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Record Nr. |
UNINA9910739403203321 |
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Autore |
Kramer Oliver |
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Titolo |
Machine Learning for Evolution Strategies / / by Oliver Kramer |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
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ISBN |
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Edizione |
[1st ed. 2016.] |
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Descrizione fisica |
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1 online resource (IX, 124 p. 38 illus. in color.) |
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Collana |
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Studies in Big Data, , 2197-6511 ; ; 20 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Computer simulation |
Data mining |
System theory |
Artificial intelligence |
Computational Intelligence |
Computer Modelling |
Data Mining and Knowledge Discovery |
Complex Systems |
Artificial Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters and index. |
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Nota di contenuto |
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Part I Evolution Strategies -- Part II Machine Learning -- Part III Supervised Learning. |
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Sommario/riassunto |
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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 |
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