Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
1-2013
Abstract
This paper is concerned with automated tuning of parameters of algorithms to handle heterogeneous and large instances. We propose an automated parameter tuning framework with the capability to provide instance-specific parameter configurations. We report preliminary results on the Quadratic Assignment Problem (QAP) and show that our framework provides a significant improvement on solutions qualities with much smaller tuning computational time.
Keywords
automated parameter tuning, instance-specific parameter configuration, parameter search space reduction, large instance parameter tuning
Discipline
Artificial Intelligence and Robotics | Business | Operations Research, Systems Engineering and Industrial Engineering
Publication
Learning and Intelligent Optimization: Proceedings of the 7th International Conference on Learning and Optimization, LION 7
Volume
7997
First Page
423
Last Page
437
ISBN
9783642449734
Identifier
10.1007/978-3-642-44973-4_45
Publisher
Springer Verlag
City or Country
Berlin
Citation
LINDAWATI, Linda; Yuan, Zhi; LAU, Hoong Chuin; and ZHU, Feida.
Automated Parameter Tuning Framework for Heterogeneous and Large Instances: Case study in Quadratic Assignment Problem. (2013). Learning and Intelligent Optimization: Proceedings of the 7th International Conference on Learning and Optimization, LION 7. 7997, 423-437.
Available at: https://ink.library.smu.edu.sg/sis_research/1657
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://dx.doi.org/10.1007/978-3-642-44973-4_45
Included in
Artificial Intelligence and Robotics Commons, Business Commons, Operations Research, Systems Engineering and Industrial Engineering Commons