Surrogate-based methods for black-box optimization

Ky Khac Vu, Claudia D'Ambrosio, Youssef Hamadi, Leo Liberti
<span title="2016-04-20">2016</span> <i title="Wiley"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2vyq3vorabczbc57sgxyrmy42y" style="color: black;">International Transactions in Operational Research</a> </i> &nbsp;
In this paper, we survey methods that are currently used in black-box optimization, i.e. the kind of problems whose objective functions are very expensive to evaluate and no analytical or derivative information are 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1111/itor.12292">doi:10.1111/itor.12292</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yoqh5glwrfatzis4kirs55dpeu">fatcat:yoqh5glwrfatzis4kirs55dpeu</a> </span>
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