基于代沟信息的自适应遗传算法

针对现有自适应遗传算法无法兼顾群体特性 ,难以稳定地收敛到最优解的问题 ,从种群多样性和适应度均值变化的角度 ,分析了进化停滞或退化的原因 .以种群适应度均值和多样性作为概率调整依据 ,提出了一种新的基于种群代沟信息的自适应遗传算法 .利用相邻两代群体间的适应度差异和多样性差异信息 ,设计了遗传概率的自适应调整策略 ,使算法维持较好的多样性 ,有效避免了早熟 .并证明了算法收敛性 .仿真结果表明该算法能够使种群保持良好的可进化性和收敛性 .

Adaptive genetic algorithms based on generational gap information

Focusing on the problems existed in some adaptive genetic algorithms that cannot take acc ount of population features and are difficult to converge to optimal solution s tead ily, the reasons of stopping evolution or degeneration are analyzed in view of f itness average and population diversity. According to the fitness average and population diversity, a novel adaptive genetic algorithms based on generatio nal gap information is proposed. By using the information of fitness difference and diversity difference between two neighbour generations, a strategy for genet ic probability adjusting is designed. By this adaptive genetic algorithm, good d iversity can be remained and premature can be avoided effectively. Convergence o f the algorithm is proved. Simulation results show that the proposed algorithms can make the population remain good evolutivity and convergence.