Contextual Preselection Methods in Pool-Based Realtime Algorithm Configuration

MCML Authors

Viktor Bengs

Dr.

* Former Member

→ Group Eyke Hüllermeier
 Artificial Intelligence and Machine Learning

Eyke Hüllermeier

Prof. Dr.

Principal Investigator

Artificial Intelligence and Machine Learning

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.

inproceedings

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LWDA 2023

Conference on Lernen. Wissen. Daten. Analysen. Marburg, Germany, Oct 09-11, 2023.

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Authors

J. Brandt • E. Schede • S. Sharma • V. Bengs • E. Hüllermeier • K. Tierney

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Links

PDF

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Research Area

 A3 | Computational Models