摘要
为了克服传统遗传算法搜索速度慢、易陷入局部最优解的缺陷,借鉴遗传算法的思想,利用云模型云滴的随机性和稳定倾向性的特点,提出了一种新的遗传算法———云遗传算法(CGA).该算法由正态云模型的Y条件云发生器实现交叉操作,由基本云发生器实现变异操作.最后,进行了函数优化实验,并与标准遗传算法(SGA)和自适应遗传算法(AGA)进行了比较,以证明其有效性.
To overcome the shortcomings of a genetic algorithm（GA）, i.e. , low convergence speed and easily getting a local optimum solution, a novel genetic algorithm, cloud theory-based genetic algorithm （CGA）, was proposed. CGA is based on both the idea of GA and the properties of randomness and stable tendency of a normal cloud model. In this algorithm, Y-conditional cloud generator is used as the cross operator, and basic cloud generator as the mutation operator. Finally, an experiment of function optimization was carried out and a comparison with standard genetic algorithm （SGA） and adaptive genetic algorithm （AGA） was made to testify the validity of CGA, the proposed genetic algorithm.
出处
《西南交通大学学报》
EI
CSCD
北大核心
2006年第6期729-732,共4页
Journal of Southwest Jiaotong University
关键词
遗传算法
云理论
云遗传算法
函数优化
genetic algorithm
cloud theory
cloud genetic algorithm
function optimization
