Publication: Using support vector machines to learn the efficient set in multiple objective discrete optimization
Program
KU-Authors
KU Authors
Co-Authors
Aytuğ, Haldun
Advisor
Publication Date
2009
Language
English
Type
Journal Article
Journal Title
Journal ISSN
Volume Title
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.
Description
Source:
European Journal of Operational Research
Publisher:
Elsevier
Keywords:
Subject
Management, Operations research and management science