Genetic Programming Theory and Practice XIV (Hardcover, 1st ed. 2018)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Genetic and Evolutionary Computation
Release date:	October 2018
First published:	2018
Editors:	Rick Riolo • Bill Worzel • Brian Goldman • Bill Tozier
Dimensions:	235 x 155 x 21mm (L x W x T)
Format:	Hardcover
Pages:	227
Edition:	1st ed. 2018
ISBN-13:	978-3-319-97087-5
Categories:	Books > Computing & IT > General theory of computing > Data structures Books > Computing & IT > Computer programming > Algorithms & procedures Books > Computing & IT > Applications of computing > Artificial intelligence > General Promotions
LSN:	3-319-97087-9
Barcode:	9783319970875

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