An adaptive niche genetic algorithm by evolution grads
引入进化梯度的改进小生境遗传算法

To solve the problems of premature convergence and local minima in simple genetic algorithm (SGA), an evolutionary grad-included niche genetic algorithm (GNGA) was proposed. In the GNGA, evolutionary grad was used to improve the ability of finding the local best; the crossover value and mutation value were adapted dynamically with the generation so that the precision was improved; the population diversity was guaranteed by the use of the niche algorithm based on crowding mechanism. Simulation results show that this method has its superiority in precision and convergence rate compared with SGA.