Machine Learning For Evolution Strategies
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Machine Learning for Evolution Strategies
Author | : Oliver Kramer |
Publisher | : Springer |
Total Pages | : 120 |
Release | : 2016-05-25 |
Genre | : Technology & Engineering |
ISBN | : 3319333836 |
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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.