Una-May O’Reilly & Tina Yu 
Genetic Programming Theory and Practice II 

The work described in this book was first presented at the Second Workshop on Genetic Programming, Theory and Practice, organized by the Center for the Study of Complex Systems at the University of Michigan, Ann Arbor, 13-15 May 2004. The goal of this workshop series is to promote the exchange of research results and ideas between those who focus on Genetic Programming (GP) theory and those who focus on the application of GP to various re- world problems. In order to facilitate these interactions, the number of talks and participants was small and the time for discussion was large. Further, participants were asked to review each other’s chapters before the workshop. Those reviewer comments, as well as discussion at the workshop, are reflected in the chapters presented in this book. Additional information about the workshop, addendums to chapters, and a site for continuing discussions by participants and by others can be found at http://cscs.umich.edu:8000/GPTP-20041. We thank all the workshop participants for making the workshop an exciting and productive three days. In particular we thank all the authors, without whose hard work and creative talents, neither the workshop nor the book would be possible. We also thank our keynote speakers Lawrence (‘Dave’) Davis of Nu Tech Solutions, Inc., Jordan Pollack of Brandeis University, and Richard Lenski of Michigan State University, who delivered three thought-provoking speeches that inspired a great deal of discussion among the participants.

Table of Content

Genetic Programming: Theory and Practice.- Discovering Financial Technical Trading Rules Using Genetic Programming with Lambda Abstraction.- Using Genetic Programming in Industrial Statistical Model Building.- Population Sizing for Genetic Programming Based on Decision-Making.- Considering the Roles of Structure in Problem Solving by Computer.- Lessons Learned Using Genetic Programming in a Stock Picking Context.- Favourable Biasing of Function Sets Using Run Transferable Libraries.- Toward Automated Design of Industrial-Strength Analog Circuits by Means of Genetic Programming.- Topological Synthesis of Robust Dynamic Systems by Sustainable Genetic Programming.- Does Genetic Programming Inherently Adopt Structured Design Techniques?.- Genetic Programming of an Algorithmic Chemistry.- ACGP: Adaptable Constrained Genetic Programming.- Using Genetic Programming to Search for Supply Chain Reordering Policies.- Cartesian Genetic Programming and the Post Docking Filtering Problem.- Listening to Data: Tuning a Genetic Programming System.- Incident Detection on Highways.- Pareto-Front Exploitation in Symbolic Regression.- An Evolved Antenna for Deployment on Nasa’s Space Technology 5 Mission.