The solution to the problem of circuit optimization is obtained on the basis of a combination of a genetic algorithm (GA) and the idea of generalized optimization, developed earlier for the deterministic case. It is shown that such a GA modification allows one to overcome premature convergence to local minima and to increase the minimization accuracy by several orders of magnitude. In this case, GA forms a set of populations determined by the fitness function, given in different way, depending on the strategy chosen within the framework of the idea of generalized optimization. The way of setting fitness functions as well as the length and structure of chromosomes, are determined by a control vector artificially introduced within the framework of generalized optimization. This vector determines the number of independent variables of the optimization problem and the method for calculating the fitness function. It allows you to build compound strategies that significantly increase the accuracy of the resulting solution. This, in turn, makes it possible to reduce the number of generations required during the operation of the GA and minimize the processor time for solving the problem of circuit optimization. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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