  When solving global optimization problems in practice, one often ends up
repeatedly solving problems that are similar to each others. By providing a
rigorous definition of similarity, in this work we propose to incorporate the
META-learning rationale into SMGO-$\Delta$, a global optimization approach
recently proposed in the literature, to exploit priors obtained from similar
past experience to efficiently solve new (similar) problems. Through a
benchmark numerical example we show the practical benefits of our
META-extension of the baseline algorithm, while providing theoretical bounds on
its performance.
