Evolutionary Multi-objective Optimization in Uncertain Environments Issues and Algorithms /

• Main Authors: Goh, Chi-Keong (Author), Tan, Kay Chen (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg, 2009.
• Series: Studies in Computational Intelligence, 186
• Subjects: Computer science.; Artificial intelligence.; Computer-aided engineering.; Applied mathematics.; Engineering mathematics.; Computer Science.; Computer-Aided Engineering (CAD, CAE) and Design.; Appl.Mathematics/Computational Methods of Engineering.; Artificial Intelligence (incl. Robotics).

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...

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.