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|a 9783319333830
|9 978-3-319-33383-0
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|a 10.1007/978-3-319-33383-0
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|a Kramer, Oliver.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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|a Machine Learning for Evolution Strategies
|h [electronic resource] /
|c by Oliver Kramer.
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|a 1st ed. 2016.
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2016.
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|a IX, 124 p. 38 illus. in color.
|b online resource.
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|a text
|b txt
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|a computer
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|a online resource
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|a text file
|b PDF
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|a Studies in Big Data,
|x 2197-6511 ;
|v 20
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|a Part I Evolution Strategies -- Part II Machine Learning -- Part III Supervised Learning.
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|a 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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|a Computational intelligence.
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|a Computer simulation.
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|a Data mining.
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|a System theory.
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|a Artificial intelligence.
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|a Computational Intelligence.
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|a Computer Modelling.
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|a Data Mining and Knowledge Discovery.
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|a Complex Systems.
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|a Artificial Intelligence.
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|a SpringerLink (Online service)
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|t Springer Nature eBook
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|i Printed edition:
|z 9783319333816
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|i Printed edition:
|z 9783319333823
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|i Printed edition:
|z 9783319815008
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|a Studies in Big Data,
|x 2197-6511 ;
|v 20
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|u https://doi.uam.elogim.com/10.1007/978-3-319-33383-0
|z Texto Completo
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|a ZDB-2-ENG
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|a ZDB-2-SXE
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|a Engineering (SpringerNature-11647)
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|a Engineering (R0) (SpringerNature-43712)
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