Genetic Programming Theory and Practice

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

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.

• algorithms
• circuit design
• complex system
• evolutionary algorithm
• genetic programming
• learning
• machine learning
• programming

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)

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

  Rick Riolo

• Genetics Squared, USA

  Bill Worzel

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