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Machine Learning for Evolution Strategies
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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.§
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