Evaluating Real-World Generalizability of Algorithm Selection Models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911741167796224 |
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| author | Cenikj, Gjorgjina Kudela, Jakub Tuba, Eva Eftimov, Tome |
| author_facet | Cenikj, Gjorgjina Kudela, Jakub Tuba, Eva Eftimov, Tome |
| contents | Algorithm Selection (AS) aims to automatically identify the most suitable optimization algorithm for a given problem instance by leveraging measurable problem characteristics and historical performance data. In this study, we investigate the generalization ability of AS models across both synthetic and real-world optimization landscapes. We consider two widely used academic benchmark suites (BBOB and CEC) and two real-world problem sets (robotics trajectory optimization tasks and unmanned aerial vehicle path-planning problems). Through a systematic cross-benchmark evaluation, we analyze how AS models transfer between domains, identify where generalization succeeds or breaks down, and highlight the challenges that arise when applying AS in realistic, domain-specific contexts. Our findings provide insights into the robustness of current AS approaches and inform the development of more reliable, broadly applicable AS systems for real-world optimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_02016 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Evaluating Real-World Generalizability of Algorithm Selection Models Cenikj, Gjorgjina Kudela, Jakub Tuba, Eva Eftimov, Tome Machine Learning Algorithm Selection (AS) aims to automatically identify the most suitable optimization algorithm for a given problem instance by leveraging measurable problem characteristics and historical performance data. In this study, we investigate the generalization ability of AS models across both synthetic and real-world optimization landscapes. We consider two widely used academic benchmark suites (BBOB and CEC) and two real-world problem sets (robotics trajectory optimization tasks and unmanned aerial vehicle path-planning problems). Through a systematic cross-benchmark evaluation, we analyze how AS models transfer between domains, identify where generalization succeeds or breaks down, and highlight the challenges that arise when applying AS in realistic, domain-specific contexts. Our findings provide insights into the robustness of current AS approaches and inform the development of more reliable, broadly applicable AS systems for real-world optimization. |
| title | Evaluating Real-World Generalizability of Algorithm Selection Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2606.02016 |