Genetic programming theory and practice XVIII

by Banzhaf, Wolfgang Trujillo, Leonardo Winkler, Stephan Worzel, Bill

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Title Statements

Full title :Genetic programming theory and practice XVIII / Wolfgang Banzhaf, Leonardo Trujillo, Stephan Winkler, Bill Worzel, editors.

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 years 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 efcient 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.

Edition Statements

Publication Date:2022.

Publication Statements

Publisher(s):Springer
Place of Publication:Singapore
Language:eng
Publication Date:2022.

Notes

5.4.1 Lexicase Selection Out-Explores Tournament Selection.

Includes index.

The Physical Object

Description:1 online resource (220 pages)

Edition Identifiers

ISBN:9789811681134, 9811681139
DOI:10.1007/978-981-16-8113-4
oclc:on1298387863
LC Classification:QA76.623
Dewey Decimal:006.3/823

Intro -- Foreword -- Preface -- Contents -- Contributors -- 1 Finding Simple Solutions to Multi-Task Visual Reinforcement Learning Problems with Tangled Program Graphs -- 1.1 Introduction -- 1.2 Tangled Program Graphs -- 1.2.1 Learners -- 1.2.2 Teams -- 1.2.3 Graphs -- 1.2.4 Memory -- 1.3 Mechanisms for Accelerating TPG Evolution -- 1.3.1 Rampant Mutation -- 1.3.2 Multi-actions -- 1.4 ViZDoom Subtask Selection and Performance Evaluation -- 1.5 Empirical Methodology -- 1.5.1 Task Domains -- 1.5.2 Parameters -- 1.6 Results -- 1.6.1 Fitness -- 1.6.2 Generalization -- 1.6.3 Complexity

1.6.4 Details of a RAPS Solution -- 1.7 Conclusions -- References -- 2 Grammar-Based Vectorial Genetic Programming for Symbolic Regression -- 2.1 Introduction -- 2.2 State of the Art -- 2.2.1 Vectorial Genetic Programming -- 2.2.2 Grammar-Based Genetic Programming -- 2.2.3 Feature Engineering and Feature Extraction -- 2.2.4 Deep Learning -- 2.3 Grammar-Based Vectorial Genetic Programming -- 2.3.1 Vectorial Tree Interpretation -- 2.3.2 Vectorial Symbolic Regression Grammar -- 2.4 Experiment Setup -- 2.5 Results -- 2.5.1 Analysis Benchmarks Group A -- 2.5.2 Analysis Benchmarks Group B

2.6 Discussion and …