Parallel genetic algorithm with parameter adaptation

This paper presents an adaptive algorithm that can adjust parameters of genetic algorithm according to the observed performance. The parameter adaptation occurs in parallel to the running of genetic algorithm. The proposed method is compared with the algorithms that use random parameter sets and a standard parameter set. It is shown to be the most promising method from two performance measurements: the reliability in finding the optimal solution and the time required for finding the optimal solution.