A hybrid GA-SA-BPNNs for human capital prediction of China regions

Abstract

Human capital formation and accelerating economic growth is a representative complex system which is not suitable to measure and forecast by classic linear statistical approaches. This paper presents an approach of fusing genetic algorithm (GA), simulated annealing (SA) and error back propagation neural networks (BPNNs) to predict human capital of China regions. Adopting multi-encoding, the GA-SA-BPNNs can simultaneously optimize the hidden nodes, transfer function, weights and bias of BP networks dynamically and adaptively. Furthermore,the most important factors of human capital formation can be identified by selecting input nodes.