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Genetic Programming Theory and Practice XIII
Title
Genetic Programming Theory and Practice XIII [electronic resource] / edited by Rick Riolo, W.P. Worzel, Mark Kotanchek, Arthur Kordon.
ISBN
9783319342238
Publication
Cham : Springer International Publishing : Imprint: Springer, 2016.
Physical Description
XX, 262 p. 69 illus., 31 illus. in color : online resource.
Local Notes
Access is available to the Yale community.
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Access restricted by licensing agreement.
Summary
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.
Variant and related titles
Springer ebooks.
Other formats
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
January 05, 2017
Series
Genetic and Evolutionary Computation,
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.
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