Genetic algorithms for support vector machine optimization

Fred Gruber, Luis Rabelo, Serge Sala-Diakanda, Luis Robledo, Jose Sepulveda

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)


Support vector machines are relatively new approach for creating classifiers that have become increasingly popular in the machine learning community. They present several advantages over other methods like neural networks in areas like training speed, convergence, complexity control of the classifier, as well as a stronger mathematical background based on optimization and statistical learning theory. This paper deals with the problem of model selection with support vector machines, that is, the problem of finding the optimal parameters that will improve the performance of the algorithm. It is shown that genetic algorithms provide an effective way to find the optimal parameters for support vector machines. The proposed algorithm is compared with a backpropagation Neural Network in a dataset that represents the NASA Shuttle Columbia Debris database.

Original languageEnglish
Publication statusPublished - 1 Dec 2005
EventIIE Annual Conference and Exposition 2005 - Atlanta, GA, United States
Duration: 14 May 200518 May 2005


ConferenceIIE Annual Conference and Exposition 2005
Country/TerritoryUnited States
CityAtlanta, GA


  • Cross validation
  • Debris
  • Genetic algorithms
  • Support vector machines

ASJC Scopus subject areas

  • Engineering(all)


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