Tuning the parameters of an artificial neural network using central composite design and genetic algorithm

An artificial Neural Network (ANN) is an efficient approach applied to solving a variety of problems. The main problem in using ANN is parameter tuning, because there is no definite and explicit method to select optimal parameters for the ANN parameters. In this study, three artificial neural network performance measuring criteria and also three important factors which affect the selected criteria have been studied. Moreover, central composite design has been used to design experiments and also analyze network behavior according to identified parameters, by using the overall desirability function. Then the Genetic Algorithm has been proposed to find optimal parameter status. For this purpose, the proposed method has been illustrated by the numerical example of a well known mathematical function. The results show that the designed ANN, according to the proposed procedure, has a better performance than other networks by random selected parameters and also parameters which are selected by the Taguchi method. In general, the proposed approach can be used for tuning neural network parameters in solving other problems.