Publication Type

Conference Proceeding Article

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

Research Areas

Intelligent Systems and Decision Analytics

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

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.1007/978-3-642-44973-4_45