ENGLISH

Machine Learning for Evolution Strategies

By Oliver Kramer (auth.)

Book information

Publisher
Springer International Publishing
Year
2016
ISBN
978-3-319-33381-6, 978-3-319-33383-0
DOI
10.1007/978-3-319-33383-0
Language
english
Format
PDF
Filesize
6 MB (5836042 bytes)
Series
Studies in Big Data 20
Edition
1
Pages
IX, 124\120
Time added
2016-07-20 04:00:00
Computational Intelligence; Simulation and ModelinPopulation and Evolutionary Models; Artificial Int

Description

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.

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