Research of improved genetic algorithm classification based on naive Bayesian algorithm

Aimed at the problems of instability and low accuracy in standard genetic algorithm,in order to improve the stability and accuracy of the genetic classification algorithm,based on theory of the Bayesian algorithm,a new method of genetic algorithm classification is presented.First,the initial sample set is divided into randomly groups of equal number.Second,select some samples of which the "discrimination" is relatively high from the initial sample set by the naive Bayesian algorithm as a new sample set.Third,the new sample set through the improved genetic algorithm is processed to get the optimal rule.Through the combination of two algorithms for data classification,the stability and accuracy of the classification are improved obviously.The result of simulation indicates that this algorithm has higher stability and accuracy.