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Contextual Preselection Methods in Pool-Based Realtime Algorithm Configuration

MCML Authors

Link to Profile Eyke Hüllermeier

Eyke Hüllermeier

Prof. Dr.

Core PI

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 BSS+23

LWDA 2023

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

Authors

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

Links

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Research Area

 A3 | Computational Models

BibTeXKey: BSS+23

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