Springer Genetic Programming Theory and Practice VI (Genetic and Evolutionary Computation)

This book is the result of the sixth workshop on Genetic Programming at the University of Michigan’s Center for the Study of Complex Systems. It explores the synergy between theory and practice in a comprehensive, up-to-date overview of GP application. Review From the reviews: "This book is a collection of contributions from various theorists and practitioners working in the genetic programming (GP) field. … anyone who is interested in the subject should monitor the ongoing research; this volume is an essential assistant for the task. … I found the book interesting … that stimulate the researcher to think of further applications of the presented techniques and methodologies. … I recommend it not only to GP researchers and practitioners, but also to any scientist dealing with optimization problems … ." (Lefteris Angelis, ACM Computing Reviews, March, 2009) From the Back Cover Genetic Programming Theory and Practice VI was developed from the sixth workshop at the University of Michigan's Center for the Study of Complex Systems to facilitate the exchange of ideas and information related to the rapidly advancing field of Genetic Programming (GP). Contributions from the foremost international researchers and practitioners in the GP arena examine the similarities and differences between theoretical and empirical results on real-world problems. The text explores the synergy between theory and practice, producing a comprehensive view of the state of the art in GP application. These contributions address several significant inter-dependent themes which emerged from this year's workshop, including: Making efficient and effective use of test data Sustaining the long term evolvability of our GP systems Exploiting discovered subsolutions for reuse Increasing the role of a Domain Expert In the course of investigating these themes, the chapters describe a variety of techniques in widespread use among practitioners who deal with industrial-scale, real-world problems, such as: Pareto optimization, particularly as a means to limit solution complexity Various types of age-layered populations or niching mechanisms Data partitioning, a priori or adaptively, e.g., via co-evolution Cluster computing or general purpose graphics processors for parallel computing Ensemble/team solutions This work covers applications of GP to a host of domains, including bioinformatics, symbolic regression for system modeling in various settings, circuit design, and financial modeling to support portfolio management. This volume is a unique and indispensable tool for academics, researchers and industry professionals involved in GP, evolutionary computation, machine learning and artificial intelligence.