A Hybrid Model based on Neural Network and Hybrid Genetic Algorithm for Automotive Price Forecasting

Abstract

In this paper, we introduce a new intelligent combination method based on Multilayer Perceptron Neural Network (MLP‐NN) and Hybrid Genetic Algorithm (HGA) for automotive price forecasting. The combination of MLPNN and HGA lead us to accelerate convergence to the optimal weights and improve the forecasting performance. In this structure, the Levenberg‐ Marquardt (LM) algorithm is employed for training of the network, and the hybridization of Genetic Algorithm (GA) with some local search optimization techniques such as steepest descent (SD) method and quasi‐Newton methods with DFP and BFGS formula is used to perform HGA. We apply our new hybrid model to forecast the automotive prices in Iran Khodro Company which is the biggest automotive manufacturing in IRAN. Simulation results show the enough reduction in the processing iterations and forecasting error which is mean square error. The results are well promising compared to the cases when we apply MLP‐NN or hybridization of MLP‐NN and GA, individually.