On the Exploitation of Search History and Accumulative Sampling in Robust Optimisation
Alyahya, K; Doherty, K; Fieldsend, JE; et al.Akman, OE
Date: 15 July 2017
Conference paper
Publisher
Association for Computing Machinery (ACM)
Publisher DOI
Abstract
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 ...
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.
Computer Science
Faculty of Environment, Science and Economy
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