In this paper, we propose a new constraint-handling technique for evolutionary algorithms which is based on multiobjective optimization concepts. The approach uses Pareto dominance as its selection criterion, and it incorporates a secondary population. The new technique is compared with respect to an approach representative of the state-of-the-art in the area using a well-known benchmark for evolutionary constrained optimization. Results indicate that the proposed approach is able to match and even outperform the technique with respect to which it was compared at a lower computational cost. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Aguirre, A. H., Rionda, S. B., Coello Coello, C. A., & Lizárraga, G. (2003). Use of multiobjective optimization concepts to handle constraints in single-objective optimization. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2723, 573–584. https://doi.org/10.1007/3-540-45105-6_69
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