Biological Cybernetics
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
Evolutionary Computation: Towards a New Philosophy of Machine Intelligence
The application of antigenic search techniques to time series forecasting
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
ADANN: automatic design of artificial neural networks
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
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In this work new improvements from a previous approach of an Automatic Design of Artificial Neural Networks applied to forecast time series is tackled. The automatic process to design Artificial Neural Networks is carried out by a Genetic Algorithm. These improvements, in order to get an accurate forecasting, are related with: to shuffle train and test patterns obtained from time series values and improving the fitness function during the global learning process (i.e. Genetic Algorithm) using a new patterns set called validation apart of the two used till the moment (i.e. train and test). The object of this study is to try to improve the final forecasting getting an accurate system. Results of the Artificial Neural Networks got by our system to forecast a set of famous time series are shown.