Genetic Programming

1. Front Matter

   Pages I-XII

   PDF

2. Talks

   1. Evolving Cellular Automata to Grow Microstructures

      • David Basanta, Peter J. Bentley, Mark A. Miodownik, Elizabeth A. Holm

      Pages 1-10

   2. An Innovative Application of a Constrained-Syntax Genetic Programming System to the Problem of Predicting Survival of Patients

      • Celia C. Bojarczuk, Heitor S. Lopes, Alex A. Freitas

      Pages 11-21

   3. New Factorial Design Theoretic Crossover Operator for Parametrical Problem

      • Kit Yan Chan, M. Emin Aydin, Terence C. Fogarty

      Pages 22-33

   4. Overfitting or Poor Learning: A Critique of Current Financial Applications of GP

      • Shu-Heng Chen, Tzu-Wen Kuo

      Pages 34-46

   5. Evolutionary Design of Objects Using Scene Graphs

      • Marc Ebner

      Pages 47-58

   6. Ensemble Techniques for Parallel Genetic Programming Based Classifiers

      • Gianluigi Folino, Clara Pizzuti, Giandomenico Spezzano

      Pages 59-69

   7. Improving Symbolic Regression with Interval Arithmetic and Linear Scaling

      • Maarten Keijzer

      Pages 70-82

   8. Evolving Hierarchical and Recursive Teleo-reactive Programs through Genetic Programming

      • Mykel J. Kochenderfer

      Pages 83-92

   9. Interactive GP for Data Retrieval in Medical Databases

      • Yann Landrin-Schweitzer, Pierre Collet, Evelyne Lutton

      Pages 93-106

   10. Parallel Programs Are More Evolvable than Sequential Programs

       • Kwong Sak Leung, Kin Hong Lee, Sin Man Cheang

       Pages 107-118

   11. Genetic Programming with Meta-search: Searching for a Successful Population within the Classification Domain

       • Thomas Loveard

       Pages 119-129

   12. Evolving Finite State Transducers: Some Initial Explorations

       • Simon M. Lucas

       Pages 130-141

   13. Reducing Population Size while Maintaining Diversity

       • Patrick Monsieurs, Eddy Flerackers

       Pages 142-152

   14. How Functional Dependency Adapts to Salience Hierarchy in the GAuGE System

       • Miguel Nicolau, Conor Ryan

       Pages 153-163

   15. More on Computational Effort Statistics for Genetic Programming

       • Jens Niehaus, Wolfgang Banzhaf

       Pages 164-172

   16. Analysis of a Digit Concatenation Approach to Constant Creation

       • Michael O’Neill, Ian Dempsey, Anthony Brabazon, Conor Ryan

       Pages 173-182

   17. Genetic Programming with Boosting for Ambiguities in Regression Problems

       • Grégory Paris, Denis Robilliard, Cyril Fonlupt

       Pages 183-193

   18. Maximum Homologous Crossover for Linear Genetic Programming

       • Michael Defoin Platel, Manuel Clergue, Philippe Collard

       Pages 194-203

   19. A Simple but Theoretically-Motivated Method to Control Bloat in Genetic Programming

       • Riccardo Poli

       Pages 204-217

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 (14–15 April, LNCS 1391). Sub- quently the workshop was held in G¨ oteborg, Sweden in 1999 (26–27 May, LNCS 1598) and then EuroGP became an annual conference: in 2000 in Edinburgh, UK (15–16 April, LNCS 1802), in 2001 in Lake Como, Italy (18–19 April, LNCS 2038) and in 2002 in Kinsale, Ireland (3–5 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.

• Ensembl
• Perl
• Syntax
• adaptive systems
• automata
• cellular automata
• cellular automaton
• databases
• evolutionary computation
• evolutionary programming
• evolvable systems
• genetic algorithms
• genetic programming
• logic
• neural computation
• algorithm analysis and problem complexity

• Computer Science and Information Systems, University of Limerick, Limerick, Ireland

  Conor Ryan

• Department of Computer Science, University of Idaho, Moscow, USA

  Terence Soule

• Department of Computer Science, Free University of Amsterdam, HV Amsterdam, The Netherlands

  Maarten Keijzer

• Department of Computer Science, University of Essex, Colchester, UK

  Edward Tsang, Riccardo Poli

• Department of Computer Science, University of Coimbra, Coimbra, Portugal

  Ernesto Costa

Policies and ethics