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.

翻译：元算法（如算法选择、配置与调度）的实证研究通常依赖于大量且计算成本高昂的实验。由于实验设置与设计存在高度自由度，大量潜在误差源威胁着我们科学洞见的可扩展性与有效性。尽管元算法研究领域存在最佳实践，但它们分散在不同出版物与学科中，且持续各自独立发展。本报告汇集了COSEAL社区各子领域在元算法实证研究中的优良实践，涵盖完整实验周期：从研究问题构建与实验设计选择，到实验执行，最终至结果分析与公正呈现。报告确立了当前元算法研究领域的最前沿实践规范，为元算法领域的新晋研究者与实践者提供了系统指导。