Abstract—Research into adjusting the probabilities of crossover and mutation � � in genetic algorithms (GAs) is one of the most significant and promising areas in evolutionary computation. � � and � � greatly determine whether the algorithm will find a near-optimum solution or whether it will find a solution efficiently. Instead of using fixed values of � � and ��, this paper presents the use of fuzzy logic to adaptively adjust the values of � � and � � in GA. By applying the u-means algorithm, distribution of the population in the search space is clustered in each generation. A fuzzy system is used to adjust the values of � � and ��. It is based on considering the relative size of the cluster containing the best chromosome and the one containing the worst chromosome. The proposed method has been applied to optimize a buck regulator that requires satisfying several static and dynamic operational requirements. The optimized circuit component values, the regulator’s performance, and the convergence rate in the training are favorably compared with the GA using fixed values of � � and ��. The effectiveness of the fuzzy-controlled crossover and mutation probabilities is also demonstrated by optimizing eight multidimensional mathematical functions. Index Terms—Evolutionary computation, fuzzy logics, genetic algorithms (GA), power electronics. I.

Abstract—This paper presents an implementation of a decoupled optimization technique for design of switching regulators using genetic algorithms (GAs). The optimization process entails the selection of component values in a switching regulator, in order to meet the static and dynamic requirements. Although the proposed method inherits characteristics of evolutionary computations that involve randomness, recombination, and survival of the fittest, it does not perform a whole-circuit optimization. Thus, intensive computations that are usually found in stochastic optimization techniques can be avoided. Similar to many design approaches for power electronics circuits, a regulator is decoupled into two components, namely the power conversion stage (PCS) and the feedback network (FN). The PCS is optimized with the required static characteristics, whilst the FN is optimized with the required static and dynamic behaviors of the whole system. Systematic optimization procedures will be described and the technique is illustrated with the design of a buck regulator with overcurrent protection. The predicted results are compared with the published results available in the literature and are verified with experimental measurements. Index Terms—Circuit optimization, circuit simulation, computer-aided design, genetic algorithms, power electronics. I.