期刊
ADVANCED ENGINEERING INFORMATICS
卷 16, 期 3, 页码 193-203出版社
ELSEVIER SCI LTD
DOI: 10.1016/S1474-0346(02)00011-3
关键词
genetic algorithms; constraint-handling; multiobjective optimization; self-adaptation; evolutionary optimization; numerical optimization
In this paper, we propose a dominance-based selection scheme to incorporate constraints into the fitness function of a genetic algorithm used for global optimization. The approach does not require the use of a penalty function and, unlike traditional evolutionary multiobjective optimization techniques, it does not require niching (or any other similar approach) to maintain diversity in the population. We validated the algorithm using several test functions taken from the specialized literature on evolutionary optimization. The results obtained indicate that the approach is a viable alternative to the traditional penalty function, mainly in engineering optimization problems. (C) 2002 Elsevier Science Ltd. All rights reserved.
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