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• W2912542961 abstract "It is not uncommon that meta-heuristic algorithms contain some intrinsic parameters, the optimal configuration of which is crucial for achieving their peak performance. However, evaluating the effectiveness of a configuration is expensive, as it involves many costly runs of the target algorithm. Perhaps surprisingly, it is possible to build a cheap-to-evaluate surrogate that models the algorithm's empirical performance as a function of its parameters. Such surrogates constitute an important building block for understanding algorithm performance, algorithm portfolio/selection, and the automatic algorithm configuration. In principle, many off-the-shelf machine learning techniques can be used to build surrogates. In this paper, we take the differential evolution (DE) as the baseline algorithm for proof-of-concept study. Regression models are trained to model the DE's empirical performance given a parameter configuration. In particular, we evaluate and compare four popular regression algorithms both in terms of how well they predict the empirical performance with respect to a particular parameter configuration, and also how well they approximate the parameter versus the empirical performance landscapes." @default.
• W2912542961 created "2019-02-21" @default.
• W2912542961 creator A5017100408 @default.
• W2912542961 creator A5025285243 @default.
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• W2912542961 date "2019-01-30" @default.
• W2912542961 modified "2023-09-25" @default.
• W2912542961 title "Which Surrogate Works for Empirical Performance Modelling? A Case Study with Differential Evolution" @default.
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• W2912542961 doi "https://doi.org/10.48550/arxiv.1901.11120" @default.
• W2912542961 hasPublicationYear "2019" @default.
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