Genetic Programming Theory And Practice Xiii by Rick RioloGenetic Programming Theory And Practice Xiii by Rick Riolo

Genetic Programming Theory And Practice Xiii

byRick RioloEditorW.p. Worzel, Mark Kotanchek

Hardcover | December 30, 2016

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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. Topics in this volume include: multi-objective genetic programming, learning heuristics, Kaizen programming, Evolution of Everything (EvE), lexicase selection, behavioral program synthesis, symbolic regression with noisy training data, graph databases, and multidimensional clustering. It also covers several chapters on best practices and lesson learned from hands-on experience. Additional application areas include financial operations, genetic analysis, and predicting product choice. 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.

Title:Genetic Programming Theory And Practice XiiiFormat:HardcoverDimensions:262 pagesPublished:December 30, 2016Publisher:Springer-Verlag/Sci-Tech/TradeLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:3319342215

ISBN - 13:9783319342214

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Table of Contents

Evolving Simple Symbolic Regression Models by Multi-objective Genetic Programming.- Learning Heuristics for Mining RNA Sequence-Structure Motifs.- Kaizen Programming for Feature Construction for Classification.- GP as if You Meant It: An Exercise for Mindful Practice.- nPool: Massively Distributed Simultaneous Evolution and Cross-Validation in EC-Star.- Highly Accurate Symbolic Regression with Noisy Training Data.- Using Genetic Programming for Data Science: Lessons Learned.- The Evolution of Everything (EvE) and Genetic Programming.- Lexicase selection for program synthesis: a Diversity Analysis.- Using Graph Databases to Explore the Dynamics of Genetic Programming Runs.- Predicting Product Choice with Symbolic Regression and Classification.- Multiclass Classification Through Multidimensional Clustering.- Prime-Time: Symbolic Regression takes its place in the Real World.