Genetic Programming Theory and Practice XIV

Herausgegeben:Riolo, Rick; Worzel, Bill; Goldman, Brian; Tozier, Bill

• Gebundenes Buch

Produktbeschreibung

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.

Produktdetails

• Genetic and Evolutionary Computation
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-319-97087-5
• 1st ed. 2018
• Seitenzahl: 244
• Erscheinungstermin: 8. November 2018
• Englisch
• Abmessung: 241mm x 160mm x 19mm
• Gewicht: 516g
• ISBN-13: 9783319970875
• ISBN-10: 3319970879
• Artikelnr.: 53009353

Inhaltsangabe

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.

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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.

Rezensionen

"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)