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When solving global optimization problems in practice, one often ends uprepeatedly solving problems that are similar to each others. By providing arigorous definition of similarity, in this work we propose to incorporate theMETA-learning rationale into SMGO-$\Delta$, a global optimization approachrecently proposed in the literature, to exploit priors obtained from similarpast experience to efficiently solve new (similar) problems. Through abenchmark numerical example we show the practical benefits of ourMETA-extension of the baseline algorithm, while providing theoretical bounds onits performance.

META-SMGO-$\Delta$: similarity as a prior in black-box optimization