A binary-encoded tabu-list genetic algorithm for fast support vector regression hyper-parameters tuning

J. Gascón-Moreno*, S. Salcedo-Sanz, E. G. Ortiz-García, L. Carro-Calvo, B. Saavedra-Moreno, J. A. Portilla-Figueras

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

The selection of hyper-parameters in support vector machines for regression (SVMr) is an essential step in the training process of these learning machines. Unfortunately, there is not an exact method to obtain the optimal values of SVM hyper-parameters. Therefore, it is necessary to use a search algorithm in order to find the best set of hyper-parameters. Grid Search is the most commonly used option to perform such a hyper-parameters search, though other possibilities based on evolutionary computation algorithms have been proposed in the literature. In this paper we analyze the use of a standard genetic algorithm with binary encoding, which allows a fast exploration of the hyper-parameters space. We include a kind of tabu-list in the proposed algorithm, where we keep the last individuals generated by the genetic algorithm to avoid re-training of the SVMr with them. This technique allows a good improvement of the SVMr training time respect to the grid search approach, while keeping the machine accuracy almost unaltered.

Original languageEnglish (US)
Title of host publicationProceedings of the 2011 11th International Conference on Intelligent Systems Design and Applications, ISDA'11
Pages1253-1257
Number of pages5
DOIs
StatePublished - 2011
Event2011 11th International Conference on Intelligent Systems Design and Applications, ISDA'11 - Cordoba, Spain
Duration: Nov 22 2011Nov 24 2011

Publication series

NameInternational Conference on Intelligent Systems Design and Applications, ISDA
ISSN (Print)2164-7143
ISSN (Electronic)2164-7151

Conference

Conference2011 11th International Conference on Intelligent Systems Design and Applications, ISDA'11
Country/TerritorySpain
CityCordoba
Period11/22/1111/24/11

Keywords

  • genetic algorithms
  • hyper-parameters estimation
  • Support Vector regression
  • tabu-list

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Signal Processing
  • Control and Systems Engineering

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