Machine Learning for Evolution Strategies - Kramer, Oliver; SpringerLink (Online service) (Cham : Springer International Publishing : Imprint: Springer, 2016)
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.
Part I Evolution Strategies -- Part II Machine Learning -- Part III Supervised Learning.
Keywords: Computational intelligence; Computer simulation; Data mining; System theory; Artificial intelligence; Computational Intelligence; Computer Modelling; Data Mining and Knowledge Discovery; Complex Systems; Artificial Intelligence