Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
481131 | European Journal of Operational Research | 2009 | 10 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Haldun Aytuğ, Serpil Sayın,