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- W2106987277 abstract "The aim of this work is to compare different approaches for parallelization in model-based optimization. As another alternative aside from the existing methods, we propose using a multi-objective infill criterion that rewards both the diversity and the expected improvement of the proposed points. This criterion can be applied more universally than the existing ones because it has less requirements. Internally, an evolutionary algorithm is used to optimize this criterion. We verify the usefulness of the approach on a large set of established benchmark problems for black-box optimization. The experiments indicate that the new method’s performance is competitive with other batch techniques and single-step EGO." @default.
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- W2106987277 date "2014-01-01" @default.
- W2106987277 modified "2023-10-01" @default.
- W2106987277 title "MOI-MBO: Multiobjective Infill for Parallel Model-Based Optimization" @default.
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- W2106987277 doi "https://doi.org/10.1007/978-3-319-09584-4_17" @default.
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