Abstract
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.
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Aguirre, A.H., Rionda, S.B., Coello Coello, C.A., Lizárraga, G.L. (2003). Use of Multiobjective Optimization Concepts to Handle Constraints in Single-Objective Optimization. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45105-6_69
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DOI: https://doi.org/10.1007/3-540-45105-6_69
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