Editors:
- Provides chapters describing cutting-edge work on the theory and applications of genetic programming (GP)
- Offers large-scale, real-world applications of GP to a variety of problem domains
- Written by leading international experts from both academia and industry
Part of the book series: Genetic and Evolutionary Computation (GEVO)
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Table of contents (14 chapters)
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Front Matter
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Back Matter
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. Chapters in this volume include:
- Similarity-based Analysis of Population Dynamics in GP Performing Symbolic Regression
- Hybrid Structural and Behavioral Diversity Methods in GP
- Multi-Population Competitive Coevolution for Anticipation of Tax Evasion
- Evolving Artificial General Intelligence for Video Game Controllers
- A Detailed Analysis of a PushGP Run
- Linear Genomes for Structured Programs
- Neutrality, Robustness, and Evolvability in GP
- Local Search in GP
- PRETSL: Distributed Probabilistic Rule Evolution for Time-Series Classification
- Relational Structure in Program Synthesis Problems with Analogical Reasoning
- An Evolutionary Algorithm for Big Data Multi-Class Classification Problems
- A Generic Framework for Building Dispersion Operators in the Semantic Space
- Assisting Asset Model Development with Evolutionary Augmentation
- Building Blocks of Machine Learning Pipelines for Initialization of a Data Science Automation Tool
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.
Keywords
- Genetic programming
- Genetic programming theory
- Genetic programming applications
- Symbolic regression
- Evolution of models
- Program induction
- Artificial evolution
- Feature selection
- Artificial General Intelligence
- Distributed Probabilistic Rule
- Dispersion Operators
- Evolutionary Augmentation
- Analogical Reasoning
- algorithm analysis and problem complexity
Reviews
“This highly technical book is meant for a very specialized audience: researchers in GP. The topics discussed offer interesting insight into how research in GP is evolving. … I strongly recommend this book for researchers in evolutionary computing and GP.” (S. V. Nagaraj, Computing Reviews, November 12, 2020)
Editors and Affiliations
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Center for the Study of Complex Sys, University of Michigan, Ann Arbor, USA
Rick Riolo
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Evolution Enterprises, Ann Arbor, USA
Bill Worzel
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Colorado State University, Fort Collins, USA
Brian Goldman
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Ann Arbor, USA
Bill Tozier
Bibliographic Information
Book Title: Genetic Programming Theory and Practice XIV
Editors: Rick Riolo, Bill Worzel, Brian Goldman, Bill Tozier
Series Title: Genetic and Evolutionary Computation
DOI: https://doi.org/10.1007/978-3-319-97088-2
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2018
Hardcover ISBN: 978-3-319-97087-5Published: 08 November 2018
Softcover ISBN: 978-3-030-07300-8Published: 30 January 2019
eBook ISBN: 978-3-319-97088-2Published: 24 October 2018
Series ISSN: 1932-0167
Series E-ISSN: 1932-0175
Edition Number: 1
Number of Pages: XV, 227
Number of Illustrations: 52 b/w illustrations
Topics: Artificial Intelligence, Computational Intelligence, Algorithm Analysis and Problem Complexity