This volume explores the emerging interaction between theory and practice in the cutting-edge, machine learning method of Genetic Programming (GP). The contributions developed from a second workshop at the University of Michigan's Center for the Study of Complex Systems where leading international genetic programming theorists from major universities and active practitioners from leading industries and businesses met to examine how GP theory informs practice and how GP practice impacts GP theory. Chapters include such topics as financial trading rules, industrial statistical model building, population sizing, the roles of structure in problem solving by computer, sk picking, automated design of industrial-strength analog circuits, topological synthesis of robust systems, algorithmic chemistry, supply chain reordering policies, post docking filtering, an evolved antenna for a NASA mission and incident detection on highways.
Table of Contents
Genetic Programming Theory and Practice.- Discovering Financial Technical Trading Rules Using.- Abstraction GP.- Using Genetic Programming in Industrial Statistical Model Building Population Sizing for Genetic Programming.- Considering the Roles of Structure in Problem Solving by Computer.- Lessons Learned using Genetic Programming in a Sk Picking Context Favourable Biasing of Function Sets.- Toward Automated Design of Industrial-Strength Analog Circuits by Means of Genetic Programming.- Topological Synthesis of Robust Systems.- Does Genetic Programming Inherently Adopt Structured DesignTechniques?- Genetic Programming of an Algorithmic Chemistry.- ACGP: Adaptable Constrained Genetic Programming.- Searching 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 a NASA Mission.