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Abstract: another, so that it is not possible to improve on any of the objective functions without deteriorating at least one of the other objective functions. This is known as the concept of Pareto optimality (Rao, 1991). In multiobjective optimization, as opposed to single-objective optimization, there may not exist an unambigous optimal solution (global minimum or global maximum). Characteristic of the multiobjective optimization problems is a very large set of acceptable solutions that are superior... (Update)
Context of citations to this paper: More
...group and co operativeness between the two sex groups. Some attempts to formulate GAs featuring gender have already been proposed [2, 21, 22, 25]. However, in most cases, these simulations simply consist of adding an extra attribute of gender to the chromosome, and defining a...
...of the objectives will fail to generate Pareto optimal solutions for non convex search spaces regardless of weights used. Lis and Eiben[23] advocated the use of multiparent crossovers among individuals of different genders. One parent from each gender is selected to...
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BibTeX entry: (Update)
Joanna Lis and A. E. Eiben. A Multi-Sexual Genetic Algorithm for Multiobjective Optimization. In Toshio Fukuda and Takeshi Furuhashi, editors, Proceedings of the 1996 International Conference on Evolutionary Computation, pages 59--64, Nagoya, Japan, 1996. IEEE. http://citeseer.ist.psu.edu/lis96multisexual.html More
@inproceedings{ lis96multisexual,
author = "Joanna Lis and A. E. Eiben",
title = "A {M}ulti-{S}exual {G}enetic {A}lgorithm for {M}ultiobjective {O}ptimization",
booktitle = "Proceedings of the 1996 International Conference on Evolutionary Computation",
publisher = "IEEE",
address = "Nagoya, Japan",
editor = "Toshio Fukuda and Takeshi Furuhashi",
pages = "59--64",
year = "1996",
url = "citeseer.ist.psu.edu/lis96multisexual.html" }
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