Machine Learning for Evolution Strategies

  • Oliver Kramer

Part of the Studies in Big Data book series (SBD, volume 20)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Oliver Kramer
    Pages 1-10
  3. Evolution Strategies

    1. Front Matter
      Pages 11-11
    2. Oliver Kramer
      Pages 13-21
    3. Oliver Kramer
      Pages 23-32
  4. Machine Learning

    1. Front Matter
      Pages 33-33
    2. Oliver Kramer
      Pages 35-43
    3. Oliver Kramer
      Pages 45-53
  5. Supervised Learning

    1. Front Matter
      Pages 55-55
    2. Oliver Kramer
      Pages 57-65
    3. Oliver Kramer
      Pages 67-76
  6. Unsupervised Learning

    1. Front Matter
      Pages 77-77
    2. Oliver Kramer
      Pages 79-87
    3. Oliver Kramer
      Pages 89-98
    4. Oliver Kramer
      Pages 99-107
  7. Ending

    1. Front Matter
      Pages 109-109
    2. Oliver Kramer
      Pages 111-117
  8. Back Matter
    Pages 119-124

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.


Big Data Data Mining Evolution Strategies Evolutionary Computation Machine Learning

Authors and affiliations

  • Oliver Kramer
    • 1
  1. 1.InformatikUniversität OldenburgOldenburgGermany

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-319-33381-6
  • Online ISBN 978-3-319-33383-0
  • Series Print ISSN 2197-6503
  • Series Online ISSN 2197-6511
  • Buy this book on publisher's site
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