Machine Learning for Evolution Strategies
(Sprache: Englisch)
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...
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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.
Inhaltsverzeichnis zu „Machine Learning for Evolution Strategies “
Part I Evolution Strategies.- Part II Machine Learning.- Part III Supervised Learning.Bibliographische Angaben
- Autor: Oliver Kramer
- 2018, Softcover reprint of the original 1st ed. 2016, IX, 124 Seiten, 124 farbige Abbildungen, Maße: 15,5 x 23,5 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 3319815008
- ISBN-13: 9783319815008
Sprache:
Englisch
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