Overview
- State of the art presentation of Machine Learning in Evolution Strategies
- Condensed presentation
- Short introduction and recent research
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Big Data (SBD, volume 20)
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Table of contents (11 chapters)
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Front Matter
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Evolution Strategies
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Front Matter
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Machine Learning
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Front Matter
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Supervised Learning
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Front Matter
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Unsupervised Learning
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Front Matter
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Ending
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Front Matter
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Back Matter
Keywords
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Machine Learning for Evolution Strategies
Authors: Oliver Kramer
Series Title: Studies in Big Data
DOI: https://doi.org/10.1007/978-3-319-33383-0
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2016
Hardcover ISBN: 978-3-319-33381-6Published: 06 June 2016
Softcover ISBN: 978-3-319-81500-8Published: 30 May 2018
eBook ISBN: 978-3-319-33383-0Published: 25 May 2016
Series ISSN: 2197-6503
Series E-ISSN: 2197-6511
Edition Number: 1
Number of Pages: IX, 124
Number of Illustrations: 38 illustrations in colour
Topics: Computational Intelligence, Simulation and Modeling, Data Mining and Knowledge Discovery, Data-driven Science, Modeling and Theory Building, Artificial Intelligence