Genetic Programming Theory and Practice XVIII
1st Edition
Published on 11. February 2022
XIV, 212 pages
978-981-16-8113-4 (ISBN)
Description
This book, written by the foremost international researchers and practitioners of genetic programming (GP), explores the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. In this year's edition, the topics covered include many of the most important issues and research questions in the ?eld, such as opportune application domains for GP-based methods, game playing and co-evolutionary search, symbolic regression and ef?cient learning strategies, encodings and representations for GP, schema theorems, and new selection mechanisms. The book 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.
More details
Series
Language
English
Place of publication
Singapore
Singapore
Publishing group
Springer Singapore
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
XIV, 212 p. 74 illus., 62 illus. in color.
File size
9,48 MB
ISBN-13
978-981-16-8113-4 (9789811681134)
DOI
10.1007/978-981-16-8113-4
Schweitzer Classification
Other editions
Additional editions
Wolfgang Banzhaf | Leonardo Trujillo | Stephan Winkler
Genetic Programming Theory and Practice XVIII
Book
02/2022
Springer
€160.49
Shipment within 3-4 weeks
Persons
Wolfgang Banzhaf
is a professor in the Department of Computer Science and Engineering at Michigan State University.
Content
Chapter 1. Finding Simple Solutions to Multi-Task Visual Reinforcement Learning Problems with Tangled Program Graphs.- Chapter 2. Grammar-based Vectorial Genetic Programming for Symbolic Regression.- Chapter 3. Grammatical Evolution Mapping for Semantically-Constrained Genetic Programming.- Chapter 4. What can phylogenetic metrics tell us about useful diversity in evolutionary algorithms?.- Chapter 5. An Exploration of Exploration: Measuring the ability of lexicaseselection to find obscure pathways to optimality.- Chapter 6. Feature Discovery with Deep Learning Algebra Networks.- Chapter 7. Back To The Future - Revisiting OrdinalGP & Trustable Models After a Decade.- Chapter 8. Fitness First.- Chapter 9. Designing Multiple ANNs with Evolutionary Development: Activity Dependence.- Chapter 10. Evolving and Analyzing modularity with GLEAM (Genetic Learning by Extraction and Absorption of Modules).- Chapter 11. Evolution of the Semiconductor Industry, and the Start of X Law.
