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Model-based Optimization (MBO) is a method to optimize expensive black-box functions that uses a surrogate to guide the search. We propose two practical approaches that allow MBO to optimize black-box functions where the relation between input and output changes over time, which are known as dynamic optimization problems (DOPs). The window approach trains the surrogate only on the most recent observations, and the time-as-covariate approach includes the time as an additional input variable in the surrogate, giving it the ability to learn the effect of the time on the outcomes. We focus on problems where the change happens systematically and label this systematic change concept drift. To benchmark our methods we define a set of benchmark functions built from established synthetic static functions that are extended with controlled drifts. We evaluate how the proposed approaches handle scenarios of no …