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Machine Learning for Evolution Strategies: Studies in Big Data, cartea 20

Autor Oliver Kramer
en Limba Engleză Hardback – 6 iun 2016
This bookintroduces numerous algorithmic hybridizations between both worlds that showhow machine learning can improve and support evolution strategies. The set ofmethods comprises covariance matrix estimation, meta-modeling of fitness andconstraint functions, dimensionality reduction for search and visualization ofhigh-dimensional optimization processes, and clustering-based niching. Aftergiving an introduction to evolution strategies and machine learning, the bookbuilds the bridge between both worlds with an algorithmic and experimentalperspective. Experiments mostly employ a (1+1)-ES and are implemented in Pythonusing the machine learning library scikit-learn. The examples are conducted ontypical benchmark problems illustrating algorithmic concepts and theirexperimental behavior. The book closes with a discussion of related lines ofresearch.
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Specificații

ISBN-13: 9783319333816
ISBN-10: 331933381X
Pagini: 110
Ilustrații: IX, 124 p. 38 illus. in color.
Dimensiuni: 155 x 235 x 10 mm
Greutate: 0.37 kg
Ediția:1st ed. 2016
Editura: Springer International Publishing
Colecția Springer
Seria Studies in Big Data

Locul publicării:Cham, Switzerland

Cuprins

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

Textul de pe ultima copertă

This bookintroduces numerous algorithmic hybridizations between both worlds that showhow machine learning can improve and support evolution strategies. The set ofmethods comprises covariance matrix estimation, meta-modeling of fitness andconstraint functions, dimensionality reduction for search and visualization ofhigh-dimensional optimization processes, and clustering-based niching. Aftergiving an introduction to evolution strategies and machine learning, the bookbuilds the bridge between both worlds with an algorithmic and experimentalperspective. Experiments mostly employ a (1+1)-ES and are implemented in Pythonusing the machine learning library scikit-learn. The examples are conducted ontypical benchmark problems illustrating algorithmic concepts and theirexperimental behavior. The book closes with a discussion of related lines ofresearch.

Caracteristici

State of the art presentation of Machine Learning in Evolution Strategies
Condensed presentation
Short introduction and recent research
Includes supplementary material: sn.pub/extras