國立虎尾科技大學 |

Machine Learning for Evolution Strategies

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Machine Learning for Evolution Strategies/ by Oliver Kramer.
Author:	Kramer, Oliver.
Description:	IX, 124 p. 38 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computational intelligence. -
Online resource:	https://doi.org/10.1007/978-3-319-33383-0
ISBN:	9783319333830

Machine Learning for Evolution Strategies
Kramer, Oliver.

Machine Learning for Evolution Strategies[electronic resource] /by Oliver Kramer. - 1st ed. 2016. - IX, 124 p. 38 illus. in color.online resource. - Studies in Big Data,202197-6503 ;. - Studies in Big Data,8.

Part I Evolution Strategies -- Part II Machine Learning -- Part III Supervised Learning.

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 high-dimensional optimization processes, and clustering-based 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.

ISBN: 9783319333830

Standard No.: 10.1007/978-3-319-33383-0doiSubjects--Topical Terms:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Machine Learning for Evolution Strategies
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