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Abstract: Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mid-eighties in an attempt to stochastically solve problems of this generic class. During the past decade, a variety of multiobjective EA (MOEA) techniques have been proposed and applied to many scientific and engineering applications. Our discussion's intent is to rigorously define multiobjective... (Update)
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BibTeX entry: (Update)
D. A. Van Veldhuizen and G. B. Lamont. Multiobjective evolutionary algorithms: Analyzing the state-ofart. Evolutionary Computation, 8(2):125-147, 2000. http://citeseer.ist.psu.edu/vanveldhuizen00multiobjective.html More
@article{ vanveldhuizen00multiobjective,
author = "David A. van Veldhuizen and Gary B. Lamont",
title = "Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art",
journal = "Evolutionary Computation",
volume = "8",
number = "2",
pages = "125-147",
year = "2000",
url = "citeseer.ist.psu.edu/vanveldhuizen00multiobjective.html" }
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