On the exploitation of search history and accumulative sampling in robust optimisation

Abstract

EEcient robust optimisation methods exploit the search history when evaluating a new solution by using information from previously visited solutions that fall in the new solution's uncertainty neighbourhood. We propose a full exploitation of the search history by updating the robust ttness approximations across the entire search history rather than a axed population. Our proposed method shows promising results on a range of test problems compared with other approaches from the literature.