Multi Objective Optimization Using Evolutionary Algorithms von Kalyanmoy Deb (Autor), Deb Kalyanmoy (Autor)

Kalyanmoy Deb (Autor), Deb Kalyanmoy (Autor)

ISBN 10: 9814126853 / ISBN 13: 9789814126854

Edité par Wiley-VCH Verlag Auflage: 1st (2001), 2001

A propos de cet article

Multi Objective Optimization Using Evolutionary Algorithms von Kalyanmoy Deb (Autor), Deb Kalyanmoy (Autor) In englischer Sprache. 518 pages.

N° de réf. du vendeur BN21651

Détails bibliographiques

• Titre: Multi Objective Optimization Using Evolutionary Algorithms von Kalyanmoy Deb (Autor), Deb Kalyanmoy (Autor)
• Éditeur: Wiley-VCH Verlag Auflage: 1st (2001)
• Date de parution: 2001
• Langue: anglais
• ISBN à 10 chiffres: 9814126853
• ISBN à 13 chiffres: 9789814126854
• Reliure: Softcover
• Édition: Auflage: 1st (2001).
• État de l'article: gut

A propos de ce titre

Quatrième de couverture

Multi–Objective Optimization using Evolutionary Algorithms

Kalyanmoy Deb
Indian Institute of Technology, Kanpur, India

The Wiley Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians and scientists.

Multi–Objective Optimization using Evolutionary Algorithms

Kalyanmoy Deb
Indian Institute of Technology, Kanpur, India

Evolutionary algorithms are very powerful techniques used to find solutions to 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.

1. Comrephensive coverage of this growing area of research.
2. Carefully introduces each algorithm with examples and in–depth discussion.
3. Includes many applications to real–world problems, including engineering design and scheduling.
4. Includes discussion of advanced topics and future research.
5. Accessible to those with limited knowledge of multi–objective optimization and evolutionary algorithms

Provides an extensive discussion on the principles of multi–objective optimization and on a number of classical approaches.

This integrated presentation of theory, algorithms and examples will benefit those working in the areas of optimization, optimal design and evolutionary computing.