Journal
ADVANCED ENGINEERING INFORMATICS
Volume 16, Issue 3, Pages 193-203Publisher
ELSEVIER SCI LTD
DOI: 10.1016/S1474-0346(02)00011-3
Keywords
genetic algorithms; constraint-handling; multiobjective optimization; self-adaptation; evolutionary optimization; numerical optimization
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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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