Surrogate‐based methods for black‐box optimization

Abstract

Abstract In this paper, we survey methods that are currently used in black‐box optimization, that is, the kind of problems whose objective functions are very expensive to evaluate and no analytical or derivative information is available. We concentrate on a particular family of methods, in which surrogate (or meta) models are iteratively constructed and used to search for global solutions.