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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.10976 |
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| _version_ | 1866911881025814528 |
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| author | Yoshikawa, Takushi Tanabe, Ryoji |
| author_facet | Yoshikawa, Takushi Tanabe, Ryoji |
| contents | Feature-based offline algorithm selection has shown its effectiveness in a wide range of optimization problems, including the black-box optimization problem. An algorithm selection system selects the most promising optimizer from an algorithm portfolio, which is a set of pre-defined optimizers. Thus, algorithm selection requires a well-constructed algorithm portfolio consisting of efficient optimizers complementary to each other. Although construction methods for the fixed-target setting have been well studied, those for the fixed-budget setting have received less attention. Here, the fixed-budget setting is generally used for computationally expensive optimization, where a budget of function evaluations is small. In this context, first, this paper points out some undesirable properties of experimental setups in previous studies. Then, this paper argues the importance of considering the number of function evaluations used in the sampling phase when constructing algorithm portfolios, whereas the previous studies ignored that. The results show that algorithm portfolios constructed by our approach perform significantly better than those by the previous approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_10976 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | On Constructing Algorithm Portfolios in Algorithm Selection for Computationally Expensive Black-box Optimization in the Fixed-budget Setting Yoshikawa, Takushi Tanabe, Ryoji Machine Learning Artificial Intelligence Feature-based offline algorithm selection has shown its effectiveness in a wide range of optimization problems, including the black-box optimization problem. An algorithm selection system selects the most promising optimizer from an algorithm portfolio, which is a set of pre-defined optimizers. Thus, algorithm selection requires a well-constructed algorithm portfolio consisting of efficient optimizers complementary to each other. Although construction methods for the fixed-target setting have been well studied, those for the fixed-budget setting have received less attention. Here, the fixed-budget setting is generally used for computationally expensive optimization, where a budget of function evaluations is small. In this context, first, this paper points out some undesirable properties of experimental setups in previous studies. Then, this paper argues the importance of considering the number of function evaluations used in the sampling phase when constructing algorithm portfolios, whereas the previous studies ignored that. The results show that algorithm portfolios constructed by our approach perform significantly better than those by the previous approach. |
| title | On Constructing Algorithm Portfolios in Algorithm Selection for Computationally Expensive Black-box Optimization in the Fixed-budget Setting |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2405.10976 |