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Evolutionary algorithms for solving multi-objective problems
Solving multi-objective problems is an evolving effort, and computer science and other related disciplines have given rise to many powerful deterministic and stochastic techniques for addressing these large-dimensional optimization problems. Evolutionary algorithms are one such generic stochastic ap...
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| Auteurs principaux : | , , |
|---|---|
| Format : | Livre |
| Langue : | anglais |
| Titre complet : | Evolutionary algorithms for solving multi-objective problems / Carlos A. Coello Coello, Gary B. Lamont, David A. Van Velduizen |
| Édition : | 2nd edition |
| Publié : |
New York :
Springer
, cop. 2007 |
| Description matérielle : | 1 vol. (XXI-800 p.) |
| Collection : | Genetic and evolutionary computation |
| Sujets : |
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| 200 | 1 | |a Evolutionary algorithms for solving multi-objective problems |b Texte imprimé |f Carlos A. Coello Coello, Gary B. Lamont, David A. Van Velduizen | |
| 205 | |a 2nd edition | ||
| 210 | |a New York |c Springer |d cop. 2007 | ||
| 215 | |a 1 vol. (XXI-800 p.) |c fig. |d 25 cm | ||
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| 330 | |a Solving multi-objective problems is an evolving effort, and computer science and other related disciplines have given rise to many powerful deterministic and stochastic techniques for addressing these large-dimensional optimization problems. Evolutionary algorithms are one such generic stochastic approach that has proven to be successful and widely applicable in solving both single-objective and multi-objective problems | ||
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