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Genetic Programming Theory and Practice XIV

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Genetic Programming Theory and Practice XIV/ edited by Rick Riolo, Bill Worzel, Brian Goldman, Bill Tozier.
其他作者:	Riolo, Rick.
面頁冊數:	XV, 227 p. 52 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Artificial intelligence. -
電子資源:	https://doi.org/10.1007/978-3-319-97088-2
ISBN:	9783319970882

Genetic Programming Theory and Practice XIV
Genetic Programming Theory and Practice XIV[electronic resource] /edited by Rick Riolo, Bill Worzel, Brian Goldman, Bill Tozier. - 1st ed. 2018. - XV, 227 p. 52 illus.online resource. - Genetic and Evolutionary Computation,1932-0167. - Genetic and Evolutionary Computation,.

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 and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool.

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.

ISBN: 9783319970882

Standard No.: 10.1007/978-3-319-97088-2doiSubjects--Topical Terms:

559380
Artificial intelligence.

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

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