Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Design and Analysis of Experiments
Design and Analysis of Experiments
A hybrid method for tuning neural network for time series forecasting
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Combining artificial neural network and particle swarm system for time series forecasting
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A prime step in the time series forecasting with hybrid methods: the function choice
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Tuning of the structure and parameters of a neural network using an improved genetic algorithm
IEEE Transactions on Neural Networks
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Artificial Neural Networks (ANN) have been widely used in order to solve the time series forecasting problem and one of its most promising approach is the combination with other intelligent techniques, such as genetic algorithms, evolutionary strategies, etc. The choice of a good fitness function still an open question for the practitioners who use these techniques to solve the forecasting problem. The effectiveness and efficiency of the fitness functions proposed in the literature have not been compared among them. Based on five well-known (in the literature) measures of statistical errors and using three non linear time series, this paper empirically compares distinct fitness functions (instead of conventional MSE based ones). They are analysed using two hybrid methods for tuning ANN structure and parameters (a simplified but still realistic method called GRASPES and a modified genetic Algorithm).