Abstract
In this work an initial approach to design Artificial Neural Networks to forecast time series is tackle, and the automatic process to design is carried out by a Genetic Algorithm. A key issue for these kinds of approaches is what information is included in the chromosome that represents an Artificial Neural Network. There are two principal ideas about this question: first, the chromosome contains information about parameters of the topology, architecture, learning parameters, etc. of the Artificial Neural Network, i.e. Direct Encoding Scheme; second, the chromosome contains the necessary information so that a constructive method gives rise to an Artificial Neural Network topology (or architecture), i.e. Indirect Encoding Scheme. The results for a Direct Encoding Scheme (in order to compare with Indirect Encoding Schemes developed in future works) to design Artificial Neural Networks for NN3 Forecasting Time Series Competition are shown.
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Peralta, J., Gutierrez, G., Sanchis, A. (2007). Design of Artificial Neural Networks Based on Genetic Algorithms to Forecast Time Series. In: Corchado, E., Corchado, J.M., Abraham, A. (eds) Innovations in Hybrid Intelligent Systems. Advances in Soft Computing, vol 44. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74972-1_31
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DOI: https://doi.org/10.1007/978-3-540-74972-1_31
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