Machine Learning for Evolution Strategies

Machine Learning for Evolution Strategies (Studies in Big Data)
Authors: Oliver Kramer
ISBN-10: 331933381X
ISBN-13: 9783319333816
Edition: 1st ed. 2016
Released: 2016-05-26
Pages: 124 pages
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.
1.Introduction
1.Evolution Strategies
2.Machine Learning
3.Supervised Learning
4.Unsupervised Learning
5.Ending