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.
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