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Genetic Programming Theory Practice, 15-17 May, 2003

Genetic Programming Theory Practice 2003 Workshop (GPTP-2003)

From 15-17 May, 2003, the Center for the Study of Complex Systems (CSCS) at the University of Michigan hosted an invitation-only workshop, Genetic Programming Theory and Practice.

This workshop focused on how theory can inform practice and what practice reveals about theory. The goal was to evaluate the state-of-the-art in genetic programming by discussing different theories and their value to practitioners of the art and to review problems and observations from practice that challenge existing theory.

This was a small, invitation-only workshop on the campus of the University of Michigan in Ann Arbor. The workshop format was informal with plenty of time for discussion. John Holland gave a keynote address, and John Koza presented the lead-off talk. Other keynote speakers included Stephen Freeland, (Dept of Biology, Univ of Maryland), and Lynne Ellyn (VP and CIO of DTE Energy). Below is the list of all papers presented at the Workshop, along with their authors and affiliations.

The papers below are being published as chapters in the following book:

Genetic Programming Theory and Practice
Rick Riolo and Bill Worzel (eds.)
Kluwer Publishers, Boston, MA. 2003


We would like to gratefully acknowledge the contributions made by the following organization which made this Workshop possible. We would also like to acknowledge the support of The Center for the Study of Complex Systems (CSCS) and its director, Carl Simon.

Christopher T. May, RedQueen Capital Management
DTE Energy Foundation, Michigan
State Street Global Advisors, Boston, MA


Workshop Talk Titles, Authors and Schedule

Thursday, 2003 May 15

Welcome and opening remarks.
Carl Simon
Director, Center for the Study of Complex Systems
Professor, Economics, Mathematics and School of Public Policy

Keynote: GA and GP, Past and Future: What do they share besides the G?
John Holland
Psychology, CSCS, University of Michigan.

Morning Session:

T2. The distribution of reversible functions is normal.
Bill Langdon.
University College, London, UK
A6. Automated Synthesis by Means of Genetic Programming of Complex Structures Incorporating Reuse, Hierarchies, Development, and Parameterized Toplogies.
John R. Koza (Stanford University, Stanford, California), Matthew J. Streeter (Genetic Programming Inc., Mountain View, CA) and Martin A. Keane (Econometrics Inc., Chicago, Illinois)

Afternoon Session #1:

T8. Doing Genetic Algorithms the Genetic Programming Way.
Conor Ryan.
University of Limerick, Ireland
A7. Modularization by Multi-Run Frequency Driven Subtree Encapsulation.
Daniel Howard.
Software Evolution Centre, Malvern, UK

Afternoon Session #2:

T4. What Makes a Problem GP-Hard? A Look at How Structure Affects Content.
Jason M. Daida.
The University of Michigan, Ann Arbor, MI
A8. Using Software Engineering Knowledge to Drive Genetic Programming Design Using Cultural Algorithms.
David Ostrowski (Ford Motor Company Scientific Research Laboratories, Dearborn, MI), Robert G. Reynolds (Wayne State University, Detroit, MI).

Friday, 2003 May 16

Keynote: Stephen Freeland
Biological Sciences, Univ. of Maryland, BC.

Morning Session:

T1. Artificial Regulatory Networks and Genetic Programming.
Wolfgang Banzhaf.
University of Dortmund, Dortmund
A2. The Continuous Hierarchical Fair Competition Model for Sustainable Innovation in Genetic Programming.
Erik D. Goodman, Jianjun Hu.
Genetic Algorithm Research & Application Group (GARAGe) Michigan State University, East Lansing, MI

Afternoon Session #1:

T9. An Essay Concerning Human Understanding of Genetic Programming.
Lee Spector.
Cognitive Science
Hampshire College, Amherst, MA
A3. Classification of Gene Expression Data with Genetic Programming.
Joseph A. Driscoll (Middle Tennessee State University, Murfreesburo, TN), Bill Worzel & Duncan MacLean (Genetics Squared, Inc., Milan, MI).

Afternoon Session #2:

T5. Probabilistic Model Building and Competent Genetic Programming.
Kumara Sastry, David E. Goldberg.
Illinois Genetic Algorithms Laboratory (IlliGAL), University of Illinois, Urbana, IL
A4. Industrial Strength Genetic Programming Empirical Modeling and Symbolic Regression via GP Integrated Methodologies, Best Practices, Lessons Learned.
Mark Kotanchek, Guido Smits, and Arthur Kordon.
Dow Chemical

Saturday, 2003 May 17

Keynote: Lynn Ellyn VP, CIO, DTEnergy

Morning Session:

T6. Operator Choice and the Evolution of Robust Solutions.
Terry Soule.
University of Idaho
A5. Hybrid GP-Fuzzy Approach for Reservoir Characterization With a Gentle Introduction to Oil Exploration and Production.
Tina (Gwoing) Yu, Davie Wilkinson, Deyi Xei.
ChevronTexaco Information Technology Company & ChevronTexaco Exploration and Production Technology Company, San Ramon, CA

Afternoon Session #1:

T3. Building Block Supply in Genetic Programming.
Kumara Sastry (Dept. of Material Sc. & Eng., University of Illinois, Urbana, IL), Una-May O'Reilly (Artificial Intelligence Lab, Massachussetts Institute of Technology, MA), David E. Goldberg (Dept. of General Engineering, University of Illinois, Urbana, IL)
A1. Enhance Emerging Market Stock Selection: A Genetic Programming Approach.
Anjun Zhou (State Street Global Advisors).

Afternoon Session #2:

T10. A Probabilistic Model of Size Drift.
Justinian Rosca.

Wrap-Up Discussion