Evolutionary Multi-objective Optimization in Uncertain Environments Issues and Algorithms

Evolutionary Multi-objective Optimization in Uncertain Environments Issues and Algorithms / by Chi-Keong Goh, Kay Chen Tan.

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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Bibliographic Details
Main Authors: Goh, Chi-Keong (Author), Tan, Kay Chen (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2009.
Edition:1st ed. 2009.
Series:Studies in Computational Intelligence, 186
Springer eBook Collection.
Subjects:
Computer-aided engineering.
Applied mathematics.
Engineering mathematics.
Artificial intelligence.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • 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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