Best Practices For Empirical Meta-Algorithmic Research: Guidelines from the COSEAL Research Network
This provides a consolidated guideline for new researchers and practitioners in meta-algorithmic fields, addressing scalability and validity issues in experiments, but it is incremental as it compiles existing practices.
The paper tackles the problem of scattered and evolving best practices in empirical meta-algorithmic research by collecting and establishing current state-of-the-art guidelines across the COSEAL community, covering the entire experimental cycle from research questions to result presentation.
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.