . . . . . . . . . . . . . . . . . . . 785 Portfolios of Genetic Algorithms Alex S. Fukunaga . . . . . . . . . . . . . . . . . . . . . . . . . . . 786 Evolutionary Algorithms for Multidimensional Knapsack Problems: the Relevance of the Boundary f the Feasible Region Jens Gottlieb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 787 Sequencing Aircraft Landings by Genetic Algorithms Alexis Guigue, Sofiane Oussedik, and Daniel Delahaye . . . . . . . . . . . . . . . . . . . . . . . . . 788 Evaluating Learning Classifier System Performance In Two­Choice Decision Tasks: An LCS Metric Toolkit John H. Holmes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 789 Simultaneously Applying Multiple Crossover and Mutation Operators Tzung­Pei Hong, Hong­Shung Wang, and Wei­Chou Chen . . . . . . . . . . . . . . . . . . . . . . . . . 790 Redundant Genetic Encodings May Not Be Harmful Bryant A. Julstrom . . . . . . . . . . . . . . . . . . . . . . . . . . 791 Genetic Algorithm, Avoiding of Deadlocks and Gantt­Chart­Generation for the Job Shop Scheduling Problem J. Käschel, Gunnar Köbernik, Bernd Meier, and Tobias Teich . . . . . . . . . . . . . . . . . . . . . . . . . . . . 792 Using Genetic Algorithms to Extract Rules From Trained Neural Networks Edward Keedwell, Ajit Narayanan, and Dragan Savic . . . . . . . . . . . . . . . . . . . . . . . . . . . 793 A New Evolutionary Approach to the Degree Constrained Minimum Spanning Tree Problem Joshua Knowles, David Corne, and Martin Oates . . . . . . . . . . . . . . . . . . . . . . . . . . . 794 Computing Simple GA Expected Waiting Times Gary J. Koehler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 795 Complexity Engineering, Evolution and Optimality of Structures Sourav Kundu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 796 GENIFER: A Nearest Neighbour based Classifier System using GA Francesc Xavier Llorà i Fàbrega and Josep Maria Garrell i Guiu . . . . . . . . . . . . . . . . . 797 Using Genetic Algorithms For Adaptive Function Approximation and Mesh Generation Rida E. Moustafa, Kenneth A. De Jong, and Edward J. Wegman . . . . . . . . . . . . . . . . . . . . . . 798 The Entropy Evaluation Method for the Thermodynamical Selection Rule Mori Naoki and Kita Hajime . . . . . . . . . . . . . . . . . . 799 A Motivated Definition of Exploitation and Exploration Bart Naudts and Adriaan Schippers . . . . . . . . . . . . . 800 On Evolution of stochastic dynamical neural networks Fernando Niño, German Hernandez, and Dipankar Dasgupta . . . . . . . . . . . . . . . . . . . . . . 801 Finding Wavelet Packet Bases with an Estimation Distribution Algorithm Alberto Ochoa Rodríguez . . . . . . . . . . . . . . . . . . . . . . 802 An Evolutionary Approach to Feature Set Selection David W. Opitz . . . . . . . . . . . . . . . . . . . . . . . . . . . . 803 Evolving High­Quality Random Number Generators Mathieu Perrenoud, Marco Tomassini, Moshe Sipper, and Mosé Zolla . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 804 A Logarithmic Mutation Operator to Solve Constrained Optimization Problems A. Petrowski and S. Ben Hamida . . . . . . . . . . . . . . . 805 Schema Theorems without Expectations Riccardo Poli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 806 An Evolutionary Approach to Point­Feature Label Placement Günther R. Raidl . . . . . . . . . . . . . . . . . . . . . . . . . . . 807 Genetic Algorithms in Road Investment Planning with Computational Comparisons to Simulated Annealing and Heuristics Vivian Salim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 808 XCS and the Monk's Problems Shaun Saxon and Alwyn Barry . . . . . . . . . . . . . . . . . 809 Fast and Robust Convergence of Chained Classifiers by Generating Operons through Niche Formation Sotaro Shimada and Yuichiro Anzai . . . . . . . . . . . . . 