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Genetic Programming Theory and Practice

  • Book
  • © 2003

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Editors:
  1. Rick Riolo

    1. Center for the Study of Complex Systems, University of Michigan, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Bill Worzel

    1. Genetics Squared, USA

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Genetic Programming (GPEM, volume 6)

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Table of contents (19 chapters)

  1. Front Matter

    Pages i-xvi
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  2. Genetic Programming: Theory and Practice

    • Bill Worzel, Rick Riolo
    Pages 1-10

  3. An Essay Concerning Human Understanding of Genetic Programming

    • Lee Spector
    Pages 11-23

  4. Classification of Gene Expression Data with Genetic Programming

    • Joseph A. Driscoll, Bill Worzel, Duncan MacLean
    Pages 25-42

  5. Artificial Regulatory Networks and Genetic Programming

    • W. Banzhaf
    Pages 43-61

  6. Using Software Engineering Knowledge to Drive Genetic Program Design Using Cultural Algorithms

    • David A. Ostrowski, Robert G. Reynolds
    Pages 63-80

  7. Continuous Hierarchical Fair Competition Model for Sustainable Innovation in Genetic Programming

    • Jianjun Hu, Erik D. Goodman, Kisung Seo
    Pages 81-98

  8. What Makes a Problem GP-Hard?

    • Jason M. Daida
    Pages 99-118

  9. A Probabilistic Model of Size Drift

    • Justinian Rosca
    Pages 119-135

  10. Building-Block Supply in Genetic Programming

    • Kumara Sastry, Una-May O’Reilly, David E. Goldberg, David Hill
    Pages 137-154

  11. Modularization by Multi-Run Frequency Driven Subtree Encapsulation

    • Daniel Howard
    Pages 155-171

  12. The Distribution of Reversible Functions is Normal

    • W. B. Langdon
    Pages 173-187

  13. Doing Genetic Algorithms the Genetic Programming Way

    • Conor Ryan, Miguel Nicolau
    Pages 189-204

  14. Probabilistic Model Building and Competent Genetic Programming

    • Kumara Sastry, David E. Goldberg
    Pages 205-220

  15. Automated Synthesis by Means of Genetic Programming of Complex Structures Incorporating Reuse, Parameterized Reuse, Hierarchies, and Development

    • John R. Koza, Matthew J. Streeter, Martin A. Keane
    Pages 221-237

  16. Industrial Strength Genetic Programming

    • Mark Kotanchek, Guido Smits, Arthur Kordon
    Pages 239-255

  17. Operator Choice and the Evolution of Robust Solutions

    • Terence Soule
    Pages 257-269

  18. A Hybrid GP-Fuzzy Approach for Resevoir Characterization

    • Tina Yu, Dave Wilkinson, Deyi Xie
    Pages 271-289

  19. Enhanced Emerging Market Stock Selection

    • Anjun Zhou
    Pages 291-302

  20. Three Fundamentals of the Biological Genetic Algorithm

    • Stephen Freeland
    Pages 303-311
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Keywords

About this book

Genetic Programming Theory and Practice explores the emerging interaction between theory and practice in the cutting-edge, machine learning method of Genetic Programming (GP). The material contained in this contributed volume was developed from a workshop at the University of Michigan's Center for the Study of Complex Systems where an international group of genetic programming theorists and practitioners met to examine how GP theory informs practice and how GP practice impacts GP theory. The contributions cover the full spectrum of this relationship and are written by leading GP theorists from major universities, as well as active practitioners from leading industries and businesses. Chapters include such topics as John Koza's development of human-competitive electronic circuit designs; David Goldberg's application of "competent GA" methodology to GP; Jason Daida's discovery of a new set of factors underlying the dynamics of GP starting from applied research; and Stephen Freeland's essay on the lessons of biology for GP and the potential impact of GP on evolutionary theory.

Reviews

From the reviews:

"This book contains 19 chapters that are based on respective research papers presented at the workshop ‘Genetic Program, Theory and Practice’. … The balanced combination of theoretical issues and application issues, along with the many new and interesting results described by the contributors make this book a useful source of up-to-date information on GP. The book will no doubt inspire new and old researchers and practitioners to push further the frontiers of GP theory and practice for the benefit of the international society." (Spyros Tzafestas, Journal of Intelligent and Robotic Systems, Vol. 45, 2006)

Editors and Affiliations

  • Center for the Study of Complex Systems, University of Michigan, USA

    Rick Riolo

  • Genetics Squared, USA

    Bill Worzel

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