Hybrid genetic algorithms and support vector regression in forecasting atmospheric corrosion of metallic materials

Abstract

A novel methodology hybridizing genetic algorithms (GAs) and support vector regression (SVR) and capable of forecasting atmospheric corrosion of metallic materials such as zinc and steel has been proposed and tested. Available techniques of data mining of the atmospheric corrosion of zinc and steel are used to examine the forecasting capability of the model. In order to improve predictive accuracy and generalization ability, GAs are adopted to automatically determine the optimal hyper-parameters for SVR. The performance of the hybrid model (GAs + SVR = GASVR) and the artificial neural network (ANN) has been compared with the experimental values. The result shows that the hybrid model provides better prediction capability and is therefore considered as a promising alternative method for forecasting atmospheric corrosion of zinc and steel.