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

• URL: http://arxiv.org/abs/2305.00438v1
• Date: Sun, 30 Apr 2023 09:41:04 GMT
• Title: META-SMGO-$\Delta$: similarity as a prior in black-box optimization
• Authors: Riccardo Busetto, Valentina Breschi, Simone Formentin
• Abstract summary: We propose to incorporate the META-learning rationale into SMGO-$Delta$, a global optimization approach recently proposed in the literature. We show the practical benefits of our META-extension of the baseline algorithm, while providing theoretical bounds on its performance.
• Score: 1.282675419968047
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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