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- W3128156750 abstract "Computer simulations are being extensively used as a partial substitute for real-world experiments. Such simulations are often computationally intensive and hence metamodels are used to approximate them and to yield estimated output values more economically. While this setup can work well in low dimensional problems it can struggle in high-dimensional ones due to poor metamodel prediction accuracy. As such this study examines the application of dimensionality-reduction procedures during the search so that a simplified problems is formulated which is easier to solve and which could yield a better solution of the original one. An extensive performance analysis with both mathematical test functions and an engineering application shows the effectiveness of the proposed approach." @default.
- W3128156750 created "2021-02-15" @default.
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- W3128156750 date "2020-03-05" @default.
- W3128156750 modified "2023-09-29" @default.
- W3128156750 title "Improving High-dimensional Simulation-driven Optimization" @default.
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- W3128156750 doi "https://doi.org/10.37394/23209.2020.17.3" @default.
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