Requirements for optimizations are not only to find an optimal solution in sufficiently long computation time but also to find a reasonably superior solution in shorter time, especially for real optimization problems. In this paper, we propose a real-coded genetic algorithm that finds the reasonably superior solution efficiently in short computation time. In genetic algorithms, the population size is an important parameter related to the population diversity and convergence speed. By switching the population size from a small value to a large value, the proposed method finds the reasonably superior solution rapidly.
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