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The design of real electrical, electronic or electromagnetic complex systems fulfilling EMC constrains often exploits the performances of population based global optimizers. One of the main drawbacks of the adoption of these optimizers in the design of a real system is the difficulty in the introduction, in the optimized design algorithm, of all the heuristic knowledge already available in the field. In order to overcome this problem Bayesian optimization algorithms, classified as estimation of distribution algorithm, could be very effective, since they are based on the definition of the distribution of promising solutions by using the information extracted from the entire set of currently good solutions. Unfortunately, their straightforward implementations usually lack of exploration, and are easily trapped in local maxima. In order to overcome even this drawback and to develop a Bayesian optimization algorithm with both the required exploitation, of the heuristic knowledge, and the exploration, for avoiding local maxima, for system or subsystem design fulfilling EMC constrains, in this paper a modified BOA is proposed by adding a suitable mutation scheme to the traditional one in order to ensure the effectiveness of the algorithm. The here proposed new algorithm has been tested on some mathematical test functions and on a typical EM design problem, a microwave microstrip filter synthesis, to show its capability.