Machine Learning for Evolution Strategies - Oliver Kramer
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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 in ... Täielik kirjeldus
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Kirjeldus
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.
Lisateave
| Autor | Oliver Kramer |
|---|---|
| Kirjastaja | Springer Nature Switzerland |
| Series | Studies in Big Data |
| Väljalaskeaasta | 2016 |
| Kaanetüüp | Kõvakaaneline |
| EAN | 9783319333816 |