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

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  • Aytug, Haldun
  • SayIn, Serpil
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    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.

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    File URL: http://www.sciencedirect.com/science/article/B6VCT-4PMT2VX-3/2/6427479ce34517a463c93918c04938f0
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    Bibliographic Info

    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 193 (2009)
    Issue (Month): 2 (March)
    Pages: 510-519

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    Handle: RePEc:eee:ejores:v:193:y:2009:i:2:p:510-519

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    Web page: http://www.elsevier.com/locate/eor

    Related research

    Keywords: Multiple objective optimization Efficient set Machine learning Support vector machines;

    References

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    1. Serpil Say{\i}n & Panos Kouvelis, 2005. "The Multiobjective Discrete Optimization Problem: A Weighted Min-Max Two-Stage Optimization Approach and a Bicriteria Algorithm," Management Science, INFORMS, vol. 51(10), pages 1572-1581, October.
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    Cited by:
    1. Rong, Aiying & Figueira, José Rui, 2013. "A reduction dynamic programming algorithm for the bi-objective integer knapsack problem," European Journal of Operational Research, Elsevier, vol. 231(2), pages 299-313.
    2. Rong, Aiying & Figueira, José Rui, 2014. "Dynamic programming algorithms for the bi-objective integer knapsack problem," European Journal of Operational Research, Elsevier, vol. 236(1), pages 85-99.

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