000 | 02792nam a22005535i 4500 | ||
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001 | 978-3-319-33383-0 | ||
003 | DE-He213 | ||
005 | 20200420220217.0 | ||
007 | cr nn 008mamaa | ||
008 | 160525s2016 gw | s |||| 0|eng d | ||
020 |
_a9783319333830 _9978-3-319-33383-0 |
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024 | 7 |
_a10.1007/978-3-319-33383-0 _2doi |
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050 | 4 | _aQ342 | |
072 | 7 |
_aUYQ _2bicssc |
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072 | 7 |
_aCOM004000 _2bisacsh |
|
082 | 0 | 4 |
_a006.3 _223 |
100 | 1 |
_aKramer, Oliver. _eauthor. |
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245 | 1 | 0 |
_aMachine Learning for Evolution Strategies _h[electronic resource] / _cby Oliver Kramer. |
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2016. |
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300 |
_aIX, 124 p. 38 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_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 | _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. | ||
650 | 0 | _aEngineering. | |
650 | 0 | _aData mining. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aComputer simulation. | |
650 | 0 | _aSociophysics. | |
650 | 0 | _aEconophysics. | |
650 | 0 | _aComputational intelligence. | |
650 | 1 | 4 | _aEngineering. |
650 | 2 | 4 | _aComputational Intelligence. |
650 | 2 | 4 | _aSimulation and Modeling. |
650 | 2 | 4 | _aData Mining and Knowledge Discovery. |
650 | 2 | 4 | _aSocio- and Econophysics, Population and Evolutionary Models. |
650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783319333816 |
830 | 0 |
_aStudies in Big Data, _x2197-6503 ; _v20 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-319-33383-0 |
912 | _aZDB-2-ENG | ||
942 | _cEBK | ||
999 |
_c51683 _d51683 |