Genetic Programming Theory and Practice XVIII / edited by Wolfgang Banzhaf, Leonardo Trujillo, Stephan Winkler, Bill Worzel.

Format:

Book

Contributor:

Banzhaf, Wolfgang., Editor.

Trujillo, Leonardo, Editor.

Winkler, Stephan, Editor.

Worzel, Bill, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Genetic and evolutionary computation series 1932-0175

Genetic and Evolutionary Computation, 1932-0175

Language:

English

Subjects (All):

Computer science.

Bionics.

Algorithms.

Models of Computation.

Bioinspired Technologies.

Local Subjects:

Models of Computation.

Bioinspired Technologies.

Algorithms.

Physical Description:

1 online resource (XIV, 212 pages) : 74 illustrations, 62 illustrations in color.

Edition:

1st ed. 2022.

Contained In:

Springer Nature eBook

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2022.

System Details:

text file PDF

Summary:

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 field, such as opportune application domains for GP-based methods, game playing and co-evolutionary search, symbolic regression and efficient 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.

Contents:

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 lexicase selection to find obscure pathways to optimality

Chapter 6. Feature Discovery with Deep Learning Algebra Networks

Chapter 7. Back To The Future - Revisiting OrdinalGP and 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.

Other Format:

Printed edition:

ISBN:

978-981-16-8113-4

9789811681134

Access Restriction:

Restricted for use by site license.