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 algorithms in the past decade, many researchers assume that the optimization problems are deterministic and uncertainties are rarely examined. The primary motivation of this book is to provide a comprehensive introduction on the design and application of evolutionary algorithms for multi-objective optimization in the presence of uncertainties. In this book, we hope to expose the readers to a range of optimization issues and concepts, and to encourage a greater degree of appreciation of evolutionary computation techniques and the exploration of new ideas that can better handle uncertainties. "Evolutionary Multi-Objective Optimization in Uncertain Environments: Issues and Algorithms" is intended for a wide readership and will be a valuable reference for engineers, researchers, senior undergraduates and graduate students who are interested in the areas of evolutionary multi-objective optimization and uncertainties. 

 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.