This workshop focuses on how theory can inform practice and what practice reveals about theory. The goal is 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 will be a small, invitation-only workshop on the campus of the University of Michigan in Ann Arbor. The workshop format is informal with plenty of time for discussion.
The papers from the workshop will be published as chapters in a book published by Springer (December 2009).
The GPTP-2009 Workshop is made possible by generous contributions from:
- State Street Global Advisors, Boston, MA
- Third Millenium
- Michael Korns, Investment Science Corporation
- Biocomputing and Developmental Systems Group, CSIS, University of Limerick
- Evolved Analytics
- Vague Innovation LLC
and the Center for the Study of Complex Systems at the University of Michigan.
Please thank them for making this workshop possible.
Please also visit the list of all GPTP workshops.
Workshop Talk / Book Chapters
Chapter 1. Genetic Programming: Theory and Practice
Una-May O'Reilly, Trent McConaghy, and Rick Riolo
Chapter 2. Environmental Sensing of Expert Knowledge
Casey S. Greene, Douglas P. Hill, and Jason H. Moore
Chapter 3. On the significance of binary versus real valued interaction functions in competitive-cooperative models of reinforcement learning
John Doucette, Peter Lichodzijewski, and Malcolm Heywood
Chapter 4. Weighting, compressing, and balancing multi-dimensional input-output data
Mark E. Kotanchek, Ekaterina J. Vladislavleva, and Guido F. Smits
Chapter 5. Symbolic regression of implicit equations
Michael Schmidt and Hod Lipson
Chapter 6. Steady-State ALPS
Gregory S. Hornby
Chapter 7. Latent Variable Symbolic Regression for High-Dimensional Inputs
Trent McConaghy, Pieter Palmers, Michiel Steyaert, and Georges Gielen
Chapter 8. Algorithmic Trading with Developmental and Linear Genetic Programming
Garnet Wilson and Wolfgang Banzhaf
Chapter 9. High-significance Averages of Event-Related Potential via GP
Luca Citi, Riccardo Poli, and Caterina Cinel
Chapter 10. Multi-objective Genetic Programming and Stochastic Processes
Brian Ross and Janine Imada
Chapter 11. Graph Structured Program Evolution: Evolution of Loop Structures
Shinichi Shirakawa and Tomoharu Nagao
Chapter 12. A Functional Crossover Operator for Genetic Programming
Chapter 13. Symbolic Regression of Conditional Target Expressions