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 ex- periments, 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）的经验性研究通常依赖于广泛且计算成本高昂的实验。由于实验设置与设计存在大量自由度，随之而来的是众多可能威胁科学见解可扩展性与有效性的误差来源。尽管元算法研究的最佳实践已存在，但它们分散在不同出版物与研究领域中，且持续各自独立发展。本报告汇集了COSEAL社区各子领域在经验性元算法研究中的良好实践，涵盖完整实验周期：从提出研究问题与选择实验设计，到执行实验，最终公正地分析与呈现结果。报告确立了当前元算法研究中的前沿实践标准，旨在为元算法领域的新研究人员与实践者提供指导。