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Machine Learning for Evolution Strategies
Hardcover
Series: Studies in Big Data, Book 20
ISBN13: 9783319333816
Publisher: Springer Nature
Published: Jun 6 2016
Pages: 124
Weight: 0.82
Height: 0.38 Width: 6.14 Depth: 9.21
Language: English
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.
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