ParamILS: An Automatic Algorithm Configuration Framework

The identification of performance-optimizing parameter settings is an important part of the development and application of algorithms. We describe an automatic framework for this algorithm configuration problem. More formally, we provide methods for optimizing a target algorithm’s performance on a g...

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Bibliographic Details
Published in:The Journal of artificial intelligence research Vol. 36; pp. 267 - 306
Main Authors: Hutter, F., Hoos, H. H., Leyton-Brown, K., Stuetzle, T.
Format: Journal Article
Language:English
Published: San Francisco AI Access Foundation 30.10.2009
Subjects:
Algorithms
Artificial intelligence
Configurations
Integer programming
Mixed integer
Parameter identification
ISSN:1076-9757, 1076-9757, 1943-5037
Online Access:Get full text
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Summary:The identification of performance-optimizing parameter settings is an important part of the development and application of algorithms. We describe an automatic framework for this algorithm configuration problem. More formally, we provide methods for optimizing a target algorithm’s performance on a given class of problem instances by varying a set of ordinal and/or categorical parameters. We review a family of local-search-based algorithm configuration procedures and present novel techniques for accelerating them by adaptively limiting the time spent for evaluating individual configurations. We describe the results of a comprehensive experimental evaluation of our methods, based on the configuration of prominent complete and incomplete algorithms for SAT. We also present what is, to our knowledge, the first published work on automatically configuring the CPLEX mixed integer programming solver. All the algorithms we considered had default parameter settings that were manually identified with considerable effort. Nevertheless, using our automated algorithm configuration procedures, we achieved substantial and consistent performance improvements.
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ISSN:1076-9757
1076-9757
1943-5037
DOI:10.1613/jair.2861