A BP neural network text categorization method optimized by an improved genetic algorithm

The back propagation(BP) neural network is widely used for text categorization and could achieve high performance. However, the greatest disadvantage of this network is its long training time. The genetic algorithm is often used to generate useful solutions for optimization. In this paper we combined the genetic algorithm and the back propagation neural network for text categorization. We use the genetic algorithm to optimize weights of connections in the back propagation neural network instead of back-propagating. At the same time, we improved the genetic algorithm to increase its efficiency. Through this method, we overcome the traditional disadvantage of the BP neural network. Our experiments show that our method outperforms the traditional method for text categorization.