抄録
Genetic Algorithms (GAs) are well known as adaptive heuristic search algorithm to find approximate solutions. However for GAs, the problem of the premature convergence and falling in the local solution also need to be solved. Although we have proposed Fuzzy Adaptive Search method for Parallel Genetic Algorithm based on diversity measure as the improvement method, its performance depended on what is the type of applied problem. Therefore, in this paper we use history of evolution to improve the robustness of proposed method. Simulation results are also further presented to show the effectiveness and performance of method we proposed in this paper.
