An Overall Performance Comparative of GA-PARSIMONY Methodology with Regression Algorithms

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 299)


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.


Genetic Algorithm Tuning Modeling Feature Selection Parsimony Criterion Model Comparative 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.EDMANS Group, Department of Mechanical EngineeringUniversity of La RiojaLogroñoSpain

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