The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
ParamILS: An Automatic Algorithm Configuration Framework
Open accessAbstract
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 algorithms 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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Cites
Improvement Strategies for the F-Race Algorithm: Sampling Design and Iterative Refinement
Balaprakash, P., Birattari, M., & Stützle, T. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-75514-2_9 · Cited by 197
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Cited by
Sequential Model-Based Optimization for General Algorithm Configuration
Hutter, F., Hoos, H. H., & Leyton-Brown, K. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-25566-3_40 · Cited by 2,469
Self-citationThe irace package: Iterated racing for automatic algorithm configuration
López-Ibáñez, M., Dubois-Lacoste, J., Pérez Cáceres, L., Birattari, M., & Stützle, T. (2016) · Operations Research Perspectives · doi:10.1016/j.orp.2016.09.002 · Cited by 1,761
Self-citationImproving the search performance of SHADE using linear population size reduction
Tanabe, R., & Fukunaga, A. S. (2014) · 2014 IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2014.6900380 · Cited by 1,625
Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms
Thornton, C., Hutter, F., Hoos, H. H., & Leyton-Brown, K. (2013) · Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2487575.2487629 · Cited by 1,359
Self-citationHyperparameter optimization: Foundations, algorithms, best practices, and open challenges
Bischl, B., Binder, M., Lang, M., Pielok, T., Richter, J., Coors, S., Thomas, J., Ullmann, T., Becker, M., Boulesteix, A., et al. (2023) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1484 · Cited by 930
Metadata
- Authors
- Hutter, F., Hoos, H. H., Leyton-Brown, K., & Stuetzle, T.
- Venue
- Journal of Artificial Intelligence Research
- Publisher
- AI Access Foundation
- Published
- 2009-10-30
- Volume
- 36
- Pages
- 267-306
- ISSN
- 1076-9757 (ISSN-L)
- OpenAlex
- W2950680102
- Cited by
- 882
- References
- 1
- FWCI
- 22.77
- Type
- journal article
- Language
- en
- Open access
- diamond
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- Hutter, F., Hoos, H. H., Leyton-Brown, K., & Stuetzle, T. (2009). ParamILS: An Automatic Algorithm Configuration Framework. Journal of Artificial Intelligence Research. https://doi.org/10.1613/jair.2861
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