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Using regression models for characterizing and comparing black box
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DOI：10.1016/j.swevo.2021.100981.png)
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摘要
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Characterizing black box optimization problems is critical for improved benchmarking and subsequent automation of algorithm selection and configuration. Existing approaches in exploratory landscape analysis are based on heterogeneous features that can create difficulties in applying the analysis and validating the results. In this paper, we propose a simple yet powerful method of comparing problems using regression models. Gaussian processes are used as a flexible regression model, providing an approximate characterization of the problem landscape. We use the difference between regression models of different problem instances as a measure of distance between the problems. The goodness of fit of the regression models allows us to validate the approximation that the model makes of the problem. This can subsequently be used to determine an appropriate sample size to provide a good representation of the problem. We apply our model-based framework to explore the problem relationships in the BBOB benchmark problem set. The results demonstrate that the framework is effective at representing the problem similarities in relatively high dimensions. We also evaluate the method on a set of clustering problems where a known ranking of similarity is imposed. The results show that the framework is very effective in recovering the problem similarities.
Keyword:
Continuous black-box optimization
Problem comparison
Exploratory landscape analysis
Gaussian processes
期刊
IF:
8.5
论文数:
2.2K
被引数:
1.0W
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Towards improved benchmarking of black-box optimization algorithms using clustering problems.png)
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