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Genetic Programming Theory and Practice V 17-19 May 2007

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 2007):
Riolo, Rick, Terence Soule, and Bill Worzel, eds. Genetic Programming Theory and Practice IV. Vol. XVI. Springer, 2007.

See the Call for Paper Proposals for information about submitting proposals for papers to presented at this workshop.

The GPTP-2007 Workshop is made possible by generous contributions from:

  • Third Millenium
  • State Street Global Advisors, Boston, MA
  • Michael Korns, Investment Science Corporation
  • Biocomputing and Developmental Systems Group, CSIS, University of Limerick
  • Chris May, Red Queen Capital Managment
  • Genetics Squared, Inc.

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 L Riolo and Bill Worzel

Chapter 2. Better Solutions Faster: Soft Evolution of Robust Regression Models in pareto Genetic Programming
Katya Vladislavela and Guido Smits

Chapter 3. Manipulation of Convergence in Evolutionary Systems
Conor Ryan and Gearoid Murphy

Chapter 4. Large-Scale, Time-Constrained Symbolic Regression-Classification
Michael F. Korns

Chapter 5. Solving Complex Problems in Human Genetics using Genetic Programming
Jason H. Moore and Nate Barney and Bill C. White

Chapter 6. Towards an Information Theoretic Framework for Genetic Programming
Stuart W. Card and Chilukuri K. Mohan

Chapter 7. Investigating Problem hardness of Real Life Applications
Leonardo Vanneschi

Chapter 8. Improving the Scalability of Generative Representations
Gregory S. Hornby

Chapter 9. Program Structure-Fitness Disconnect and Its Impact on Evolution in Genetic Programming
A.A. Almal and C.D. MacLean and W.P. Worzel

Chapter 10. Genetic Programming with Design Reuse for Industrially Scalable, Novel Circuit Design
Trent McConaghy and Pieter Palmers and Georges Gielen and Michiel Steyaert

Chapter 11. Robust Engineering Design of Electronic Circuits with Active Components Using Genetic Programming and Bond Graphs
Xiangdong Peng and Erik D. Goodman and Ronald C. Rosenberg

Chapter 12. Trustable Symoblic Regression Models
Mark Kotanchek and Katya Vladislavleva

Chapter 13. Improving Performance and Cooperation in Multi-Agent Systems
Terence Soule and Robert B. Heckendorn

Chapter 14. An Empirical Study of Multi-Objective Algorithms for Stock Ranking
Ying L. Becker and Harold Fox and Peng Fei

Chapter 15. Using GP and Cultural Algorithms to Simulate the Evolution of an Ancient Urban Center
R.G. Reynolds and Mostafa Ali and Patrick Franzel