Editors:
- Discusses hurdles in solving large-scale applications
- Describes techniques including fitness and age layered populations, code reuse through caching, archives and libraries, Pareto optimization, pre- and post-processing, the use of expert knowledge and information-theoretic fitness measures
- Addresses practical methods for choosing between techniques for improving GP performance and to evolve trustable solutions
Part of the book series: Genetic and Evolutionary Computation (GEVO)
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Table of contents (15 chapters)
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Front Matter
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Back Matter
About this book
Genetic Programming Theory and Practice V was developed from the fifth 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.
The work covers applications of GP to a wide variety of domains, including bioinformatics, symbolic regression for system modeling, financial modeling, circuit design and robot controllers. This volume is a unique and indispensable tool for academics, researchers and industry professionals involved in GP, evolutionary computation, machine learning and artificial intelligence.
Editors and Affiliations
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Center for the Study of Complex Systems, University of Michigan, Ann Arbor
Rick Riolo
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Department of Computer Science, University of Idaho, Moscow
Terence Soule
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Genetics Squared, Ann Arbor
Bill Worzel
Bibliographic Information
Book Title: Genetic Programming Theory and Practice V
Editors: Rick Riolo, Terence Soule, Bill Worzel
Series Title: Genetic and Evolutionary Computation
DOI: https://doi.org/10.1007/978-0-387-76308-8
Publisher: Springer New York, NY
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag US 2008
Hardcover ISBN: 978-0-387-76307-1Published: 11 January 2008
Softcover ISBN: 978-1-4419-4547-1Published: 19 November 2010
eBook ISBN: 978-0-387-76308-8Published: 20 December 2007
Series ISSN: 1932-0167
Series E-ISSN: 1932-0175
Edition Number: 1
Number of Pages: XIV, 279
Topics: Artificial Intelligence, Artificial Intelligence, Theory of Computation, Algorithm Analysis and Problem Complexity, Programming Techniques