总结元算法实证研究最佳实践，提升实验可复现性与科学性。
Best Practices For Empirical Meta-Algorithmic Research: Guidelines from the COSEAL Research Network
- 系统梳理元算法研究全流程的实验设计规范
- 涵盖从问题提出到结果呈现的完整研究周期
- 适合新手及从业者参考，避免常见实验错误
元算法研究（如算法选择、配置与调度）通常依赖大量计算密集型实验。由于实验设置和设计具有高度自由度，容易引入多种误差源，威胁研究结果的可扩展性与有效性。尽管已有部分最佳实践，但分散于不同文献与领域，且各自独立发展。本报告整合了COSEAL研究网络内各子领域的实证元算法研究最佳实践，覆盖整个实验周期：包括研究问题定义、实验设计选择、实验执行，以及结果分析与公正呈现。报告确立了当前元算法研究的前沿实践标准，为新研究人员和从业者提供实用指南。
原文摘要 · Abstract (English)
Empirical research on meta-algorithmics, such as algorithm selection, configuration, and scheduling, often relies on extensive and thus computationally expensive experiments. With the large degree of freedom we have over our experimental setup and design comes a plethora of possible error sources that threaten the scalability and validity of our scientific insights. Best practices for meta-algorithmic research exist, but they are scattered between different publications and fields, and continue to evolve separately from each other. In this report, we collect good practices for empirical meta-algorithmic research across the subfields of the COSEAL community, encompassing the entire experimental cycle: from formulating research questions and selecting an experimental design, to executing experiments, and ultimately, analyzing and presenting results impartially. It establishes the current state-of-the-art practices within meta-algorithmic research and serves as a guideline to both new researchers and practitioners in meta-algorithmic fields.
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