Genetic Programming Theory and Practice II

1. Front Matter

   Pages i-xv

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2. Genetic Programming: Theory and Practice

   • Una-May O’Reilly, Tina Yu, Rick Riolo, Bill Worzel

   Pages 1-10

3. Discovering Financial Technical Trading Rules Using Genetic Programming with Lambda Abstraction

   • Tina Yu, Shu-Heng Chen, Tzu-Wen Kuo

   Pages 11-30

4. Using Genetic Programming in Industrial Statistical Model Building

   • Flor Castillo, Arthur Kordon, Jeff Sweeney, Wayne Zirk

   Pages 31-48

5. Population Sizing for Genetic Programming Based on Decision-Making

   • Kumara Sastry, Una-May O’Reilly, David E. Goldberg

   Pages 49-65

6. Considering the Roles of Structure in Problem Solving by Computer

   • Jason M. Daida

   Pages 67-86

7. Lessons Learned Using Genetic Programming in a Stock Picking Context

   • Michael Caplan, Ying Becker

   Pages 87-102

8. Favourable Biasing of Function Sets Using Run Transferable Libraries

   • Conor Ryan, Maarten Keijzer, Mike Cattolico

   Pages 103-120

9. Toward Automated Design of Industrial-Strength Analog Circuits by Means of Genetic Programming

   • John R. Koza, Lee W. Jones, Martin A. Keane, Matthew J. Streeter, Sameer H. Al-Sakran

   Pages 121-142

10. Topological Synthesis of Robust Dynamic Systems by Sustainable Genetic Programming

    • Jianjun Hu, Erik Goodman

    Pages 143-157

11. Does Genetic Programming Inherently Adopt Structured Design Techniques?

    • John M. Hall, Terence Soule

    Pages 159-174

12. Genetic Programming of an Algorithmic Chemistry

    • W. Banzhaf, C. Lasarczyk

    Pages 175-190

13. ACGP: Adaptable Constrained Genetic Programming

    • Cezary Z. Janikow

    Pages 191-206

14. Using Genetic Programming to Search for Supply Chain Reordering Policies

    • Scott A. Moore, Kurt DeMaagd

    Pages 207-223

15. Cartesian Genetic Programming and the Post Docking Filtering Problem

    • A. Beatriz Garmendia-Doval, Julian F. Miller, S. David Morley

    Pages 225-244

16. Listening to Data: Tuning a Genetic Programming System

    • Duncan MacLean, Eric A. Wollesen, Bill Worzel

    Pages 245-262

17. Incident Detection on Highways

    • Daniel Howard, Simon C. Roberts

    Pages 263-282

18. Pareto-Front Exploitation in Symbolic Regression

    • Guido F. Smits, Mark Kotanchek

    Pages 283-299

19. An Evolved Antenna for Deployment on Nasa’s Space Technology 5 Mission

    • Jason D. Lohn, Gregory S. Hornby, Derek S. Linden

    Pages 301-315

20. Back Matter

    Pages 317-320

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The work described in this book was first presented at the Second Workshop on Genetic Programming, Theory and Practice, organized by the Center for the Study of Complex Systems at the University of Michigan, Ann Arbor, 13-15 May 2004. The goal of this workshop series is to promote the exchange of research results and ideas between those who focus on Genetic Programming (GP) theory and those who focus on the application of GP to various re- world problems. In order to facilitate these interactions, the number of talks and participants was small and the time for discussion was large. Further, participants were asked to review each other's chapters before the workshop. Those reviewer comments, as well as discussion at the workshop, are reflected in the chapters presented in this book. Additional information about the workshop, addendums to chapters, and a site for continuing discussions by participants and by others can be found at http://cscs.umich.edu:8000/GPTP-20041. We thank all the workshop participants for making the workshop an exciting and productive three days. In particular we thank all the authors, without whose hard work and creative talents, neither the workshop nor the book would be possible. We also thank our keynote speakers Lawrence ("Dave") Davis of NuTech Solutions, Inc., Jordan Pollack of Brandeis University, and Richard Lenski of Michigan State University, who delivered three thought-provoking speeches that inspired a great deal of discussion among the participants.

• Algorithms
• Automat
• algorithm
• computer
• genetic programming
• learning
• machine learning
• programming
• algorithm analysis and problem complexity

• Massachusetts Institute of Technology, USA

  Una-May O’Reilly

• Chevron Texaco Information Technology Group, Chevron

  Tina Yu

• University of Michigan

  Rick Riolo

• Genetics Squared, Inc., Genetics

  Bill Worzel

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