RunAndSchedule2Survive
Algorithm Scheduling Based on Run2Survive
Abstract
The algorithm selection problem aims to identify the most suitable algorithm for a given problem instance under specific time constraints, where suitability typically refers to a performance metric such as algorithm runtime. While previous work has employed machine learning techniques to tackle this challenge, methods from survival analysis have proven particularly effective. This article presents RunAndSchedule2Survive to address the more general and complex problem of algorithm scheduling, where the objective is to allocate computational resources across multiple algorithms to maximize performance within specified time constraints. Our approach combines survival analysis with evolutionary algorithms to optimize algorithm schedules by leveraging runtime distributions modeled as survival functions. Experimental results across various standard benchmarks demonstrate that our approach significantly outperforms previous methods for algorithm scheduling and yields more robust results than its algorithm selection variant. More specifically, RunAndSchedule2Survive achieves superior performance in 20 out of 25 benchmark scenarios, surpassing hitherto state-of-the-art approaches.
Details
- Organisationseinheit(en)
-
Fachgebiet Maschinelles Lernen
Forschungszentrum L3S
- Externe Organisation(en)
-
Munich Center for Machine Learning (MCML)
Ludwig-Maximilians-Universität München (LMU)
Beckhoff Automation GmbH und Co KG, Verl, Germany
- Typ
- Artikel
- Journal
- ACM Transactions on Evolutionary Learning and Optimization
- Band
- 5
- Anzahl der Seiten
- 17
- ISSN
- 2688-299X
- Publikationsdatum
- 29.08.2025
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Informatik (sonstige), Software, Maschinelles Sehen und Mustererkennung, Angewandte Informatik, Theoretische Informatik und Mathematik, Artificial intelligence
- Elektronische Version(en)
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https://doi.org/10.1145/3737705 (Zugang:
Geschlossen
)
