Short-term vegetable prices forecast based on improved gene expression programming
Lei Yang, Kangshun Li, Wensheng Zhang, Yaolang Kong · International Journal of High Performance Computing and Networking · 2018
Gene expression programming (GEP) is a new evolutionary algorithm based on genotype and phenotype, which is commonly used in network optimisation and prediction. Aiming at the problem that the traditional gene expression programming (GEP) is susceptible to noise interference, leading to premature convergence and falling into local solutions, this paper proposes an improved GEP algorithm, which increases 'inverted series' and 'extraction' operator. The improved algorithm can effectively increase the rate of utilisation of genes, with convergence speed and solution precision becoming higher, and can avoid the premature phenomenon. Taking the Chinese vegetables price change trend of mooli, scallion, white gourd, eggplant, green pepper and potato as example, this paper discusses the way to solve the forecast problems by adopting gene expression programming and constructing time interval unified time series data and normalised processing. Through the training and construction model, it realises the simulation and forecast of price trend. The experimental results show that the improved GEP algorithm has fast calculation speed and high forecast precision.