Overview
- Provides papers describing cutting-edge work on the theory and applications of genetic programming (GP)
- Offers large-scale, real-world applications (big data) of GP to a variety of problem domains, including commercial and scientific applications as well as financial and insurance problems
- Explores controlled semantics, lexicase and other selection methods, crossover techniques, diversity analysis and understanding of convergence tendencies
Part of the book series: Genetic and Evolutionary Computation (GEVO)
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Table of contents (11 chapters)
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Front Matter
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Back Matter
Keywords
About this book
These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Topics in this volume include: evolving developmental programs for neural networks solving multiple problems, tangled program, transfer learning and outlier detection using GP, program search for machine learning pipelines in reinforcement learning, automatic programming with GP, new variants of GP, like SignalGP, variants of lexicase selection, and symbolic regression and classification techniques. The volume includes several chapters on best practices and lessons learned from hands-on experience. 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.
Editors and Affiliations
Bibliographic Information
Book Title: Genetic Programming Theory and Practice XVI
Editors: Wolfgang Banzhaf, Lee Spector, Leigh Sheneman
Series Title: Genetic and Evolutionary Computation
DOI: https://doi.org/10.1007/978-3-030-04735-1
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-04734-4Published: 04 February 2019
eBook ISBN: 978-3-030-04735-1Published: 23 January 2019
Series ISSN: 1932-0167
Series E-ISSN: 1932-0175
Edition Number: 1
Number of Pages: XXI, 234
Number of Illustrations: 18 b/w illustrations, 47 illustrations in colour
Topics: Artificial Intelligence, Computational Intelligence, Algorithm Analysis and Problem Complexity