The application of sophisticated evolutionary computing
approaches for solving complex problems with multiple conflicting
objectives in science and engineering have increased steadily in
the recent years. Within this growing trend, Memetic algorithms
are, perhaps, one of the most successful stories, having
demonstrated better efficacy in dealing with multi-objective
problems as compared to its conventional counterparts. Nonetheless,
researchers are only beginning to realize the vast potential of
multi-objective Memetic algorithm and there remain many open topics
in its design.
This book presents a very first comprehensive collection of
works, written by leading researchers in the field, and reflects
the current state-of-the-art in the theory and practice of
multi-objective Memetic algorithms. "Multi-Objective Memetic
algorithms" is organized 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 Memetic algorithms and multi-objective optimization.
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