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Topology optimization using the binary information of magnetic material is one of the most attractive simulations for the conceptual design of electrical machines. Heuristic algorithms based on random search allow engineers to define general-purpose objects with various constraint conditions. However, it is difficult for topology optimization to realize a practical solution without island and void distribution. In this paper, we propose a hybrid evolutionary algorithm that is composed of a genetic algorithm (GA) and extended compact GA (ECGA). We verify the effectiveness of the proposed algorithm on the binary topology optimization problem for the design of magnetostatic shielding.