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...

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Détails bibliographiques
Cote:Libro Electrónico
Auteurs principaux: Goh, Chi-Keong (Auteur), Tan, Kay Chen (Auteur)
Collectivité auteur: SpringerLink (Online service)
Format: Électronique eBook
Langue:Inglés
Publié: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2009.
Édition:1st ed. 2009.
Collection:Studies in Computational Intelligence, 186
Sujets:
Computer-aided engineering.
Engineering mathematics.
Engineering-Data processing.
Artificial intelligence.
Computer-Aided Engineering (CAD, CAE) and Design.
Mathematical and Computational Engineering Applications.
Artificial Intelligence.
Accès en ligne:Texto Completo
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.

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