Best Practices For Empirical Meta-Algorithmic Research: Guidelines from the COSEAL Research Network

TL;DR

This paper consolidates best practices for conducting empirical meta-algorithmic research, covering the entire experimental process to improve validity, reproducibility, and scientific rigor in the field.

Contribution

It provides a comprehensive, community-driven guideline for best practices in empirical meta-algorithmic research, unifying scattered knowledge across subfields.

Findings

Establishes current state-of-the-art practices

Provides guidelines for experimental design and analysis

Aims to improve research validity and reproducibility

Abstract

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.