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

Placeholder

Organizational Units

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

Citation

Endorsement

Review

Supplemented By

Referenced By

Copy Rights Note

0

Views

0

Downloads

View PlumX Details