Evolutionary multi-objective optimization in uncertain environments : issues and algorithms
Author(s)
Bibliographic Information
Evolutionary multi-objective optimization in uncertain environments : issues and algorithms
（Studies in computational intelligence, v. 186）
Springer, c2009
Available at 3 libraries
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Library, Research Institute for Mathematical Sciences, Kyoto University数研
GOH||4||1200010694346
Note
Includes bibliographical references
Description and Table of Contents
Description
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.
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.
by "Nielsen BookData"