Availability of Perfect Decomposition in Statistical Linkage Learning for Unitation-based Function Concatenations
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913751143284736 |
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| author | Prusik, Michal Frej, Bartosz Przewozniczek, Michal W. |
| author_facet | Prusik, Michal Frej, Bartosz Przewozniczek, Michal W. |
| contents | Statistical Linkage Learning (SLL) is a part of many state-of-the-art optimizers. The purpose of SLL is to discover variable interdependencies. It has been shown that the effectiveness of SLL-using optimizers is highly dependent on the quality of SLL-based problem decomposition. Thus, understanding what kind of problems are hard or easy to decompose by SLL is important for practice. In this work, we analytically estimate the size of a population sufficient for obtaining a perfect decomposition in case of concatenations of certain unitation-based functions. The experimental study confirms the accuracy of the proposed estimate. Finally, using the proposed estimate, we identify those problem types that may be considered hard for SLL-using optimizers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17397 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Availability of Perfect Decomposition in Statistical Linkage Learning for Unitation-based Function Concatenations Prusik, Michal Frej, Bartosz Przewozniczek, Michal W. Neural and Evolutionary Computing Machine Learning Optimization and Control Statistical Linkage Learning (SLL) is a part of many state-of-the-art optimizers. The purpose of SLL is to discover variable interdependencies. It has been shown that the effectiveness of SLL-using optimizers is highly dependent on the quality of SLL-based problem decomposition. Thus, understanding what kind of problems are hard or easy to decompose by SLL is important for practice. In this work, we analytically estimate the size of a population sufficient for obtaining a perfect decomposition in case of concatenations of certain unitation-based functions. The experimental study confirms the accuracy of the proposed estimate. Finally, using the proposed estimate, we identify those problem types that may be considered hard for SLL-using optimizers. |
| title | Availability of Perfect Decomposition in Statistical Linkage Learning for Unitation-based Function Concatenations |
| topic | Neural and Evolutionary Computing Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2503.17397 |