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Using support vector machines to learn the efficient set in multiple objective discrete optimization
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DOI：10.1016/j.ejor.2007.09.002.png)
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Abstract
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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. (C) 2007 Elsevier B.V. All rights reserved.
Keywords:
Multiple objective optimization
Efficient set
Machine learning
Support vector machines
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