Using support vector machines to learn the efficient set in multiple objective discrete optimization

Haldun Aytug and Serpil SayIn

European Journal of Operational Research, 2009, vol. 193, issue 2, 510-519

Abstract: We propose using support vector machines (SVMs) to learn the efficient set in multiple objective discrete optimization (MODO). We conjecture that a surface generated by SVM could provide a good approximation of the efficient set. As one way of testing this idea, we embed the SVM-approximated efficient set information into a Genetic Algorithm (GA). This is accomplished by using a SVM-based fitness function that guides the GA search. We implement our SVM-guided GA on the multiple objective knapsack and assignment problems. We observe that using SVM improves the performance of the GA compared to a benchmark distance based fitness function and may provide competitive results.

Keywords: Multiple; objective; optimization; Efficient; set; Machine; learning; Support; vector; machines (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:193:y:2009:i:2:p:510-519

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