Capping methods for the automatic configuration of optimization algorithms
Automatic configuration techniques are widely and successfully used to find good parameter settings for optimization algorithms. Configuration is costly, because it is necessary to evaluate many configurations on different instances. For decision problems, when the objective is to minimize the runni...
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| Published in: | Computers & operations research Vol. 139; p. 105615 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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New York
Elsevier Ltd
01.03.2022
Pergamon Press Inc |
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| ISSN: | 0305-0548, 1873-765X, 0305-0548 |
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| Abstract | Automatic configuration techniques are widely and successfully used to find good parameter settings for optimization algorithms. Configuration is costly, because it is necessary to evaluate many configurations on different instances. For decision problems, when the objective is to minimize the running time of the algorithm, many configurators implement capping methods to discard poor configurations early. Such methods are not directly applicable to optimization problems, when the objective is to optimize the cost of the best solution found, given a predefined running time limit. We propose new capping methods for the automatic configuration of optimization algorithms. They use the previous executions to determine a performance envelope, which is used to evaluate new executions and cap those that do not satisfy the envelope conditions. We integrate the capping methods into the irace configurator and evaluate them on different optimization scenarios. Our results show that the proposed methods can save from about 5% to 78% of the configuration effort, while finding configurations of the same quality. Based on the computational analysis, we identify two conservative and two aggressive methods, that save an average of about 20% and 45% of the configuration effort, respectively. We also provide evidence that capping can help to better use the available budget in scenarios with a configuration time limit.
•Novel capping methods for configuring optimization algorithms.•An integration of these methods with the irace configurator.•An experimental study conducted on six configuration scenarios.•Capping reduces the configuration effort and maintains its quality.•Capping works for both scenarios with budget as total executions and as maximum time. |
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| AbstractList | Automatic configuration techniques are widely and successfully used to find good parameter settings for optimization algorithms. Configuration is costly, because it is necessary to evaluate many configurations on different instances. For decision problems, when the objective is to minimize the running time of the algorithm, many configurators implement capping methods to discard poor configurations early. Such methods are not directly applicable to optimization problems, when the objective is to optimize the cost of the best solution found, given a predefined running time limit. We propose new capping methods for the automatic configuration of optimization algorithms. They use the previous executions to determine a performance envelope, which is used to evaluate new executions and cap those that do not satisfy the envelope conditions. We integrate the capping methods into the irace configurator and evaluate them on different optimization scenarios. Our results show that the proposed methods can save from about 5% to 78% of the configuration effort, while finding configurations of the same quality. Based on the computational analysis, we identify two conservative and two aggressive methods, that save an average of about 20% and 45% of the configuration effort, respectively. We also provide evidence that capping can help to better use the available budget in scenarios with a configuration time limit.
•Novel capping methods for configuring optimization algorithms.•An integration of these methods with the irace configurator.•An experimental study conducted on six configuration scenarios.•Capping reduces the configuration effort and maintains its quality.•Capping works for both scenarios with budget as total executions and as maximum time. Automatic configuration techniques are widely and successfully used to find good parameter settings for optimization algorithms. Configuration is costly, because it is necessary to evaluate many configurations on different instances. For decision problems, when the objective is to minimize the running time of the algorithm, many configurators implement capping methods to discard poor configurations early. Such methods are not directly applicable to optimization problems, when the objective is to optimize the cost of the best solution found, given a predefined running time limit. We propose new capping methods for the automatic configuration of optimization algorithms. They use the previous executions to determine a performance envelope, which is used to evaluate new executions and cap those that do not satisfy the envelope conditions. We integrate the capping methods into the irace configurator and evaluate them on different optimization scenarios. Our results show that the proposed methods can save from about 5% to 78% of the configuration effort, while finding configurations of the same quality. Based on the computational analysis, we identify two conservative and two aggressive methods, that save an average of about 20% and 45% of the configuration effort, respectively. We also provide evidence that capping can help to better use the available budget in scenarios with a configuration time limit. |
| ArticleNumber | 105615 |
| Author | Ritt, Marcus López-Ibáñez, Manuel de Souza, Marcelo |
| Author_xml | – sequence: 1 givenname: Marcelo surname: de Souza fullname: de Souza, Marcelo email: marcelo.desouza@udesc.br organization: Department of Software Engineering, Santa Catarina State University, Brazil – sequence: 2 givenname: Marcus surname: Ritt fullname: Ritt, Marcus email: marcus.ritt@inf.ufrgs.br organization: Institute of Informatics, Federal University of Rio Grande do Sul, Brazil – sequence: 3 givenname: Manuel surname: López-Ibáñez fullname: López-Ibáñez, Manuel email: manuel.lopez-ibanez@uma.es organization: School of Computer Science, University of Málaga, Spain |
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| CitedBy_id | crossref_primary_10_1109_TEVC_2024_3352450 crossref_primary_10_1145_3653024 crossref_primary_10_1016_j_cor_2024_106836 crossref_primary_10_3390_app12052396 crossref_primary_10_1016_j_ejor_2025_07_024 |
| Cites_doi | 10.1016/j.ejor.2013.10.043 10.1016/j.artint.2015.11.002 10.1007/s11721-015-0106-x 10.1016/j.ejor.2016.04.030 10.1007/s11721-015-0110-1 10.1016/S0377-2217(99)00284-2 10.1080/00401706.1964.10490181 10.1109/TEVC.2011.2182651 10.1007/s10288-009-0115-y 10.1016/j.ejor.2012.07.012 10.1080/01621459.1961.10482090 10.1287/ijoc.11.4.345 10.1287/mnsc.44.3.388 10.1613/jair.2861 10.1007/s12532-008-0001-1 10.1057/jors.1987.70 10.1007/s11590-017-1192-z 10.1007/s10878-014-9734-0 10.1016/j.ejor.2019.01.018 10.1023/B:ANOR.0000039519.03572.08 10.1016/j.cor.2018.12.015 10.15388/Informatica.2006.138 10.1287/ijoc.15.3.284.16077 10.1287/opre.21.2.498 10.1023/A:1009823419804 10.1007/s12532-009-0004-6 |
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| Keywords | Automatic algorithm configuration Optimization algorithms Capping methods Parameter tuning |
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