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- W2593731893 abstract "In this paper we investigate surrogate-based optimisation performance using two different ensemble approaches, and a novel update strategy based on the local Pearson correlation coefficient. The ?first ensemble, is based on a selective approach, where ns RBFs are constructed and the most accurate RBF is selected for prediction at each iteration, while the others are ignored. The second ensemble uses a combined approach, which takes advantage of ns different RBFs, in the hope of reducing errors in the prediction through a weighted combination of the RBFs used. The update strategy uses the local Pearson correlation coefficient as a constraint to ignore domain areas where there is disagreement between the surrogates. In total the performance of six different approaches are investigated, using ?five analytical test functions with 2 to 50 dimensions, and one engineering problem related to the frequency response of a satellite boom with 2 to 40 dimensions." @default.
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- W2593731893 date "2016-06-19" @default.
- W2593731893 modified "2023-09-27" @default.
- W2593731893 title "Improving the optimisation performance of an ensemble of radial basis functions" @default.
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