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Machine Learning for Evolution Strategies

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124pages
Temps de lecture
5heures

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Focusing on the intersection of machine learning and evolution strategies, this book presents a variety of algorithmic hybridizations that enhance optimization processes. It covers techniques such as covariance matrix estimation, meta-modeling, and clustering-based niching. With a strong emphasis on practical application, experiments utilize a (1+1)-ES implemented in Python with scikit-learn, addressing benchmark problems to illustrate key concepts. The text also explores broader research implications, bridging theoretical frameworks with experimental findings.

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Machine Learning for Evolution Strategies, Oliver Kramer

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Année de publication
2018
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