The Growing and Pruning Radial Basis Function (GAP-RBF) network is a promising sequential learning algorithm for prediction analysis, but the parameter selection of such a network is usually a non-convex problem and makes it difficult to handle. In this paper, a hybrid bio-inspired intelligent algorithm is proposed to optimize GAP-RBF. In specific, the excellent local convergence of PSO and the extensive search ability of GA are both considered to optimize the weights and bias term of GAP-RBF. Meanwhile, a competitive mechanism is proposed to make the hybrid algorithm choose the appropriate individuals for effective search and further improve its optimiation ability. Moreover, a Decoupled EKF (DEKF) method is introduced in this study to reduce the size of error covariance matrix and decrease the computational complexity for performing real-time predictions. In the experiments, three classic forecasting issues including abalone age, Boston house price and auto MPG are adopted for extensive test, and the experimental results show that our method performs better than PSO and GA these two single bio-inspired optimization algorithms. What is more, our method via DEKF achieves the better results in comparison with the state-of-art sequential learning algorithms, such as GAP-RBF, MRAN, RANEKF and RAN.