Genetic Programming Theory and Practice XIII

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, General Computing, Programming
Cover of the book Genetic Programming Theory and Practice XIII by , Springer International Publishing
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Author: ISBN: 9783319342238
Publisher: Springer International Publishing Publication: December 20, 2016
Imprint: Springer Language: English
Author:
ISBN: 9783319342238
Publisher: Springer International Publishing
Publication: December 20, 2016
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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