A Survey of Features Used for Representing Black-box Single-objective Continuous Optimization

Abstract:This survey examines key advancements in designing features to represent optimization problem instances, algorithm instances, and their interactions within the context of single-objective continuous black-box optimization. These features support machine learning tasks such as algorithm selection, algorithm configuration, and problem classification, and they are also used to evaluate the complementarity of benchmark problem sets. We provide a comprehensive overview of problem landscape features, algorithm features, high-level problem-algorithm interaction features, and trajectory features, including the latest works from the past five years. We also point out limitations of the current state-of-the-art and suggest directions for future research.

Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)

arXiv:2406.06629 [cs.LG]

(or arXiv:2406.06629v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2406.06629 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1016/j.swevo.2026.102288 Focus to learn more DOI(s) linking to related resources

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