Studies in Big Data

Machine Learning for Evolution Strategies

Authors: Kramer, Oliver

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eBook 101,14 €
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  • ISBN 978-3-319-33383-0
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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.

Table of contents (11 chapters)

Buy this book

eBook 101,14 €
price for Spain (gross)
  • ISBN 978-3-319-33383-0
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 124,79 €
price for Spain (gross)
  • ISBN 978-3-319-33381-6
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
Softcover 124,79 €
price for Spain (gross)
  • ISBN 978-3-319-81500-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

Bibliographic Information
Book Title
Machine Learning for Evolution Strategies
Authors
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