Genetic Programming Theory and Practice XIII / edited by Rick Riolo, W.P. Worzel, Mark Kotanchek, Arthur Kordon.
Riolo, Rick, editor.; Worzel, W. P., editor.; Kotanchek, Mark, editor.; Kordon, Arthur, editor.
2016
Q334-342
Available Online

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Title Genetic Programming Theory and Practice XIII / edited by Rick Riolo, W.P. Worzel, Mark Kotanchek, Arthur Kordon.
ISBN 9783319342238
3319342231
9783319342214
DOI https://doi.org/10.1007/978-3-319-34223-8
Published Cham : Springer International Publishing : Imprint : Springer, 2016.
Language English
Description 1 online resource (xx, 262 pages) : illustrations.
Item Number 10.1007/978-3-319-34223-8 doi
Call Number Q334-342
Dewey Decimal Classification 006.3
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.
Access Note Access limited to authorized users.
Digital File Characteristics text file PDF
Added Author Riolo, Rick, editor.
Worzel, W. P., editor.
Kotanchek, Mark, editor.
Kordon, Arthur, editor.
Series Genetic and evolutionary computation series.
Available in Other Form Print version: 9783319342214
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
All Resources
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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