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|a Kramer, Oliver.
|e author.
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| 245 |
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|a Machine Learning for Evolution Strategies
|h [electronic resource] /
|c by Oliver Kramer.
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| 250 |
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|a 1st ed. 2016.
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| 264 |
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1 |
|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2016.
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| 300 |
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|a IX, 124 p. 38 illus. in color.
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|a Studies in Big Data,
|x 2197-6503 ;
|v 20
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|a Springer eBook Collection
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| 505 |
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|a Part I Evolution Strategies -- Part II Machine Learning -- Part III Supervised Learning.
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| 520 |
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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 Electronic access restricted to members of the Holy Cross Community.
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|a Computational intelligence.
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| 650 |
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|a Computer simulation.
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|a Data mining.
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|a Sociophysics.
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|a Artificial intelligence.
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