Parallel genetic algorithm with parameter adaptation

Abstract This paper presents an adaptive algorithm that can adjust parameters of a genetic algorithm according to the observed performance. The parameter adaptation occurs in parallel to the running of the genetic algorithm. The proposed method is compared with the algorithms that use random parameter sets and a standard parameter set. The experimental results show that the proposed method offers two advantages over the other competing methods: the reliability in finding the optimal solution and the time required for finding the optimal solution.

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