Automated Dynamic Algorithm Configuration

verfasst von

Steven Adriaensen, André Biedenkapp, Gresa Shala, Noor Awad, Theresa Eimer, Marius Lindauer, Frank Hutter

Abstract

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, e.g., to adapt to the current part of the optimization landscape. 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; (iv) conduct empirical case studies for using DAC in evolutionary optimization, AI planning, and machine learning.

Organisationseinheit(en)

Institut für Künstliche Intelligenz

Externe Organisation(en)

Albert-Ludwigs-Universität Freiburg

Typ

Artikel

Journal

Journal of Artificial Intelligence Research

Band

75

Seiten

1633-1699

Anzahl der Seiten

67

ISSN

1076-9757

Publikationsdatum

12.2022

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Artificial intelligence

Elektronische Version(en)

https://doi.org/10.48550/arXiv.2205.13881 (Zugang: Offen)
https://doi.org/10.1613/jair.1.13922 (Zugang: Offen)