| 書(shū)目名稱 | Machine Learning for Evolution Strategies |
| 編輯 | Oliver Kramer |
| 視頻video | http://file.papertrans.cn/621/620621/620621.mp4 |
| 概述 | State of the art presentation of Machine Learning in Evolution Strategies.Condensed presentation.Short introduction and recent research.Includes supplementary material: |
| 叢書(shū)名稱 | Studies in Big Data |
| 圖書(shū)封面 |  |
| 描述 | .This bookintroduces numerous algorithmic hybridizations between both worlds that showhow machine learning can improve and support evolution strategies. The set ofmethods comprises covariance matrix estimation, meta-modeling of fitness andconstraint functions, dimensionality reduction for search and visualization ofhigh-dimensional optimization processes, and clustering-based niching. Aftergiving an introduction to evolution strategies and machine learning, the bookbuilds the bridge between both worlds with an algorithmic and experimentalperspective. Experiments mostly employ a (1+1)-ES and are implemented in Pythonusing the machine learning library scikit-learn. The examples are conducted ontypical benchmark problems illustrating algorithmic concepts and theirexperimental behavior. The book closes with a discussion of related lines ofresearch.. |
| 出版日期 | Book 2016 |
| 關(guān)鍵詞 | Big Data; Data Mining; Evolution Strategies; Evolutionary Computation; Machine Learning; data-driven scie |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-33383-0 |
| isbn_softcover | 978-3-319-81500-8 |
| isbn_ebook | 978-3-319-33383-0Series ISSN 2197-6503 Series E-ISSN 2197-6511 |
| issn_series | 2197-6503 |
| copyright | Springer International Publishing Switzerland 2016 |
|Archiver|手機(jī)版|小黑屋|
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