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_aKramer, Oliver _0Local _999376 |
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_aMachine Learning for Evolution Strategies _cby Oliver Kramer |
| 260 |
_aCham _bSpringer International Publishing _c2016 |
||
| 300 |
_a1 recurso en línea (IX, 124 páginas) _b38 ilustraciones en color |
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| 336 |
_aTexto _btxt _2rdacontent |
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_aelectrónico _bc _2rdamedia |
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_arecurso electrónico _bcr _2rdacarrier |
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_aStudies in Big Data _x2197-6503 _v20 |
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| 505 | 0 | _aPart I Evolution Strategies -- Part II Machine Learning -- Part III Supervised Learning. | |
| 520 | 3 | _aThis 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. | |
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_aSpringerLink (Online service) _0Local _9106996 |
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_aInteligencia artificial _0comprobar BNE19900997218 _2embne _9413115 |
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_aAprendizaje automático _0(OCoLC)1004795 _2embne _0 _9166090 |
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_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-33383-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid) |
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