810 Neural network construction using Voronoi dissections Richard Stewart and Paul L. Rosin . . . . . . . . . . . . . . 811 The Difference between Individual and Population Genetic Algorithms Nicolaas J. Vriend . . . . . . . . . . . . . . . . . . . . . . . . . . . 812 Solving Combinatorial Optimization Problems with Multi­Step Genetic Algorithms Hirokazu Watabe and Tsukasa Kawaoka . . . . . . . . . . 813 Adaptive Genetic Algorithm for Multiprocessor Scheduling Mohamed M. Zahran, Ashraf H. Abdel Wahab, and Samir I. Shaheen . . . . . . . . . . . . . . . . . . . . . . . . 814 EVOLUTION STRATEGIES AND EVOLUTIONARY PROGRAMMING Fitness Noise and Localization Errors of the Optimum in General Quadratic Fitness Models Hans­Georg Beyer and Dirk V. Arnold . . . . . . . . . . . . 817 Extremal Optimization: Methods derived from Co­Evolution Stefan Boettcher and Allon G. Percus . . . . . . . . . . . . . 825 Perhaps Not a Free Lunch But At Least a Free Appetizer Stefan Droste, Thomas Jansen, and Ingo Wegener . . . . 833 Real­valued Evolutionary Optimization using a Flexible Probability Density Estimator Marcus Gallagher, Marcus Frean, and Tom Downs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 840 An Analysis of Local Selection in Evolution Strategies Martina Gorges­Schleuter . . . . . . . . . . . . . . . . . . . . . 847 Comparing Evolutionary Programs and Evolutionary Pattern Search Algorithms: A Drug Docking Application William E. Hart . . . . . . . . . . . . . . . . . . . . . . . . . . . . 855 Stochastic Differential Model for Evolutionary Algorithms over Continuous Spaces German Hernandez, Jerome A. Goldstein, and Fernando Niño . . . . . . . . . . . . . . . . . . . . . . . . . . 863 An Efficient Generalized Multiobjective Evolutionary Algorithm Shinn­Ying Ho and Xiao­I Chang . . . . . . . . . . . . . . . 871 A Depth Controlling Strategy for Strongly Typed Evolutionary Programming Claire J. Kennedy and Christophe Giraud­Carrier . . . 879 Evolutionary Programming using the Levy Probability Distribution Chang­Yong Lee and Yoonseon Song . . . . . . . . . . . . . . 886 MOAQ an Ant­Q Algorithm for Multiple Objective Optimization Problems Carlos E. Mariano and Eduardo Morales M. . . . . . . . 894 An Evolution Strategy with Coordinate System Invariant Adaptation of Arbitrary Normal Mutation Distributions Within the Concept of Mutative Strategy Parameter Control Andreas Ostermeier and Nikolaus Hansen . . . . . . . . . 902 Training Neural Networks with 3­bit Integer Weights V. P. Plagianakos and M. N. Vrahatis . . . . . . . . . . . . 910 Voting Teams: A cooperative approach to non­typical problems using genetic programming Terence Soule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 916 Extending Evolutionary Programming Methods to the Learning of Dynamic Bayesian Networks Allan Tucker and Xiaohui Liu . . . . . . . . . . . . . . . . . 923 An Evolutionary Algorithm for Continuous Global optimization Jinn­Moon Yang and Cheng­Yan Kao . . . . . . . . . . . . 930 EVOLUTION STRATEGIES AND EVOLUTIONARY PROGRAMMING, POSTER PAPERS Coevolving Mutualists Guide Simulated Evolution Michael L. Best . . . . . . . . . . . . . . . . . . . . . . . . . . . . 941 G­Prop­III: Global Optimization of Multilayer Perceptrons using an Evolutionary Algorithm P. A. Castillo, V. Rivas, J. J. Merelo, J. González, A. Prieto, and G. Romero . . . . . . . . . . . . . . . . . . . . . 942 An Evolution Strategy to Solve Sports Scheduling Problems Hsien­Da Huang, Jih Tsung Yang, Shu Fong Shen, and Jorng­Tzong Horng . . . . . . . . . . . . . . . . . . . . . . . 943 Training Hidden Markov Models using Population­Based Learning Bruce Maxwell and Sven Anderson . . . . . . . . . . . . . . 944