Abstract
Recently, Genetic Algorithm has been studied as an effective approach for large scale optimization problems. However, we have issues of early convergence and settings of many parameters in GA approach to actual applications. In order to deal with such issues, parameter free genetic algorithm(PfGA) and distributed genetic algorithm(DGA) are proposed. In this paper, we proposed distributed parameter free genetic algorithm(DPfGA) that keeps a parameter free characteristic and improve efficiency of the optimization. Besides the distributed construction of GA, we proposed the method varying the number of offspring adaptively in accordance with the current performance of the optimization. We showed effectiveness of the algorithm through application of the algorithm to TSP(Travelling Salesman Problem).
