Research on Hybrid Algorithm Based on BP Neural Network and Genetic Algorithm

BP neural network can approximate nonlinear functions in any degree of accuracy. However, it exhibits the shortcoming of slow convergence, and it is easy to fall into local minimum value. The genetic algorithm has academic advantages both at home and abroad. On the basis of analysis on features of genetic algorithm and BP neural network, the explanations on the necessity of combining genetic algorithm and neural network are provided. In order to improve the structure and setting parameters of neural network, a hybrid algorithm which can optimize BP neural network based on genetic algorithm is proposed. Hybrid algorithm optimizes the weights of BP neural network by an improved genetic algorithm, and makes up the neural network with global random search, which has the capability of genetic algorithm and likely falls into the local optimal solution. Meanwhile, the changes of traditional mechanisms on the same generation crossover in genetic algorithm, and the use of parent and offspring cross to avoid premature loss of evolution ability of genetic algorithm are also discussed in this paper. The algorithm is validated by using standard data sets wine and letterrecognition of UCI database. The results show that the hybrid algorithm proposed in this paper can improve the convergence speed of BP neural network and speed up the training process of this network.