Machine Learning for Evolution Strategies (Hardcover)

Oliver Kramer

  • 出版商: Springer
  • 出版日期: 2016-05-26
  • 售價: $5,719
  • 貴賓價: 9.5$5,433
  • 語言: 英文
  • 頁數: 124
  • 裝訂: Hardcover
  • ISBN: 331933381X
  • ISBN-13: 9783319333816
  • 相關分類: 機器學習

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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.