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arXiv · 2606.02016

Evaluating Real-World Generalizability of Algorithm Selection Models

Gjorgjina Cenikj·Jakub Kudela·Eva Tuba·Tome Eftimov
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Abstract

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.

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Gjorgjina Cenikj, Jakub Kudela, Eva Tuba, Tome Eftimov. 2026-06-01. Evaluating Real-World Generalizability of Algorithm Selection Models. https://doi.org/10.1145/3795101.3805348

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