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A multi-objective evolutionary algorithm that explicitly introduces the management of a single or multiple constraints on the solutions of an electromagnetic problem is presented in this paper. The proposed strategy is based upon a modified genetic algorithm which evaluates the strength of a solution by considering both the match to the desired performance (objectives) as well as the satisfaction of specific requirements imposed to the design (constraints). The key issue is represented by the adaptive constraints management and its influence on the selective pressure of the genetic algorithm. The procedure is applied to the optimization of concentric ring arrays. Several design examples of steerable and wideband arrays are provided to prove the flexibility and reliability of the approach. A particular emphasis is given to the changes in array performance when the same objectives are requested but different kinds of constraints are forced.