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.
| ISBN: | 9783319333816 |
| Publication date: | 6th June 2016 |
| Author: | Oliver Kramer |
| Publisher: | Springer an imprint of Springer International Publishing |
| Format: | Hardback |
| Pagination: | 124 pages |
| Series: | Studies in Big Data |
| Genres: |
Artificial intelligence Expert systems / knowledge-based systems Cybernetics and systems theory Data mining Computer modelling and simulation |
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.
Machine Learning for Evolution Strategies features in the following genres: Artificial intelligence, Expert systems / knowledge-based systems, Cybernetics and systems theory, Data mining, Computer modelling and simulation
Machine Learning for Evolution Strategies is available in Hardback
Machine Learning for Evolution Strategies was written by Oliver Kramer and published by Springer an imprint of Springer International Publishing
Machine Learning for Evolution Strategies has 124 pages
Yes it is part of Studies in Big Data series