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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.
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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.
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Machine Learning in Evolution Strategies.
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Cham, Springer International Publishing; : Springer., 2016
ISBN 10: 331933381X
ISBN 13: 9783319333816
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Da: Universitätsbuchhandlung Herta Hold GmbH, Berlin, Germania
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23.5 cm x 15.5 cm, 0 g. VII, 124 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Studies in Big Data ; 20. Sprache: Englisch. Codice articolo 41532HB
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Machine Learning for Evolution Strategies (Studies in Big Data, 20)
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Machine Learning for Evolution Strategies
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Machine Learning for Evolution Strategies (Hardcover)
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Springer International Publishing AG, Cham, 2016
ISBN 10: 331933381X
ISBN 13: 9783319333816
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Hardcover. Condizione: new. Hardcover. 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. This bookintroduces numerous algorithmic hybridizations between both worlds that showhow machine learning can improve and support evolution strategies. Aftergiving an introduction to evolution strategies and machine learning, the bookbuilds the bridge between both worlds with an algorithmic and experimentalperspective. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9783319333816
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Machine Learning for Evolution Strategies (Studies in Big Data, 20)
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Machine Learning for Evolution Strategies
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Springer International Publishing Jun 2016, 2016
ISBN 10: 331933381X
ISBN 13: 9783319333816
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 136 pp. Englisch. Codice articolo 9783319333816
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Machine Learning for Evolution Strategies
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Machine Learning for Evolution Strategies
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Springer International Publishing AG, 2016
ISBN 10: 331933381X
ISBN 13: 9783319333816
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Condizione: New. Series: Studies in Big Data. Num Pages: 133 pages, 38 colour illustrations, biography. BIC Classification: UYQM. Category: (G) General (US: Trade). Dimension: 235 x 155 x 10. Weight in Grams: 374. . 2016. Hardback. . . . . Codice articolo V9783319333816
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Machine Learning in Evolution Strategies
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Springer International Publishing, 2016
ISBN 10: 331933381X
ISBN 13: 9783319333816
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. State of the art presentation of Machine Learning in Evolution StrategiesCondensed presentationShort introduction and recent researchThis bookintroduces numerous algorithmic hybridizations between both worlds that showhow machine. Codice articolo 119052186
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