Title: A Novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regression
Authors: Wu, Chih-Hung
Tzeng, Gwo-Hshiung
Lin, Rong-Ho
Institute of Management of Technology
Keywords: Support vector regression (SVR);Hybrid genetic algorithm (HGA);Parameter optimization;Kernel function optimization;Electrical load forecasting;Forecasting accuracy
Issue Date: 1-Apr-2009
Abstract: This study developed a novel model, HCA-SVR, for type of kernel function and kernel parameter value optimization in support vector regression (SVR), which is then applied to forecast the maximum electrical daily load. A novel hybrid genetic algorithm (HGA) was adapted to search for the optimal type of kernel function and kernel parameter values of SVR to increase the accuracy of SVR. The proposed model was tested at an electricity load forecasting competition announced on the EUNITE network. The results showed that the new HGA-SVR model Outperforms the previous models. Specifically, the new HGA-SVR model can successfully identify the optimal type of kernel function and all the optimal values of the parameters of SVR with the lowest prediction error values in electricity load forecasting. Crown Copyright (C) 2008 Published by Elsevier Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.eswa.2008.06.046
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2008.06.046
Volume: 36
Issue: 3
Begin Page: 4725
End Page: 4735
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