Evolutionary multi-objective optimization in uncertain environments : issues and algorithms
Evolutionary algorithms are sophisticated search methods that have been found to be very efficient and effective in solving complex real-world multi-objective problems where conventional optimization tools fail to work well. Despite the tremendous amount of work done in the development of these algo...
Gardado en:
| Auteurs principaux: | , |
|---|---|
| Formato: | Livre numérique |
| Idioma: | Anglais |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
2009.
Cham : Springer Nature |
| Series: | Studies in Computational Intelligence
186 |
| Acceso en liña: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Evolutionary Multi-objective Optimization in Uncertain Environments, Texte imprimé, 9783540959755 • Evolutionary Multi-objective Optimization in Uncertain Environments, Texte imprimé, 9783642101137 • Evolutionary Multi-objective Optimization in Uncertain Environments, Texte imprimé, 9783642001246 |
Table des matières:
- I: Evolving Solution Sets in the Presence of Noise
- Noisy Evolutionary Multi-objective Optimization
- Handling Noise in Evolutionary Multi-objective Optimization
- Handling Noise in Evolutionary Neural Network Design
- II: Tracking Dynamic Multi-objective Landscapes
- Dynamic Evolutionary Multi-objective Optimization
- A Coevolutionary Paradigm for Dynamic Multi-Objective Optimization
- III: Evolving Robust Solution Sets
- Robust Evolutionary Multi-objective Optimization
- Evolving Robust Solutions in Multi-Objective Optimization
- Evolving Robust Routes
- Final Thoughts.