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• W3036258001 abstract "Automated algorithm selection promises to support the user in the decisive task of selecting a most suitable algorithm for a given problem. A common component of these machine-trained techniques are regression models which predict the performance of a given algorithm on a previously unseen problem instance. In the context of numerical black-box optimization, such regression models typically build on exploratory landscape analysis (ELA), which quantifies several characteristics of the problem. These measures can be used to train a supervised performance regression model. First steps towards ELA-based performance regression have been made in the context of a fixed-target setting. In many applications, however, the user needs to select an algorithm that performs best within a given budget of function evaluations. Adopting this fixed-budget setting, we demonstrate that it is possible to achieve high-quality performance predictions with off-the-shelf supervised learning approaches, by suitably combining two differently trained regression models. We test this approach on a very challenging problem: algorithm selection on a portfolio of very similar algorithms, which we choose from the family of modular CMA-ES algorithms." @default.
• W3036258001 created "2020-06-25" @default.
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• W3036258001 date "2020-06-25" @default.
• W3036258001 modified "2023-10-06" @default.
• W3036258001 title "Landscape-aware fixed-budget performance regression and algorithm selection for modular CMA-ES variants" @default.
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• W3036258001 doi "https://doi.org/10.1145/3377930.3390183" @default.
• W3036258001 hasPublicationYear "2020" @default.
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