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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Bibliographic Details
Published in:Computers & operations research Vol. 139; p. 105615
Main Authors: de Souza, Marcelo, Ritt, Marcus, López-Ibáñez, Manuel
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 01.03.2022
Pergamon Press Inc
Subjects:
Algorithms
Automatic algorithm configuration
Capping
Capping methods
Configurations
Operations research
Optimization
Optimization algorithms
Parameter tuning
Automatic algorithm configuration
Optimization algorithms
Capping methods
Parameter tuning
ISSN:0305-0548, 1873-765X, 0305-0548
Online Access:Get full text
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Summary: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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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2021.105615