摘要
遗传算法是基于进化理论，并采用遗传结合、遗传变异及自然选择等设计方法的优化技术。遗传算法中交叉概率和变异概率的选择是影响遗传算法行为和性能的关键所在，直接影响算法的收敛性。该文结合正态云模型云滴的随机性和稳定倾向性，由X条件云发生器产生自适应交叉概率和变异概率。函数优化实验结果表明，云遗传算法只需要较少的进化代数就可以收敛，收敛速度明显快于标准遗传算法。
Genetic algorithm is an optimization technology which is based on the theory of evolution, and using design method of the genetic combination, genetic mutation and natural selection. The selection of Genetic algorithm＇s crossover probability and mutation probability are the key which is influenced the behavior and performance of genetic algorithm, directly affects the convergence of the algorithm. This paper combines the normal cloud model cloud droplets of randomness and stable tendency, by X condition cloud generator to generate adaptive crossover probability and mutation probability. Function optimization experiments show that, the cloud genetic algorithm convergence requires fewer generations, convergence rate is faster than the standard genetic algorithm.
作者
吴立锋
WU Li-feng （Department of Computer Science and Technology, SCUFN, Wuhan 430074, China）
出处
《电脑知识与技术》
2011年第10期6951-6953,共3页
Computer Knowledge and Technology
基金
中南民族大学自然科学基金资助项目（YZQ09003）
关键词
云模型
遗传算法
函数优化
交叉
变异
cloud model
genetic algorithm
function optimization
crossover
mutation
