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.
1133114163
Machine Learning for Evolution Strategies
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.
109.99
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Machine Learning for Evolution Strategies
124
Machine Learning for Evolution Strategies
124Paperback(Softcover reprint of the original 1st ed. 2016)
$109.99
109.99
In Stock
Product Details
| ISBN-13: | 9783319815008 |
|---|---|
| Publisher: | Springer International Publishing |
| Publication date: | 06/01/2018 |
| Series: | Studies in Big Data , #20 |
| Edition description: | Softcover reprint of the original 1st ed. 2016 |
| Pages: | 124 |
| Product dimensions: | 6.10(w) x 9.25(h) x (d) |
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