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 were published as chapters in a book published by Springer (November 2008):
Riolo, Rick, Terence Soule, and Bill Worzel, eds. Genetic Programming Theory and Practice VI. Vol. XIV. Springer, 2009.
The GPTP-2008 Workshop is made possible by generous contributions from:
- Third Millenium
- State Street Global Advisors, Boston, MA
- Biocomputing and Developmental Systems Group, CSIS, University of Limerick
- Michael Korns, Investment Science Corporation
- 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
Terence Soule, Rick Riolo and Una-May O'Reilly
Chapter 2. A Population Based Study Of Evolutionary Dynamics In Genetic Programming
Bill Worzel, A.A. Almal, C.D. MacLean
Chapter 3. An Application of Information Theoretic Selection of Continuous Valued GP Inputs
Stu Card and Chilukuri K. Mohan
Chapter 4. Pareto Cooperative-Competitive Genetic Programming
Malcolm Heywood, Andrew McIntyre
Chapter 5. Genetic Programming with Historically Asssessed Hardness
Jon Klein, Lee Spector
Chapter 6. Crossover and sampling biases on nearly uniform landscapes
Chapter 7. Analysis of Effects of Elitism on Bloat in Linear and Tree-based Genetic Programming
Riccardo Poli, Nicholas McPhee, Leonardo Vanneschi
Chapter 8. Automated Extraction of Expert Domain Knowledge from Synthesis Results
Chapter 9. Does complexity matter? Artificial evolution, computational evolution and the genetic analysis of epistasis in common human diseases
Jason H. Moore, Bill White
Chapter 10. Targeted Data Collection using ParetoGP
Mark Kotanchek, Guido Smits, Katya Vladislavleva
Chapter 11. Evolving Effective Incremental Solvers for SAT with a Hyper-Heuristic Framework Based on Genetic Programming
Mohamed Bader-El-Den, Riccardo Poli
Chapter 12. Constrained Genetic Programming to Minimize Overfitting in Stock Selection
Minkyu Kim, Ying Becker, Peng Fei, Una-May O'reilly
Chapter 13. Co-Evolving Trading Strategies to Analyze Bounded Rationality in Double Auction Markets
Shu-Heng Chen, Ren-Jie Zeng, Tina Yu
Chapter 14. Profiling Symbolic Regression-Classification
Michael Korns, Loryfel Nunez
Chapter 15. GP on Graphics Processing Units
Wolfgang Banzhaf, S. Harding, W.B. Langdon, G. Wilson
Chapter 16. Genetic Programming of Game Characters: Using Genetic Programming to Model the Emergence of Cultural Systems within a Virtual World