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Surrogate algorithms such as Bayesian optimisation are especially designedfor black-box optimisation problems with expensive objectives, such ashyperparameter tuning or simulation-based optimisation. In the literature,these algorithms are usually evaluated with synthetic benchmarks which are wellestablished but have no expensive objective, and only on one or two real-lifeapplications which vary wildly between papers. There is a clear lack ofstandardisation when it comes to benchmarking surrogate algorithms onreal-life, expensive, black-box objective functions. This makes it verydifficult to draw conclusions on the effect of algorithmic contributions and togive substantial advice on which method to use when. A new benchmark library,EXPObench, provides first steps towards such a standardisation. The library isused to provide an extensive comparison of six different surrogate algorithmson four expensive optimisation problems from different real-life applications.This has led to new insights regarding the relative importance of exploration,the evaluation time of the objective, and the used model. We also provide rulesof thumb for which surrogate algorithm to use in which situation. A furthercontribution is that we make the algorithms and benchmark problem instancespublicly available, contributing to more uniform analysis of surrogatealgorithms. Most importantly, we include the performance of the six algorithmson all evaluated problem instances. This results in a unique new dataset thatlowers the bar for researching new methods as the number of expensiveevaluations required for comparison is significantly reduced.

EXPObench: Benchmarking Surrogate-based Optimisation Algorithms on Expensive Black-box Functions