一种模糊自适应遗传算法

New Fuzzy Self-Tuning Genetic Algorithm

为克服标准遗传算法的早熟现象,提高算法的全局收敛性和收敛速度,采用并行遗传算法的思想,将整个种群分为几个子种群,分别用不同的遗传算子进行遗传操作;并根据它们各自对进化的贡献,利用模糊推理的方法,对其所作用的子种群的规模作出调整.对函数优化的仿真结果表明,该算法能较好地克服早熟现象,取得较为满意的优化效果.

To overcome the drawback of early maturing of the classical genetic algorithm (GA) and improve its global convergency and convergency speed, a new fuzzy self-tuning genetic algorithm was proposed. In the new algorithm, the overall population is divided into several sub-populations and each sub-population has its own operators. Fuzzy reasoning is applied to give effective operators more opportunity to search under the condition of keeping the overall population size unchanged. The fuzzy reasoning can sense the contributions of these operators and then decides their population size. Simulation result of function optimization shows that with the proposed algorithm, the phenomenon of the early maturing can be effectively overcome, and a satisfying optimization result can be obtained.