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.