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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 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.
| Autorius: | Oliver Kramer |
| Serija: | Studies in Big Data |
| Leidėjas: | Springer Nature Switzerland |
| Išleidimo metai: | 2016 |
| Knygos puslapių skaičius: | 136 |
| ISBN-10: | 331933381X |
| ISBN-13: | 9783319333816 |
| Formatas: | 241 x 160 x 14 mm. Knyga kietu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Machine Learning for Evolution Strategies“
