The Function Optimization Based on Cloud Genetic Algorithm

Genetic algorithm is an optimization technology which is based on the theory of evolution,and using design method of the ge-netic combination,genetic mutation and natural selection.The selection of Genetic algorithm’s crossover probability and mutation proba-bility 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 gener-ate adaptive crossover probability and mutation probability.Function optimization experiments show that,the cloud genetic algorithm con-vergence requires fewer generations,convergence rate is faster than the standard genetic algorithm.