Genetic Programming Theory and Practice IV 11-13 May 2006

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 2006):
Yu, Tina, Riolo, Rick, and Bill Worzel, eds. Genetic Programming Theory and Practice III. Vol. XVI. Springer, 2006.

Acknowledgements

The GPTP-2006 Workshop is made possible by generous contributions from:

  • Third Millenium
  • State Street Global Advisors, Boston, MA
  • Christopher T. May, Red Queen Capital Management
  • Biocomputing and Developmental Systems Group, CSIS, University of Limerick
  • Michael Korns, Investment Science Corporation

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 L Riolo and Bill Worzel

Chapter 2. Genome-Wide Genetic Analysis Using Genetic Programming
Jason H. Moore and Bill C. White

Chapter 3. Lifting the Curse of Dimensionality
W.P. Worzel, A. Almal and C.D. MacLean

Chapter 4. GP for Classifiying Cancer Data and Controlling Humaniod Robots
Topan Kumar Paul and Hitoshi Iba

Chapter 5. Boosting Improves Stability and Accuracy of Genetic Programming in Biological Sequence Classification
Pal Saetrom, Olaf Rene Brikeland and Ola Snove Jr.

Chapter 6. Orthogonal Evolution of Teams: A Class of Algorithms for Evolving Teams with Inversely Correlated Errors
Terence Soule and Pavankumarreddy Komireddy

Chapter 7. Multidimensional Tags, Cooperative Populations, and Genetice Programming
Lee Spector and Jon Klein

Chater 8. Coevolving Fitness Models for Accelerating Evolution and Reducing Evaluations
Michael D. Schmidt and Hod Lipson

Chapter 9. Multi-Domain Observations Concerning the Use of Genetic Programming to Automatically Synthesize Human-Competitive Desings for Analog Circuits, Optical Lens Systems, Controllers, Antennas, Mechanical Systems and Quantum Computing Circuits
John R. Koza, Sameer H. Al-Sakran and Lee W. Jones

Chapter 10. Robust Pareto Front Genetic Programming Parameter Selection Based on Desing of Experiments and Industrial Data
Flor Castillo, Arthur Kordon and Guido Smits

Chapter 11. Pursuing the Pareto Paradigm: Tournaments, Algorithm Variations and Ordinal Optimization
Mark Kotanchek, Guido Smits and Ekaterina Vladislavela

Chapter 12. Applying Genetic Programming to Resevior History Matching Problem
Tina Yu, Dave Wilkinson and Alexandre Castellini

Chapter 13. Camparison of Robustness of Three Filter Design Strategies Using Genetic Programming and Bond Graphs
Xiangdong Peng, Erik D. Goodman and Ronald C. Rosenburg

Chapter 14. Design of Posynomial Models for Mosfets: Symbolic Regression Using Genetic Algorithms
Varun Agarwal and Una-May O'Reilly

Chapter 15. Phase Transitions in Genetic Programming Search
Jason M. Daida, Ricky Tang, Michael E. Samples and Matthew J. Byom

Chapter 16. Efficient Markov Chain Model of Machine Code Program Execution and Halting
Riccardo Poli and William B. Langdon

Chapter 17. A Re-examination of a Real World Blood Flow Modeling Problem Using Context-aware Crossover
Hammad Majeed and Conor Ryan

Chapter 18. Large Scale, Time Constrained Symbolic Regression
Michael F. Korns

Chapter 19. Stock Selection: An Innovative Application of Genetic Programming Methodology
Ying L. Becker, Peng Fei and Anna M. Lester