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Machine Learning for Evolution Strategies

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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 problems illustrating algorithmic concepts and their experimental behavior. The book closes with a discussion of related lines of research.

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Publisher

Cham : Springer Cham., 2016

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Language

English

ISBN/ISSN

978-3-319-33383-0

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NONE

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Statement of Responsibility

Oliver Kramer

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Cataloger

Kholif Basri

Source

https://link.springer.com/book/10.1007/978-3-319-33383-0

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