Using Racing to Automatically Configure Algorithms for Scaling Performance
Automated algorithm configuration has been proven to be an effective approach for achieving improved performance of solvers for many computationally hard problems. Following our previous work, we consider the challenging situation where the kind of problem instances for which we desire optimised per...
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Published in | Learning and Intelligent Optimization Vol. 7997; pp. 382 - 388 |
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Main Authors | , |
Format | Book Chapter |
Language | English |
Published |
Germany
Springer Berlin / Heidelberg
2013
Springer Berlin Heidelberg |
Series | Lecture Notes in Computer Science |
Subjects | |
Online Access | Get full text |
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Summary: | Automated algorithm configuration has been proven to be an effective approach for achieving improved performance of solvers for many computationally hard problems. Following our previous work, we consider the challenging situation where the kind of problem instances for which we desire optimised performance are too difficult to be used during the configuration process. In this work, we propose a novel combination of racing techniques with existing algorithm configurators to meet this challenge. We demonstrate that the resulting algorithm configuration protocol achieves better results than previous approaches and in many cases closely matches the bound on performance obtained using an oracle selector. An extended version of this paper can be found at www.cs.ubc.ca/labs/beta/Projects/Config4Scaling. |
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ISBN: | 9783642449727 3642449727 |
ISSN: | 0302-9743 1611-3349 |
DOI: | 10.1007/978-3-642-44973-4_41 |