© 2008

Genetic Programming Theory and Practice V

  • Rick Riolo
  • Terence Soule
  • Bill Worzel

Part of the Genetic and Evolutionary Computation Series book series (GEVO)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Terence Soule, Rick L. Riolo, Bill Worzel
    Pages 1-12
  3. Ekaterina Vladislavleva, Guido Smits, Mark Kotanchek
    Pages 13-32
  4. Gearoid Murphy, Conor Ryan
    Pages 33-52
  5. Stuart W. Card, Chilukuri K. Mohan
    Pages 87-106
  6. A. A. Almal, C. D. MacLean, W. P. Worzel
    Pages 143-158
  7. Trent McConaghy, Pieter Palmers, Georges Gielen, Michiel Steyaert
    Pages 159-184
  8. Terence Soule, Robert B. Heckendorn
    Pages 221-237
  9. Ying L. Becker, Harold Fox, Peng Fei
    Pages 239-259
  10. Robert G. Reynolds, Mostafa Z. Ali, Patrick Franzel
    Pages 261-276
  11. Back Matter
    Pages 277-279

About this book


Genetic Programming Theory and Practice V was developed from the fifth workshop at the University of Michigan’s Center for the Study of Complex Systems to facilitate the exchange of ideas and information related to the rapidly advancing field of Genetic Programming (GP). Contributions from the foremost international researchers and practitioners in the GP arena examine the similarities and differences between theoretical and empirical results on real-world problems. The text explores the synergy between theory and practice, producing a comprehensive view of the state of the art in GP application.

Specific topics addressed in the book include:

  • the hurdles faced in solving large-scale, cutting edge applications
  • promising techniques, including fitness and age layered populations, code reuse through caching, archives and run transferable libraries, Pareto optimization, and pre- and post-processing
  • the use of information theoretic measures and ensemble techniques
  • approaches to help GP create trustable solutions
  • the use of expert knowledge to guide GP
  • ways to make GP tools more accessible to the non-GP-expert
  • practical methods for understanding and choosing between the recent proliferation of techniques for improving GP performance
  • the potential for GP to undergo radical changes to accommodate the expanded understanding of biological genetics and evolution

The work covers applications of GP to a wide variety of domains, including bioinformatics, symbolic regression for system modeling, financial modeling, circuit design and robot controllers. This volume is a unique and indispensable tool for academics, researchers and industry professionals involved in GP, evolutionary computation, machine learning and artificial intelligence.


Algorithms Multi-agent system artificial intelligence bioinformatics circuit design classification evolution evolutionary computation genetic programming genetics machine learning modeling programming proving robot

Editors and affiliations

  • Rick Riolo
    • 1
  • Terence Soule
    • 2
  • Bill Worzel
    • 3
  1. 1.Center for the Study of Complex SystemsUniversity of MichiganAnn Arbor
  2. 2.Department of Computer ScienceUniversity of IdahoMoscow
  3. 3.Genetics SquaredAnn Arbor

Bibliographic information

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