Taguchi-Based Parameter Designing of Genetic Algorithm for Artificial Neural Network Training
Najmeh Sadat Jaddi, Salwani Abdullah, Abdul Razak Hamdan · 2013
A number of properties of Artificial Neural Networks (ANNs) make them suitable for many applications such as time series prediction problem. However, lack of training model which finds a global optimal set of weights has been disadvantaged in some real-world problems. Genetic algorithm is an optimization procedure which is superior at exploring a search space in an intelligent method. In this paper we present a genetic-based algorithm to optimize the weights and biases of the ANN. In this work we tune the parameters of the genetic algorithm using Taguchi method. To test the method two standard time series prediction problems are employed. The results are compared to the methods in the literature. The comparison showed the superiority of the proposed method.