Abstract
This paper presents a performance comparative of GA-PAR SIMONY methodology with five well-known regression algorithms and with different genetic algorithm (GA) configurations. This approach is mainly based on combining GA and feature selection (FS) during model tuning process to achieve better overall parsimonious models that assure good generalization capacities. For this purpose, individuals, already sorted by their fitness function, are rearranged in each iteration depending on the model complexity. The main objective is to analyze the overall model performance achieve with this methodology for each regression algorithm against different real databases and varying the GA setting parameters. Our preliminary results show that two algorithms, multilayer perceptron (MLP) with the Broyden-Fletcher-Goldfarb-Shanno training method and support vector machines for regression (SVR) with radial basis function kernel, performing better with similar features reduction when database has low number of input attributes (\(\lesssim32\)) and it has been used low GA population sizes.
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Urraca-Valle, R., Sodupe-Ortega, E., Antoñanzas Torres, J., Antoñanzas-Torres, F., Martínez-de-Pisón, F.J. (2014). An Overall Performance Comparative of GA-PARSIMONY Methodology with Regression Algorithms. In: de la Puerta, J., et al. International Joint Conference SOCO’14-CISIS’14-ICEUTE’14. Advances in Intelligent Systems and Computing, vol 299. Springer, Cham. https://doi.org/10.1007/978-3-319-07995-0_6
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DOI: https://doi.org/10.1007/978-3-319-07995-0_6
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