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Abstract: In this paper, a new evolutionary multiobjective optimization algorithm is proposed. The approach is based on a micro genetic algorithm (micro-GA) which is a genetic algorithm with a very small population (four individuals were used in our experiment) and a reinitialization process. We use three forms of elitism and a memory to generate the initial population of the micro-GA. Our approach is tested with several standard functions found in the specialized literature. The results obtained are... (Update)
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BibTeX entry: (Update)
@incollection{ coello01microgenetic,
author = "Carlos A. {Coello Coello} and Gregorio {Toscano Pulido}",
title = "A {M}icro-{G}enetic {A}lgorithm for {M}ultiobjective {O}ptimization",
booktitle = "First International Conference on Evolutionary Multi-Criterion Optimization",
publisher = "Springer-Verlag. Lecture Notes in Computer Science No. 1993",
editor = "Eckart Zitzler and Kalyanmoy Deb and Lothar Thiele and Carlos A. Coello Coello and David Corne",
pages = "126--140",
year = "2001",
url = "citeseer.ist.psu.edu/375223.html" }
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99 Comparison of Multiobjective Evolutionary Algorithms: Empiri.. - Zitzler, Deb et al. - 2000 DBLP
91 A Comprehensive Survey of Evolutionary-Based Multiobjective .. - Coello - 1999 DBLP
66 Approximating the Nondominated Front Using the Pareto Archiv.. - Knowles, Corne - 2000 DBLP
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