On the Exploitation of Search History and Accumulative Sampling in Robust Optimisation
Association for Computing Machinery (ACM)
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Reason for embargo
Embargoed until after conference
Efficient 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 fitness approximations across the entire search history rather than a fixed population. Our proposed method shows promising results on a range of test problems compared with other approaches from the literature.
This work was supported by the Engineering and Physical Sciences Research Council [grant number EP/N017846/1].
This is the author accepted manuscript. The final version is available from ACM via the DOI in this record.
GECCO 2017: Genetic and Evolutionary Computation Conference, 15-19 July 2017, Berlin, Germany