基于多种编码的多群体遗传算法

Multi-species Genetic Algorithms Based on Multi-encoding

为了有效地克服标准遗传算法 (SGA)中的早熟收敛现象 ,提出了一种基于多种编码的多群体遗传算法 .该方法是采用 3个群体同时进行进化的策略 .其中 ,第 1个群体是采用浮点数编码方法 ,以使该群体具有较强的局部搜索能力 ;第 2个群体是采用二进制编码方法 ,以使该群体具有较强的全局搜索能力 ;第 3个群体为“精华种群”,用于保存算法在进化过程中产生的优秀个体 .在进化过程中 ,还通过引入“移民”策略来交换 3个群体中的优秀个体 ,以有效地增加群体的多样性 .该算法不仅不易陷入局部收敛 ,还具有较强的跳出局部收敛的能力 ,且收敛速度较快 .通过对一系列典型复杂多模函数进行的优化计算试验 ,结果证实了该方法的有效性和优越性 .

In this paper, a genetic algorithm using multi-species and multi-encoding method is proposed to combat premature convergence inherent in Standard Genetic Algorithms(SGA). It involves with three species evolved simultaneously. By using float encoding method and binary encoding method respectively, the first species has stronger local search ability and the second has stronger global search ability. The third species, which called 'elitist species',aims to keep the elitist individuals in the evolution process. And at the same time, it evolves too, which will enhances the convergence speed and improves the perfomance of GA. And the migration strategy adopted in the proposed method which immigrates elitist individuals among the three species can keep the population diversity efficiently. This multi-species method can help genetic algorithms to escape from possible local entrapment and obtain good tradeoff between exploration ability and exploitation ability. The experimental results of this method on a series of classical complex multimodal functions have shown its efficience and superiority.