Automated Dynamic Algorithm Configuration

The performance of an algorithm often critically depends on its parameter configuration. While a variety of automated algorithm configuration methods have been proposed to relieve users from the tedious and error-prone task of manually tuning parameters, there is still a lot of untapped potential as...

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Veröffentlicht in:The Journal of artificial intelligence research Jg. 75; S. 1633 - 1699
Hauptverfasser: Adriaensen, Steven, Biedenkapp, André, Shala, Gresa, Awad, Noor, Eimer, Theresa, Lindauer, Marius, Hutter, Frank
Format: Journal Article
Sprache:Englisch
Veröffentlicht: San Francisco AI Access Foundation 01.01.2022
Schlagworte:
Algorithms
Artificial intelligence
Automation
Configuration management
Machine learning
Optimization
Parameters
ISSN:1076-9757, 1076-9757, 1943-5037
Online-Zugang:Volltext
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Zusammenfassung:The performance of an algorithm often critically depends on its parameter configuration. While a variety of automated algorithm configuration methods have been proposed to relieve users from the tedious and error-prone task of manually tuning parameters, there is still a lot of untapped potential as the learned configuration is static, i.e., parameter settings remain fixed throughout the run. However, it has been shown that some algorithm parameters are best adjusted dynamically during execution. Thus far, this is most commonly achieved through hand-crafted heuristics. A promising recent alternative is to automatically learn such dynamic parameter adaptation policies from data. In this article, we give the first comprehensive account of this new field of automated dynamic algorithm configuration (DAC), present a series of recent advances, and provide a solid foundation for future research in this field. Specifically, we (i) situate DAC in the broader historical context of AI research; (ii) formalize DAC as a computational problem; (iii) identify the methods used in prior art to tackle this problem; and (iv) conduct empirical case studies for using DAC in evolutionary optimization, AI planning, and machine learning.
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ISSN:1076-9757
1076-9757
1943-5037
DOI:10.1613/jair.1.13922