Overview
- 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)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (14 chapters)
-
Front Matter
-
Back Matter
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
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.
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
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