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Brandt, Jasmin; Schede, Elias; Sharma, Shivam; Bengs, Viktor ORCID logoORCID: https://orcid.org/0000-0001-6988-6186; Hüllermeier, Eyke ORCID logoORCID: https://orcid.org/0000-0002-9944-4108 and Tierney, Kevin (October 2023): Contextual Preselection Methods in Pool-based Realtime Algorithm Configuration. Lernen, Wissen, Daten, Analysen (LWDA), Marburg, Germany, 9-11 October 2023. Leyer, Michael and Wichmann, Johannes (eds.) : In: Lernen, Wissen, Daten, Analysen (LWDA) Conference Proceedings, Vol. 3630 CEUR-WS.org. pp. 492-505

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Abstract

Realtime algorithm configuration is concerned with the task of designing a dynamic algorithm configurator that observes sequentially arriving problem instances of an algorithmic problem class for which it selects suitable algorithm configurations (e.g., minimal runtime) of a specific target algorithm. The Contextual Preselection under the Plackett-Luce (CPPL) algorithm maintains a pool of configurations from which a set of algorithm configurations is selected that are run in parallel on the current problem instance. It uses the well-known UCB selection strategy from the bandit literature, while the pool of configurations is updated over time via a racing mechanism. In this paper, we investigate whether the performance of CPPL can be further improved by using different bandit-based selection strategies as well as a ranking-based strategy to update the candidate pool. Our experimental results show that replacing these components can indeed improve performance again significantly.

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