Genetic Programming: 6th European Conference, EuroGP 2003, Essex, UK, April 14-16, 2003. Proceedings (Lecture Notes in Computer Science, 2610)

In this volume we present the accepted contributions to the Sixth European Conference on Genetic Programming (EuroGP 2003) which took place at the University of Essex, UK on 14-16 April 2003. EuroGP is now a well-established conference and, without any doubt, the most important international event - voted to Genetic Programming occurring in Europe. The proceedings have all been published by Springer-Verlag in the LNCS series. EuroGP began as an - ternational workshop in Paris, France in 1998 (14{u2013}15 April, LNCS 1391). Sub- quently the workshop was held in G{u00A8} oteborg, Sweden in 1999 (26{u2013}27 May, LNCS 1598) and then EuroGP became an annual conference: in 2000 in Edinburgh, UK (15{u2013}16 April, LNCS 1802), in 2001 in Lake Como, Italy (18{u2013}19 April, LNCS 2038) and in 2002 in Kinsale, Ireland (3{u2013}5 April, LNCS 2278). From the outset, there have always been specialized workshops, co-located with EuroGP, focusing on applications of evolutionary algorithms (LNCS 1468, 1596, 1803, 2037, and 2279). This year was no exception and EvoWorkshops 2003, incorporating Evo- BIO, EvoCOP, EvoIASP, EvoMUSART, EvoSTIM and EvoROB, took place at the University of Essex (LNCS 2611). Genetic Programming (GP) is that part of Evolutionary Computation which solves particular complex problems or tasks by evolving and adapting popu- tions of computer programs, using Darwinian evolution and Mendelian genetics as a source of inspiration Evolving Cellular Automata to Grow Microstructures....Pages 1-10 An Innovative Application of a Constrained-Syntax Genetic Programming System to the Problem of Predicting Survival of Patients....Pages 11-21 New Factorial Design Theoretic Crossover Operator for Parametrical Problem....Pages 22-33 Overfitting or Poor Learning: A Critique of Current Financial Applications of GP....Pages 34-46 Evolutionary Design of Objects Using Scene Graphs....Pages 47-58 Ensemble Techniques for Parallel Genetic Programming Based Classifiers....Pages 59-69 Improving Symbolic Regression with Interval Arithmetic and Linear Scaling....Pages 70-82 Evolving Hierarchical and Recursive Teleo-reactive Programs through Genetic Programming....Pages 83-92 Interactive GP for Data Retrieval in Medical Databases....Pages 93-106 Parallel Programs Are More Evolvable than Sequential Programs....Pages 107-118 Genetic Programming with Meta-search: Searching for a Successful Population within the Classification Domain....Pages 119-129 Evolving Finite State Transducers: Some Initial Explorations....Pages 130-141 Reducing Population Size while Maintaining Diversity....Pages 142-152 How Functional Dependency Adapts to Salience Hierarchy in the GAuGE System....Pages 153-163 More on Computational Effort Statistics for Genetic Programming....Pages 164-172 Analysis of a Digit Concatenation Approach to Constant Creation....Pages 173-182 Genetic Programming with Boosting for Ambiguities in Regression Problems....Pages 183-193 Maximum Homologous Crossover for Linear Genetic Programming....Pages 194-203 A Simple but Theoretically-Motivated Method to Control Bloat in Genetic Programming....Pages 204-217 Divide and Conquer: Genetic Programming Based on Multiple Branches Encoding....Pages 218-228 Feature Construction and Selection Using Genetic Programming and a Genetic Algorithm....Pages 229-237 Genetic Programming Applied to Compiler Heuristic Optimization....Pages 238-253 Modularity in Genetic Programming....Pages 254-263 Decreasing the Number of Evaluations in Evolutionary Algorithms by Using a Meta-model of the Fitness Function....Pages 264-275 Assembling Strategies in Extrinsic Evolvable Hardware with Bidirectional Incremental Evolution....Pages 276-285 Neutral Variations Cause Bloat in Linear GP....Pages 286-296 Experimental Design Based Multi-parent Crossover Operator....Pages 297-306 An Enhanced Framework for Microprocessor Test-Program Generation....Pages 307-316 The Effect of Plagues in Genetic Programming: A Study of Variable-Size Populations....Pages 317-326 Multi Niche Parallel GP with a Junk-Code Migration Model....Pages 327-334 Tree Adjoining Grammars, Language Bias, and Genetic Programming....Pages 335-344 Artificial Immune System Programming for Symbolic Regression....Pages 345-353 Grammatical Evolution with Bidirectional Representation....Pages 354-363 Introducing a Perl Genetic Programming System - and Can Meta-evolution Solve the Bloat Problem?....Pages 364-373 Evolutionary Optimized Mold Temperature Control Strategies Using a Multi-polyline Approach....Pages 374-383 Genetic Programming for Attribute Construction in Data Mining....Pages 384-393 Sensible Initialisation in Chorus....Pages 394-403 An Analysis of Diversity of Constants of Genetic Programming....Pages 404-413 Research of a Cellular Automaton Simulating Logic Gates by Evolutionary Algorithms....Pages 414-423 From Implementations to a General Concept of Evolvable Machines....Pages 424-433 Cooperative Evolution on the Intertwined Spirals Problem....Pages 434-442 The Root Causes of Code Growth in Genetic Programming....Pages 443-454 Fitness Distance Correlation in Structural Mutation Genetic Programming....Pages 455-464 Disease Modeling Using Evolved Discriminate Function....Pages 465-474 No Free Lunch, Program Induction and Combinatorial Problems....Pages 475-484

This book constitutes the refereed proceedings of the 6th European Conference on Genetic Programming, EuroGP 2003, held in Essex, UK in April 2003.

The 45 revised papers presented were carefully reviewed and selected from 61 submissions. All current aspects of genetic programming and genetic algorithms are addressed, ranging from foundational, theoretical, and methodological issues to advanced applications in various fields.

This book constitutes the refereed proceedings of the 6th European Conference on Genetic Programming, EuroGP 2003, held in Essex, UK in April 2003. The 45 revised papers presented were carefully reviewed and selected from 61 submissions. All current aspects of genetic programming and genetic algorithms are addressed, ranging from foundational, theoretical, and methodological issues to advanced applications in various fields Classification is an important problem extensively studied in several research areas, such as statistical pattern recognition, machine learning and data mining [Hand 1997].