Machine Learning for Evolution Strategies

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Focusing on the integration of machine learning and evolution strategies, this book presents various algorithmic hybridizations that enhance optimization processes. It covers methods such as covariance matrix estimation and dimensionality reduction, alongside clustering-based niching. The author introduces both fields and bridges them through practical experiments primarily utilizing a (1+1)-ES algorithm implemented in Python with scikit-learn. Typical benchmark problems are used to illustrate concepts, culminating in a discussion of related research avenues.