评估算法选择模型在真实场景中的泛化能力，发现其跨领域表现不稳定。
Evaluating Real-World Generalizability of Algorithm Selection Models
- 对比合成与真实优化问题，测试算法选择模型的跨域适应性。
- 在机器人轨迹与无人机路径规划中，模型泛化效果显著下降。
- 为构建更可靠的现实优化系统提供实证依据，适合优化研究者参考。
算法选择（AS）旨在通过可测量的问题特征和历史性能数据，自动识别最适合特定问题实例的优化算法。本研究系统考察了AS模型在合成与真实世界优化景观中的泛化能力。我们采用两个广泛使用的学术基准套件（BBOB和CEC）以及两个真实世界问题集（机器人轨迹优化任务与无人机路径规划问题）。通过跨基准的系统评估，分析了AS模型在不同领域间的迁移表现，揭示了泛化成功或失效的关键节点，并指出了在具体应用中面临的挑战。研究结果为当前AS方法的鲁棒性提供了洞见，有助于推动更可靠、普适性强的现实优化算法选择系统的发展。
原文摘要 · Abstract (English)
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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