Genetic Programming Theory and Practice XIV

by Rick Riolo, Bill Worzel, Brian Goldman, Bill Tozier

Overview

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.

Product Details

ISBN-13:	9783319970875
Publisher:	Springer International Publishing
Publication date:	10/24/2018
Series:	Genetic and Evolutionary Computation
Edition description:	1st ed. 2018
Pages:	227
Product dimensions:	6.10(w) x 9.25(h) x (d)

Table of Contents

1 Similarity-based Analysis of Population Dynamics in Genetic Programming Performing Symbolic Regression.- 2 An Investigation of Hybrid Structural and Behavioral Diversity Methods in Genetic Programming.- 3 Investigating Multi-Population Competitive Coevolution for Anticipation of Tax Evasion.- 4 Evolving Artificial General Intelligence for Video Game Controllers.- 5 A Detailed Analysis of a PushGP Run.- 6 Linear Genomes for Structured Programs.- 7 Neutrality, Robustness, and Evolvability in Genetic Programming.- 8 Local Search is Underused in Genetic Programming.- 9 PRETSL: Distributed Probabilistic Rule Evolution for Time-Series Classification.- 10 Discovering Relational Structural in Program Synthesis Problems with Analogical Reasoning.- 11 An Evolutionary Algorithm for Big Data Multi-Class Classification Problems.- 12 A Genetic Framework for Building Dispersion Operators in the Semantic Space.- 13 Assisting Asset Model Development with Evolutionary Augmentation.- 14 Identifying andHarnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool.

Editorial 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)

From the Publisher