Using Genetic Algorithm to Optimize Artificial Neural Network: A Case Study on Earthquake Prediction

By integrating the global searching advantage of Genetic Algorithm (GA) and the local searching ability of BP Artificial Neural Network (BP ANN), this paper proposes a new model of BP ANN based on GA (called GA-BP ANN). Firstly, it applies GA to optimize the initial interconnecting weights and thresholds of BP ANN. Then, it utilizes the BP algorithm to train the neural network more accurately. This method can speed up the convergence and avoid local minimum of BP ANN. The experiments of earthquake prediction with general BP ANN and optimized GA-BP ANN are respectively conducted as a case study. The results show that the BP ANN optimized with GA can not only get better network configurations, but also improve the efficiency, precision and stability of earthquake prediction.