Overview
- Presents large-scale, real-world applications of GP
- Addresses a variety of problem domains that respond to GP solutions
- Written by leading researchers and practitioners in the field
- Includes supplementary material: sn.pub/extras
Part of the book series: Genetic and Evolutionary Computation (GEVO, volume 8)
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Table of contents (14 chapters)
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Front Matter
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Back Matter
Keywords
About this book
The contributions in this volume are written by the foremost international researchers and practitioners in the GP arena. They 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.
Topics include: FINCH: A System for Evolving Java, Practical Autoconstructive Evolution, The Rubik Cube and GP Temporal Sequence Learning, Ensemble classifiers: AdaBoost and Orthogonal Evolution of Teams, Self-modifying Cartesian GP, Abstract Expression Grammar Symbolic Regression, Age-Fitness Pareto Optimization, Scalable Symbolic Regression by Continuous Evolution, Symbolic Density Models, GP Transforms in Linear Regression Situations, Protein Interactions in a Computational Evolution System, Composition of Music and Financial Strategies via GP, and Evolutionary Art Using Summed Multi-Objective Ranks.
Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results in GP .
Reviews
From the reviews:
“The book consists of 14 papers and an introduction. The applications it considers clearly demonstrate the maturity of GP techniques, and their ability to efficiently address difficult problem instances. … A specialized audience of experts in genetic algorithms will find state-of-the-art applications and methodologies in this book. It will also be of interest to practitioners for the large number of applications discussed, and to advanced students and researchers for the numerous opportunities for investigation and thesis topics.” (Renato De Leone, ACM Computing Reviews, July, 2011)
Editors and Affiliations
Bibliographic Information
Book Title: Genetic Programming Theory and Practice VIII
Editors: Rick Riolo, Trent McConaghy, Ekaterina Vladislavleva
Series Title: Genetic and Evolutionary Computation
DOI: https://doi.org/10.1007/978-1-4419-7747-2
Publisher: Springer New York, NY
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Science+Business Media, LLC 2011
Hardcover ISBN: 978-1-4419-7746-5Published: 28 October 2010
Softcover ISBN: 978-1-4614-2719-3Published: 01 December 2012
eBook ISBN: 978-1-4419-7747-2Published: 20 October 2010
Series ISSN: 1932-0167
Series E-ISSN: 1932-0175
Edition Number: 1
Number of Pages: XXVIII, 248
Topics: Artificial Intelligence, Artificial Intelligence, Theory of Computation, Algorithm Analysis and Problem Complexity, Programming Techniques