Articles liés à Machine Learning for Evolution Strategies
Synopsis
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.
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Machine Learning for Evolution Strategies (Studies in Big Data, 20)
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Machine Learning for Evolution Strategies (Paperback)
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Springer International Publishing AG, Cham, 2018
ISBN 10 : 3319815008
ISBN 13 : 9783319815008
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Paperback
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Paperback. Etat : new. Paperback. 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. N° de réf. du vendeur 9783319815008
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Machine Learning for Evolution Strategies (Studies in Big Data, 20)
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Machine Learning for Evolution Strategies (Studies in Big Data, 20)
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Machine Learning for Evolution Strategies
Edité par
Berlin Springer International Publishing Springer Mai 2018, 2018
ISBN 10 : 3319815008
ISBN 13 : 9783319815008
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Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Taschenbuch. Etat : 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. 124 pp. Englisch. N° de réf. du vendeur 9783319815008
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Machine Learning for Evolution Strategies
Vendeur : Books Puddle, New York, NY, Etats-Unis
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Etat : New. pp. IX, 124 38 illus. in color. 1 Edition NO-PA16APR2015-KAP. N° de réf. du vendeur 26384560698
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Machine Learning for Evolution Strategies
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Springer International Publishing, 2018
ISBN 10 : 3319815008
ISBN 13 : 9783319815008
Neuf
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Etat : 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. N° de réf. du vendeur 458621863
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Machine Learning for Evolution Strategies
impression à la demandeVendeur : Majestic Books, Hounslow, Royaume-Uni
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Etat : New. Print on Demand pp. IX, 124 38 illus. in color. N° de réf. du vendeur 379343333
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Machine Learning for Evolution Strategies
impression à la demandeVendeur : Biblios, Frankfurt am main, HESSE, Allemagne
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Etat : New. PRINT ON DEMAND pp. IX, 124 38 illus. in color. N° de réf. du vendeur 18384560688
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Machine Learning for Evolution Strategies
Edité par
Springer, Berlin, Springer International Publishing, Springer, 2018
ISBN 10 : 3319815008
ISBN 13 : 9783319815008
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. N° de réf. du vendeur 9783319815008
Quantité disponible : 2 disponible(s)