Genetic Programming

1. Front Matter

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2. Talks

   1. An Algorithmic Chemistry for Genetic Programming

      • Christian W. G. Lasarczyk, Wolfgang Banzhaf

      Pages 1-12

   2. Assessing the Effectiveness of Incorporating Knowledge in an Evolutionary Concept Learner

      • Federico Divina

      Pages 13-24

   3. Automated Re-invention of a Previously Patented Optical Lens System Using Genetic Programming

      • Sameer H. Al-Sakran, John R. Koza, Lee W. Jones

      Pages 25-37

   4. Bayesian Automatic Programming

      • Evandro Nunes Regolin, Aurora Trindad Ramirez Pozo

      Pages 38-49

   5. Dynamic Size Populations in Distributed Genetic Programming

      • Denis Rochat, Marco Tomassini, Leonardo Vanneschi

      Pages 50-61

   6. Evolution of Robot Controller Using Cartesian Genetic Programming

      • Simon Harding, Julian F. Miller

      Pages 62-73

   7. Evolving L-Systems to Capture Protein Structure Native Conformations

      • Gabi Escuela, Gabriela Ochoa, Natalio Krasnogor

      Pages 74-84

   8. Evolving Rules for Document Classification

      • Laurence Hirsch, Masoud Saeedi, Robin Hirsch

      Pages 85-95

   9. Genetic Programming in Wireless Sensor Networks

      • Derek M. Johnson, Ankur M. Teredesai, Robert T. Saltarelli

      Pages 96-107

   10. Genetic Transposition in Tree-Adjoining Grammar Guided Genetic Programming: The Duplication Operator

       • Nguyen Xuan Hoai, Robert Ian Bob McKay, Daryl Essam, Hoang Tuan Hao

       Pages 108-119

   11. GP-EndChess: Using Genetic Programming to Evolve Chess Endgame Players

       • Ami Hauptman, Moshe Sipper

       Pages 120-131

   12. GP-Gammon: Using Genetic Programming to Evolve Backgammon Players

       • Yaniv Azaria, Moshe Sipper

       Pages 132-142

   13. GP-Robocode: Using Genetic Programming to Evolve Robocode Players

       • Yehonatan Shichel, Eran Ziserman, Moshe Sipper

       Pages 143-154

   14. Incorporating Learning Probabilistic Context-Sensitive Grammar in Genetic Programming for Efficient Evolution and Adaptation of Snakebot

       • Ivan Tanev

       Pages 155-166

   15. Multi-logic-Unit Processor: A Combinational Logic Circuit Evaluation Engine for Genetic Parallel Programming

       • Wai Shing Lau, Gang Li, Kin Hong Lee, Kwong Sak Leung, Sin Man Cheang

       Pages 167-177

   16. Operator-Based Distance for Genetic Programming: Subtree Crossover Distance

       • Steven Gustafson, Leonardo Vanneschi

       Pages 178-189

   17. Repeated Patterns in Tree Genetic Programming

       • W. B. Langdon, W. Banzhaf

       Pages 190-202

   18. Tarpeian Bloat Control and Generalization Accuracy

       • Sébastien Mahler, Denis Robilliard, Cyril Fonlupt

       Pages 203-214

   19. The Tree-String Problem: An Artificial Domain for Structure and Content Search

       • Steven Gustafson, Edmund K. Burke, Natalio Krasnogor

       Pages 215-226

In this volume we present the contributions for the 18th European Conference on Genetic Programming (EuroGP 2005). The conference took place from 30 March to 1 April in Lausanne, Switzerland. EuroGP is a well-established conf- ence and the only one exclusively devoted to genetic programming. All previous proceedings were published by Springer in the LNCS series. From the outset, EuroGP has been co-located with the EvoWorkshops focusing on applications of evolutionary computation. Since 2004, EvoCOP, the conference on evolutionary combinatorial optimization, has also been co-located with EuroGP, making this year’s combined events one of the largest dedicated to evolutionary computation in Europe. Genetic programming (GP) is evolutionary computation that solves complex problems or tasks by evolving and adapting a population of computer programs, using Darwinian evolution and Mendelian genetics as its sources of inspiration. Some of the 34 papers included in these proceedings address foundational and theoretical issues and there is also a wide variety of papers dealing with di?erent application areas, such as computer science, engineering, language processing, biology and computational design, demonstrating that GP is a powerful and practical problem-solving paradigm.

• adaptive systems
• algorithm
• algorithms
• cellular automata
• evolutionary computation
• evolutionary programming
• evolvable systems
• genetic algorithms
• genetic programming
• heuristic search
• learning
• logic
• natural computing
• optimization
• programming
• algorithm analysis and problem complexity

• No Affiliation,  

  Maarten Keijzer

• Dipartimento di Tecnologie dell’Informazione, Università di Milano Crema,  

  Andrea Tettamanzi

• Université Louis Pasteur, LSIIT, FDBT, Pôle API, Illkirch, France

  Pierre Collet

• National e-Science Centre, University of Edinburgh, United Kingdom

  Jano Hemert

• Institut des Systèmes d’Information (ISI), Université de Lausanne, Hautes Etudes Commerciales (HEC),  

  Marco Tomassini

Policies and ethics