- State of the art presentation of Machine Learning in Evolution Strategies
- Condensed presentation
- Short introduction and recent research
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- About this book
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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.
- Table of contents (11 chapters)
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Introduction
Pages 1-10
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Evolution Strategies
Pages 13-21
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Covariance Matrix Estimation
Pages 23-32
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Machine Learning
Pages 35-43
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Scikit-Learn
Pages 45-53
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Table of contents (11 chapters)
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- Download Table of contents PDF (116.2 KB)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Machine Learning for Evolution Strategies
- Authors
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- Oliver Kramer
- Series Title
- Studies in Big Data
- Series Volume
- 20
- Copyright
- 2016
- Publisher
- Springer International Publishing
- Copyright Holder
- Springer International Publishing Switzerland
- eBook ISBN
- 978-3-319-33383-0
- DOI
- 10.1007/978-3-319-33383-0
- Hardcover ISBN
- 978-3-319-33381-6
- Softcover ISBN
- 978-3-319-81500-8
- Series ISSN
- 2197-6503
- Edition Number
- 1
- Number of Pages
- IX, 124
- Number of Illustrations
- 38 illustrations in colour
- Topics
