引入进化梯度的改进小生境遗传算法

An adaptive niche genetic algorithm by evolution grads

针对基本遗传算法易于早熟及局部寻优能力较差等不足,提出了一种引入进化梯度的改进小生境混合遗传算法(GNGA)。利用进化梯度信息调整个体向更优解进化,并根据进化代数自适应调整实数编码个体的交叉量和变异量,增强了局部寻优能力和解的精度。基于排挤的小生境算法的引入,保持了种群的个体多样性以克服早熟。在Shubert函数上的仿真结果表明,与小生境遗传算法相比该算法能有效提高解的精度及收敛速度,找到更多最优解。

To solve the problems of premature convergence and local minima in simple genetic algorithm （ SGA）, an evolutionary gad-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.