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Genetic Programming

8th European Conference, EuroGP 2005, Lausanne, Switzerland, March 30-April 1, 2005, Proceedings

  • Conference proceedings
  • © 2005

Overview

Editors:
  1. Maarten Keijzer

    1. No Affiliation,  

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  2. Andrea Tettamanzi

    1. Dipartimento di Tecnologie dell’Informazione, Università di Milano Crema,  

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  3. Pierre Collet

    1. Université Louis Pasteur, LSIIT, FDBT, Pôle API, Illkirch, France

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  4. Jano Hemert

    1. National e-Science Centre, University of Edinburgh, United Kingdom

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  5. Marco Tomassini

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

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 3447)

Part of the book sub series: Theoretical Computer Science and General Issues (LNTCS)

Included in the following conference series:

Conference proceedings info: EuroGP 2005.

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About this book

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.

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Table of contents (34 papers)

  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
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Other volumes

  1. Genetic Programming

Editors and Affiliations

  • 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

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