Machine Learning for Evolution Strategies

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106,99 

Studies in Big Data 20

ISBN: 331933381X
ISBN 13: 9783319333816
Autor: Kramer, Oliver
Verlag: Springer Verlag GmbH
Umfang: ix, 124 S., 38 farbige Illustr., 124 p. 38 illus. in color.
Erscheinungsdatum: 06.06.2016
Auflage: 1/2016
Format: 1.4 x 24.2 x 16.2
Gewicht: 365 g
Produktform: Gebunden/Hardback
Einband: Gebunden

This book introduces numerous algorithmic hybridizations between both worlds that show how machine learning can improve and support evolution strategies. The set of methods comprises covariance matrix estimation, meta-modeling of fitness and constraint functions, dimensionality reduction for search and visualization of highdimensional optimization processes, and clusteringbased niching. After giving an introduction to evolution strategies and machine learning, the book builds the bridge between both worlds with an algorithmic and experimental perspective. Experiments mostly employ a (1+1)-ES and are implemented in Python using the machine learning library scikit-learn. The examples are conducted on typical benchmark problems illustrating algorithmic concepts and their experimental behavior. The book closes with a discussion of related lines of research.

Artikelnummer: 9241632 Kategorie:

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E-Mail: juergen.hartmann@springer.com

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