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Benchmarking is a key aspect of research into optimization algorithms, and assuch the way in which the most popular benchmark suites are designed implicitlyguides some parts of algorithm design. One of these suites is the black-boxoptimization benchmarking (BBOB) suite of 24 single-objective noiselessfunctions, which has been a standard for over a decade. Within this problemsuite, different instances of a single problem can be created, which isbeneficial for testing the stability and invariance of algorithms undertransformations. In this paper, we investigate the BBOB instance creationprotocol by considering a set of 500 instances for each BBOB problem. Usingexploratory landscape analysis, we show that the distribution of landscapefeatures across BBOB instances is highly diverse for a large set of problems.In addition, we run a set of eight algorithms across these 500 instances, andinvestigate for which cases statistically significant differences inperformance occur. We argue that, while the transformations applied in BBOBinstances do indeed seem to preserve the high-level properties of thefunctions, their difference in practice should not be overlooked, particularlywhen treating the problems as box-constrained instead of unconstrained.

BBOB Instance Analysis: Landscape Properties and Algorithm Performance across Problem Instances