Class of improved genetic algorithm with sifting strategy and its performance analysis

To avoid premature convergence of genetic algorithm (GA) and to enhance the exploration and exploitation abilities, the sifting strategy is incorporated into classic elitist GA to maintain the population diversity. That is, some bad redundant individuals are deleted from the population according to the difference of population performance and location. Numerical simulation results based on benchmark complex functions show that the convergence rate and hitting probability on global optima of the proposed algorithm are greatly better than that of the classic method, and the improved algorithm is robust on its parameters as well.