Research on the Optimized Support Vector Regression Machines Based on the Differential Evolution Algorithm

The Support Vector Regression machine (SVR) is an effective tool to solve the problem of nonlinear prediction, but its prediction accuracy and generalization performances depend on the selection of parameters greatly. And the parameters selection is a procedure of global optimization search. Since the Differential Evolution (DE) population-based algorithm is a real coding optimal algorithm with powerful global searching capacity, a hybrid model of DE-SVR based on the standard SVR model and DE algorithm is proposed in this paper. And then, the new hybrid implementation was applied to the short range regression prediction of the chaotic time series. At last, the experiment results showed the effectiveness of this approach and the better performance in searching time, compared with the conventional parameters searching approach of grid algorithm.