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Abstract: The application of evolutionary algorithms in multiobjective optimization, proposed relatively early in their history, is currently receiving growing interest from researchers with various backgrounds. The paper reviews and discusses current multiobjective evolutionary approaches, identifying some of the issues raised by how they handle multiple objectives, such as how they affect the fitness landscape. Directions for future research are identified from the discussion. Keywords: evolutionary... (Update)
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BibTeX entry: (Update)
C. M. Fonseca and P. J. Fleming. An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation, 3(1):1--16, 1995. http://citeseer.ist.psu.edu/fonseca95overview.html More
@article{ fonseca95overview,
author = "Carlos M. Fonseca and Peter J. Fleming",
title = "An Overview of Evolutionary Algorithms in Multiobjective Optimization",
journal = "Evolutionary Computation",
volume = "3",
number = "1",
pages = "1-16",
year = "1995",
url = "citeseer.ist.psu.edu/fonseca95overview.html" }
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104 Some guidelines for genetic algorithms with penalty function.. (context) - Richardson, Palmer et al. - 1989
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58 Using genetic algorithms in engineering design optimization .. (context) - Powell, Skolnick - 1993
46 Genetic search strategies in multicriterion optimal design (context) - Hajela, Lin - 1992
44 Parallel Problem Solving from Nature (context) - Manner, Manderick - 1992
38 A variant of evolution strategies for vector optimization - Kursawe - 1991
36 Compaction of symbolic layout using genetic algorithms (context) - Fourman - 1985
33 A survey of evolution strategies (context) - Back, Hoffmeister et al. - 1991
29 Genetic algorithms for changing environments - Grefenstette - 1992
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7 The influence of variation and of developmental constraints .. (context) - Wagner - 1988
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5 Genetic Algorithms: Proceedings of the Fourth International .. (context) - Belew, Booker - 1991
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3 Goal setting and compromise solutions (context) - Shi, Yu - 1989
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