Using genetic algorithms to select architecture of a feedforward artificial neural network
AbstractThis paper proposes a model selection methodology for feedforward network models based on the genetic algorithms and makes a number of distinct but inter-related contributions to the model selection literature for the feedforward networks. First, we construct a genetic algorithm which can search for the global optimum of an arbitrary function as the output of a feedforward network model. Second, we allow the genetic algorithm to evolve the type of inputs, the number of hidden units and the connection structure between the inputs and the output layers. Third, we study how introduction of a local elitist procedure which we call the election operator affects the algorithm's performance. We conduct a Monte Carlo simulation to study the sensitiveness of the global approximation properties of the studied genetic algorithm. Finally, we apply the proposed methodology to the daily foreign exchange returns.
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Bibliographic InfoArticle provided by Elsevier in its journal Physica A: Statistical Mechanics and its Applications.
Volume (Year): 289 (2001)
Issue (Month): 3 ()
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Web page: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/
Genetic algorithms; Neural networks; Model selection;
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- Preminger, Arie & Franck, Raphael, 2007.
"Forecasting exchange rates: A robust regression approach,"
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Elsevier, vol. 23(1), pages 71-84.
- PREMINGER, Arie & FRANCK, Raphael, 2005. "Forecasting exchange rates: a robust regression approach," CORE Discussion Papers 2005025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- PREMINGER, Arie & FRANCK, Raphael, . "Forecasting exchange rates: a robust regression approach," CORE Discussion Papers RP -1917, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Roberto Baragona & Domenico Cucina, 2013. "Multivariate Self-Exciting Threshold Autoregressive Modeling by Genetic Algorithms," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 233(1), pages 3-21, January.
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