Multi-objective optimization using evolutionary algorithms / Kalyanmoy Deb [ Livre]
Langue: Anglais ; de l'oeuvre originale, Anglais.Publication : New York : Wiley, 2001Description : XIX-515 p. ; 25 cmISBN: 047187339X.Collection: Wiley-Interscience series in systems and optimizationClassification: F 2 Analyse des algorithmes et complexitéRésumé: Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run. Comprehensive coverage of this growing area of research Carefully introduces each algorithm with examples and in-depth discussion Includes many applications to real-world problems, including engineering design and scheduling Includes discussion of advanced topics and future research Can be used as a course text or for self-study Accessible to those with limited knowledge of classical multi-objective optimization and evolutionary algorithms The integrated presentation of theory, algorithms and examples will benefit those working and researching in the areas of optimization, optimal design and evolutionary computing. This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study..Sujet - Nom commun: Programmation évolutionnaire | Optimisation mathématique | Décision multicritère| Current location | Call number | Status | Notes | Date due | Barcode |
|---|---|---|---|---|---|
| ENS Rennes - Bibliothèque Informatique | F 2 DEB (Browse shelf) | Checked out | F 2 Analyse des algorithmes et complexité | 27/11/2019 | 00008937 |
Bibliogr. p. [489]-508. Index
Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run.
Comprehensive coverage of this growing area of research
Carefully introduces each algorithm with examples and in-depth discussion
Includes many applications to real-world problems, including engineering design and scheduling
Includes discussion of advanced topics and future research
Can be used as a course text or for self-study
Accessible to those with limited knowledge of classical multi-objective optimization and evolutionary algorithms
The integrated presentation of theory, algorithms and examples will benefit those working and researching in the areas of optimization, optimal design and evolutionary computing. This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.