Impact of spatial transformations on landscape features of CEC2022 basic benchmark problems
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910326896721920 |
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| author | Yin, Haoran Vermetten, Diederick Ye, Furong Bäck, Thomas H. W. Kononova, Anna V. |
| author_facet | Yin, Haoran Vermetten, Diederick Ye, Furong Bäck, Thomas H. W. Kononova, Anna V. |
| contents | When benchmarking optimization heuristics, we need to take care to avoid an algorithm exploiting biases in the construction of the used problems. One way in which this might be done is by providing different versions of each problem but with transformations applied to ensure the algorithms are equipped with mechanisms for successfully tackling a range of problems. In this paper, we investigate several of these problem transformations and show how they influence the low-level landscape features of a set of 5 problems from the CEC2022 benchmark suite. Our results highlight that even relatively small transformations can significantly alter the measured landscape features. This poses a wider question of what properties we want to preserve when creating problem transformations, and how to fairly measure them. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_07654 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Impact of spatial transformations on landscape features of CEC2022 basic benchmark problems Yin, Haoran Vermetten, Diederick Ye, Furong Bäck, Thomas H. W. Kononova, Anna V. Neural and Evolutionary Computing When benchmarking optimization heuristics, we need to take care to avoid an algorithm exploiting biases in the construction of the used problems. One way in which this might be done is by providing different versions of each problem but with transformations applied to ensure the algorithms are equipped with mechanisms for successfully tackling a range of problems. In this paper, we investigate several of these problem transformations and show how they influence the low-level landscape features of a set of 5 problems from the CEC2022 benchmark suite. Our results highlight that even relatively small transformations can significantly alter the measured landscape features. This poses a wider question of what properties we want to preserve when creating problem transformations, and how to fairly measure them. |
| title | Impact of spatial transformations on landscape features of CEC2022 basic benchmark problems |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2402.07654 |