摘要
为了克服传统遗传算法收敛速度慢和容易陷入局部最优的不足，提出了一种新的自适应免疫遗传算法SIGA（Self-adaptive Immune Genetic Algorithm）。新算法对遗传算子进行改进，提出了自适应交叉和变异算子，保证了种群多样性和防止早熟现象发生；为了使免疫算子兼顾个体多样性和提高种群个体适应度的水平，提出了基于相似性矢量距离的免疫选择算法。实验表明，与传统的遗传算法和免疫算法相比，该算法收敛速度提高了3～90倍，求解精度达到10^-3，并有效地抑制了早熟现象。
This paper proposed a novel self-adaptive genetic algorithm SIGA （Self-adaptive Immune Genetic Algorithm） based on immunity tO overcome the shortage of traditional genetic algorithms that the converging speed is slow and the solution is a local optimum. The algorithm improved the genetic operators and proposed self-adaptive crossover and mutation operators in case of keeping individual diversity and avoiding prematurity; proposed an immune selection algorithm based on selection probability of similarity and vector distance in order to keep individual diversity and improve the level of fitness. The results of the experiments indicate that SIGA can improve the conver- ging speed by three to ninety times, enhance the precision which reaches to 10^-3, and avoid prematurity to some extent compared with traditional genetic algorithms and immune algorithms.
出处
《中山大学学报（自然科学版）》
CAS
CSCD
北大核心
2008年第3期6-9,共4页
Acta Scientiarum Naturalium Universitatis Sunyatseni
基金
国家自然科学基金资助项目(60773169)
四川省青年软件创新工程资助项目(2007AA0032)
关键词
自适应
免疫遗传算法
免疫选择
早熟
self-adaptive
immune genetic algorithm
immune selection
prematurity
