Chi-Keong Goh, Kay Chen Tan (auth.)9783540959755, 3540959750
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 :
Front Matter….Pages –
Introduction….Pages 1-40
Front Matter….Pages 41-41
Noisy Evolutionary Multi-objective Optimization….Pages 43-54
Handling Noise in Evolutionary Multi-objective Optimization….Pages 55-99
Handling Noise in Evolutionary Neural Network Design….Pages 101-121
Front Matter….Pages 123-123
Dynamic Evolutionary Multi-objective Optimization….Pages 125-152
A Coevolutionary Paradigm for Dynamic Multi-Objective Optimization….Pages 153-185
Front Matter….Pages 187-187
Robust Evolutionary Multi-objective Optimization….Pages 189-211
Evolving Robust Solutions in Multi-Objective Optimization….Pages 213-227
Evolving Robust Routes….Pages 229-247
Final Thoughts….Pages 249-251
Back Matter….Pages –
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