基于进化稳定策略的遗传算法

A Genetic Algorithm Based on Evolutionarily Stable Strategy

为了解决传统遗传算法易陷入局部最优解的问题,在借鉴生物学中进化稳定策略的基础上,对传统的遗传算法进行了改进,提出了基于进化稳定策略的遗传算法.该算法的核心在于,稳定参数控制下的突变算子的构造,通过稳定参数的设定来稳定种群中最优个体的数目,并有目标地对最优个体进行突变操作,以达到快速扩大搜索空间、稳定群体中个体多样性的目的.仿真结果表明,该算法有效地避免了传统遗传算法中因选择压力过大造成早熟现象的发生,显著地提高了GA对全局最优解的搜索能力和收敛速度.这将使GA在众多实际的优[(\273\257\316\312\314\342\311\317\276\337\323\320\270\374\271\343\267\272\)0(\265\304\323\246\323\303\307\260\276\260)].

An improved genetic algorithm based on the evolutionarily stable strategy is proposed to avoid the problem of local optimum. The key to this algorithm lies in the construction of a new mutation operator controlled by a stable factor,, which maintains the polymorphism in the colony by setting a stable factor and changing certain best seeds to mutant. Therefore, the operator can keep the number of the best individuals at a stable level when it enlarges the search space. The simulation experiments show that this algorithm can effectively avoid the premature convergence problem caused by the high selective pressure. Moreover, this algorithm improves the ability of searching an optimum solution and increases the convergent speed. This algorithm has extensive application prospects in many practical optimization problems.