Gray编码对族群进化算法性能的改进

Improving performance of ethnic group evolution algorithm by Gray code

族群进化算法(EGEA)利用族群机制进行群体结构调控。在基于二进制编码的群体中,个体间编码的差异性被作为族群聚类的标准。由于自然二进制编码所存在的Hamming悬崖问题易影响族群聚类的准确性,从而降低EGEA的搜索效率,因此提出利用Gray编码连续个体间编码只有一位不同的特点来改进族群聚类的精度。针对典型多维函数的仿真实验表明,基于Gray编码的族群聚类过程可显著提高EGEA的收敛速度和解的精度。

The Ethnic Group Evolution Algorithm （EGEA） has used ethnic group mechanism, a kind of populationstructured technology, to control the evolution tendency of population; meanwhile, it has used the binary code similarity among individuals to be the ethnic group clustering criterion. Because the hamming cliff problem of nature binary code was likely to affect the accuracy of ethnic group clustering, we proposed to make use of gray code to improve the evolution efficiency of EGEA. The simulations of numerical optimization show the EGEA based on gray code can improve the searching speed and the solution precision greatly.