Machine Learning in Evolution Strategies
Machine Learning in Evolution Strategies
Springer | Studies in Big Data | June 26, 2016 | ISBN-10: 331933381X | 110 pages | pdf | 5.57 mb
Springer | Studies in Big Data | June 26, 2016 | ISBN-10: 331933381X | 110 pages | pdf | 5.57 mb
Authors: Kramer, Oliver
State of the art presentation of Machine Learning in Evolution Strategies
Condensed presentation
Short introduction and recent research
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
Number of Illustrations and Tables
38 illustrations in colour
Topics
Computational Intelligence
Simulation and Modeling
Data Mining and Knowledge Discovery
Socio- and Econophysics, Population and Evolutionary Models
Artificial Intelligence (incl. Robotics)
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