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| 008 | 140715s2013 gw | s |||| 0|eng d | ||
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_a9783642173394 _9978-3-642-17339-4 |
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_a10.1007/978-3-642-17339-4 _2doi |
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| 035 | _ato000484268 | ||
| 040 |
_aSpringer _cSpringer _dRU-ToGU |
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_aCOM031000 _2bisacsh |
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_a004.0151 _223 |
| 100 | 1 |
_aJansen, Thomas. _eauthor. _9332668 |
|
| 245 | 1 | 0 |
_aAnalyzing Evolutionary Algorithms _h[electronic resource] : _bThe Computer Science Perspective / _cby Thomas Jansen. |
| 260 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2013. |
||
| 300 |
_aX, 255 p. 19 illus. _bonline resource. |
||
| 336 |
_atext _btxt _2rdacontent |
||
| 337 |
_acomputer _bc _2rdamedia |
||
| 338 |
_aonline resource _bcr _2rdacarrier |
||
| 490 | 1 |
_aNatural Computing Series, _x1619-7127 |
|
| 505 | 0 | _aIntroduction -- Evolutionary Algorithms and Other Randomized Search Heuristics -- Theoretical Perspectives on Evolutionay Algorithms -- General Limits in Black-Box Optimization -- Methods for the Analysis of Evolutionary Algorithms -- Selected Topics in the Analysis of Evolutionary Algorithms -- App. A, Landau Notation -- App. B, Tail Estimations -- App. C, Martingales and Applications. | |
| 520 | _aEvolutionary algorithms is a class of randomized heuristics inspired by natural evolution. They are applied in many different contexts, in particular in optimization, and analysis of such algorithms has seen tremendous advances in recent years. In this book the author provides an introduction to the methods used to analyze evolutionary algorithms and other randomized search heuristics. He starts with an algorithmic and modular perspective and gives guidelines for the design of evolutionary algorithms. He then places the approach in the broader research context with a chapter on theoretical perspectives. By adopting a complexity-theoretical perspective, he derives general limitations for black-box optimization, yielding lower bounds on the performance of evolutionary algorithms, and then develops general methods for deriving upper and lower bounds step by step. This main part is followed by a chapter covering practical applications of these methods. The notational and mathematical basics are covered in an appendix, the results presented are derived in detail, and each chapter ends with detailed comments and pointers to further reading. So the book is a useful reference for both graduate students and researchers engaged with the theoretical analysis of such algorithms. | ||
| 650 | 0 |
_aComputer Science. _9155490 |
|
| 650 | 0 |
_aInformation theory. _9303504 |
|
| 650 | 0 |
_aArtificial intelligence. _9274099 |
|
| 650 | 0 |
_aMathematical optimization. _9566241 |
|
| 650 | 0 |
_aengineering. _9224332 |
|
| 650 | 1 | 4 |
_aComputer Science. _9155490 |
| 650 | 2 | 4 |
_aTheory of Computation. _9303507 |
| 650 | 2 | 4 |
_aComputational Intelligence. _9307538 |
| 650 | 2 | 4 |
_aoptimization. _9566242 |
| 650 | 2 | 4 |
_aArtificial Intelligence (incl. Robotics). _9274102 |
| 710 | 2 |
_aSpringerLink (Online service) _9143950 |
|
| 773 | 0 | _tSpringer eBooks | |
| 830 | 0 |
_aNatural Computing Series, _9318767 |
|
| 856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-642-17339-4 |
| 912 | _aZDB-2-SCS | ||
| 999 | _c355885 | ||