An effective feature selection scheme via genetic algorithm using mutual information

In the artificial neural networks (ANNs), feature selection is a well-researched problem, which can improve the network performance and speed up the training of the network. The statistical-based methods and the artificial intelligence-based methods have been widely used to feature selection, and the latter are more attractive. In this paper, using genetic algorithm (GA) combining with mutual information (MI) to evolve a nearoptimal input feature subset for ANNs is proposed, in which mutual information between each input and each output of the data set is employed in mutation in evolutionary process to purposefully guide search direction based on some criterions. By examining the forecasting at the Australian Bureau of Meteorology, the simulation of three different methods of feature selection shows that the proposed method can reduce the dimensionality of inputs, speed up the training of the network and get better performance.