Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2014

Abstract

Several automated parameter tuning procedures/configurators have been proposed in order to find the best parameter setting for a target algorithm. These configurators can generally be classified into model-free and model-based approaches. We introduce a recent approach which is based on the hybridization of both approaches. It combines the Design of Experiments (DOE) and Response Surface Methodology (RSM) with prevailing model-free techniques. DOE is mainly used for determining the importance of parameters. A First Order-RSM is initially employed to define the promising region for the important parameters. A Second Order-RSM is then built to approximate the center point as well as the final promising ranges of parameter values. We show how our approach can be embedded with existing model-free techniques, namely ParamILS and Randomized Convex Search, to tune target algorithms and demonstrate that our proposed methodology leads to improvements in terms of the quality of the solutions compared against the earlier work.

Keywords

Design of Experiment, Parameter Tuning, Response Surface Methodology, Second Order Model

Discipline

Artificial Intelligence and Robotics | Computer Sciences | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Optimization

Publication

2014 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM): 9-12 December, Selangor: Proceedings

First Page

652

Last Page

656

ISBN

9781479964109

Identifier

10.1109/IEEM.2014.7058719

Publisher

IEEE

City or Country

Piscataway, NJ

Copyright Owner and License

Authors/LARC

Additional URL

https://doi.org/10.1109/IEEM.2014.7058719

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