基于协同进化的自适应遗传算法研究

针对传统遗传算法易于陷入局部最优解,性能不稳定的问题,提出了一种基于协同进化的自适应遗传算法(CEAGA)。在协同进化的两层框架模型的基础上,引入一个自适应的变异策略,改进了协同进化遗传算法中的局部进化操作,加强了在上层中的局部搜索;在下层,在种群之间采用协同进化算法,克服未成熟收敛,在种群内部进化中引入自适应遗传操作,保护种群中的优秀个体。实验验证CEAGA既具有很快的收敛速度,又具有很好的全局搜索性能。

Adaptive genetic algorithm based on co-evolution

For the problem of local optimum and the performance of stability,an adaptive genetic algorithm based on co-evo- lution is proposed.Adaptive probability of mutation is applied in local evolution and the capability of local search on the top floor is enhanced.On the bottom floor,co-evolution algorithm is used for solving the premature convergence among sub-populations,and adaptive genetic manipulation is used to protect outstanding individuals in sub-populations.The experi- ments demonstrate that this algorithm can increase the convergent speed and it has the ability of searching an optimum solution