000 | 02966nam a22004815i 4500 | ||
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001 | 978-3-540-73192-4 | ||
003 | DE-He213 | ||
005 | 20161121231204.0 | ||
007 | cr nn 008mamaa | ||
008 | 100301s2007 gw | s |||| 0|eng d | ||
020 |
_a9783540731924 _9978-3-540-73192-4 |
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024 | 7 |
_a10.1007/978-3-540-73192-4 _2doi |
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050 | 4 | _aTA329-348 | |
050 | 4 | _aTA640-643 | |
072 | 7 |
_aTBJ _2bicssc |
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072 | 7 |
_aMAT003000 _2bisacsh |
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082 | 0 | 4 |
_a519 _223 |
100 | 1 |
_aSchaefer, Robert. _eauthor. |
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245 | 1 | 0 |
_aFoundations of Global Genetic Optimization _h[electronic resource] / _cby Robert Schaefer. |
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2007. |
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300 |
_aXI, 222 p. _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 Computational Intelligence, _x1860-949X ; _v74 |
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505 | 0 | _aGlobal optimization problems -- Basic models of genetic computations -- Asymptotic behavior of the artificial genetic systems -- Adaptation in genetic search -- Two-phase stochastic global optimization strategies -- Summary and perspectives of genetic algorithms in continuous global optimization. | |
520 | _aThis book is devoted to the application of genetic algorithms in continuous global optimization. Some of their properties and behavior are highlighted and formally justified. Various optimization techniques and their taxonomy are the background for detailed discussion. The nature of continuous genetic search is explained by studying the dynamics of probabilistic measure, which is utilized to create subsequent populations. This approach shows that genetic algorithms can be used to extract some areas of the search domain more effectively than to find isolated local minima. The biological metaphor of such behavior is the whole population surviving by rapid exploration of new regions of feeding rather than caring for a single individual. One group of strategies that can make use of this property are two-phase global optimization methods. In the first phase the central parts of the basins of attraction are distinguished by genetic population analysis. Afterwards, the minimizers are found by convex optimization methods executed in parallel. | ||
650 | 0 | _aEngineering. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aApplied mathematics. | |
650 | 0 | _aEngineering mathematics. | |
650 | 1 | 4 | _aEngineering. |
650 | 2 | 4 | _aAppl.Mathematics/Computational Methods of Engineering. |
650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783540731917 |
830 | 0 |
_aStudies in Computational Intelligence, _x1860-949X ; _v74 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-540-73192-4 |
912 | _aZDB-2-ENG | ||
950 | _aEngineering (Springer-11647) | ||
999 |
_c509869 _d509869 |