The Center for the Study of Complex Systems (CSCS) at the University of Michigan hosted an invitation-only workhop:
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).
Please also visit the list of all GPTP workshops.
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 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