Machine Learning for Evolution Strategies (Hardcover, 1st ed. 2016)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Studies in Big Data, 20
Release date:	June 2016
First published:	2016
Authors:	Oliver Kramer
Dimensions:	235 x 155 x 10mm (L x W x T)
Format:	Hardcover
Pages:	124
Edition:	1st ed. 2016
ISBN-13:	978-3-319-33381-6
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	3-319-33381-X
Barcode:	9783319333816

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