000 | 03504nam a22005775i 4500 | ||
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001 | 978-3-540-32373-0 | ||
003 | DE-He213 | ||
005 | 20161121231021.0 | ||
007 | cr nn 008mamaa | ||
008 | 100806s2005 gw | s |||| 0|eng d | ||
020 |
_a9783540323730 _9978-3-540-32373-0 |
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024 | 7 |
_a10.1007/b10910 _2doi |
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050 | 4 | _aQA75.5-76.95 | |
072 | 7 |
_aUY _2bicssc |
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072 | 7 |
_aUYA _2bicssc |
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072 | 7 |
_aCOM014000 _2bisacsh |
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072 | 7 |
_aCOM031000 _2bisacsh |
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082 | 0 | 4 |
_a004.0151 _223 |
100 | 1 |
_aPelikan, Martin. _eauthor. |
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245 | 1 | 0 |
_aHierarchical Bayesian Optimization Algorithm _h[electronic resource] : _bToward a new Generation of Evolutionary Algorithms / _cby Martin Pelikan. |
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2005. |
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300 |
_aXVIII, 166 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 Fuzziness and Soft Computing, _x1434-9922 ; _v170 |
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505 | 0 | _aFrom Genetic Variation to Probabilistic Modeling -- Probabilistic Model-Building Genetic Algorithms -- Bayesian Optimization Algorithm -- Scalability Analysis -- The Challenge of Hierarchical Difficulty -- Hierarchical Bayesian Optimization Algorithm -- Hierarchical BOA in the Real World. | |
520 | _aThis book provides a framework for the design of competent optimization techniques by combining advanced evolutionary algorithms with state-of-the-art machine learning techniques. The book focuses on two algorithms that replace traditional variation operators of evolutionary algorithms by learning and sampling Bayesian networks: the Bayesian optimization algorithm (BOA) and the hierarchical BOA (hBOA). BOA and hBOA are theoretically and empirically shown to provide robust and scalable solution for broad classes of nearly decomposable and hierarchical problems. A theoretical model is developed that estimates the scalability and adequate parameter settings for BOA and hBOA. The performance of BOA and hBOA is analyzed on a number of artificial problems of bounded difficulty designed to test BOA and hBOA on the boundary of their design envelope. The algorithms are also extensively tested on two interesting classes of real-world problems: MAXSAT and Ising spin glasses with periodic boundary conditions in two and three dimensions. Experimental results validate the theoretical model and confirm that BOA and hBOA provide robust and scalable solution for nearly decomposable and hierarchical problems with only little problem-specific information. | ||
650 | 0 | _aComputer science. | |
650 | 0 | _aComputer programming. | |
650 | 0 | _aComputers. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aApplied mathematics. | |
650 | 0 | _aEngineering mathematics. | |
650 | 0 | _aAlgorithms. | |
650 | 1 | 4 | _aComputer Science. |
650 | 2 | 4 | _aTheory of Computation. |
650 | 2 | 4 | _aAppl.Mathematics/Computational Methods of Engineering. |
650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
650 | 2 | 4 | _aProgramming Techniques. |
650 | 2 | 4 | _aAlgorithms. |
650 | 2 | 4 | _aApplications of Mathematics. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783540237747 |
830 | 0 |
_aStudies in Fuzziness and Soft Computing, _x1434-9922 ; _v170 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/b10910 |
912 | _aZDB-2-ENG | ||
950 | _aEngineering (Springer-11647) | ||
999 |
_c507357 _d507357 |