Use of Multiobjective Optimization Concepts to Handle Constraints in Single-Objective Optimization Arturo Hern´ andez Aguirre , Salvador Botello Rionda , Carlos A. Coello Coello and Giovanni Liz´ arraga Liz´ arraga Center for Research in Mathematics (CIMAT) Department of Computer Science Guanajuato, Gto. 36240, M´ exico artha,botello,giovanni@cimat.mx CINVESTAV-IPN Evolutionary Computation Group Depto. de Ingenier´ ıa El´ ectrica Secci´ on de Computaci´ on Av. Instituto Polit´ ecnico Nacional No. 2508 Col. San Pedro Zacatenco M´ exico, D. F. 07300 ccoello@cs.cinvestav.mx 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 incorpo- rates 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 re- spect to which it was compared at a lower computational cost. 1 Introduction The success of Evolutionary Algorithms (EAs) in global optimization has triggered a considerable amount of research regarding the development of mechanisms able to incorporate information about the constraints of a problem into the fitness function of the EA used to optimize it [7]. So far, the most common approach adopted in the evolutionary optimization literature to deal with constrained search spaces is the use of penalty functions [10]. Despite the popularity of penalty functions, they have several drawbacks from which the main one is that they require a careful fine tuning of the penalty factors that indicates the degree of penalization to be applied [12]. Recently, some researchers have suggested the use of multiobjective optimization concepts to handle constraints in EAs. This paper introduces a new approach that is based on an evolution strategy that was originally proposed for multiobjective opti- mization: the Pareto Archived Evolution Strategy (PAES) [5]. Our approach (which is an extension of PAES) can be used to handle constraints in single-objective opti- mization problems and does not present the scalability problems of the original PAES.