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Finding good parameter configurations is a formidable challenge, even for fixed problem instance distributions. However, when the instance distribution can change over time, a once effective configuration may no longer provide adequate performance. Realtime algorithm configuration (RAC) offers assistance in finding high-quality configurations for such distributions by automatically adjusting the configurations it recommends based on instances seen so far. Existing RAC methods treat the solver as a black box, meaning the solver is given a configuration as input, and it outputs either a solution or runtime as an objective function for the configurator. However, analyzing intermediate output from the solver can enable configurators to avoid wasting time on poorly performing configurations. We propose a gray-box approach that utilizes intermediate output during evaluation and implement it within the RAC method Contextual Preselection with Plackett-Luce (CPPL blue). We apply cost-sensitive machine learning with pairwise comparisons to determine whether ongoing evaluations can be terminated to free resources. We compare our approach to a black-box equivalent on several experimental settings and show that our approach reduces the total solving time in several scenarios and improves solution quality in an additional scenario.<\/jats:p>","DOI":"10.1007\/s10472-023-09890-x","type":"journal-article","created":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T04:01:42Z","timestamp":1692331302000},"page":"109-130","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Realtime gray-box algorithm configuration using cost-sensitive classification"],"prefix":"10.1007","volume":"93","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9039-3869","authenticated-orcid":false,"given":"Dimitri","family":"Weiss","sequence":"first","affiliation":[]},{"given":"Kevin","family":"Tierney","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,18]]},"reference":[{"key":"9890_CR1","doi-asserted-by":"crossref","unstructured":"Hutter, F., Hoos, H.H., Leyton-Brown, K., St\u00fctzle, T.: Paramils: An automatic algorithm configuration framework. 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