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 the GA approach. In order to deal with such issues, parameter free genetic algorithm(PfGA) and distributed genetic algorithm(DGA) were proposed. In this paper, we propose a distributed parameter free genetic algorithm(DPfGA) that keeps parameter free characteristic and improves efficiency of optimization. Besides the distributed construction of GA, we propose the method varying the number of offspring adaptively in accordance with the current performance of optimization. We show effectiveness of the algorithm through application of the algorithm to TSP(Travelling Salesman Problem).
