A Survey of Methods for Automated Algorithm Configuration
- authored by
- Elias Schede, Jasmin Brandt, Alexander Tornede, Marcel Wever, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney
- Abstract
Algorithm configuration (AC) is concerned with the automated search of the most suitable parameter configuration of a parametrized algorithm. There is currently a wide variety of AC problem variants and methods proposed in the literature. Existing reviews do not take into account all derivatives of the AC problem, nor do they offer a complete classification scheme. To this end, we introduce taxonomies to describe the AC problem and features of configuration methods, respectively. We review existing AC literature within the lens of our taxonomies, outline relevant design choices of configuration approaches, contrast methods and problem variants against each other, and describe the state of AC in industry. Finally, our review provides researchers and practitioners with a look at future research directions in the field of AC.
- External Organisation(s)
-
Bielefeld University
Paderborn University
Heinz Nixdorf Institute
Ludwig-Maximilians-Universität München (LMU)
Munich Center for Machine Learning (MCML)
- Type
- Review article
- Journal
- Journal of Artificial Intelligence Research
- Volume
- 75
- Pages
- 425-487
- No. of pages
- 63
- ISSN
- 1076-9757
- Publication date
- 2022
- Publication status
- Published
- Peer reviewed
- Yes
- ASJC Scopus subject areas
- Artificial Intelligence
- Electronic version(s)
-
https://doi.org/10.1613/jair.1.13676 (Access:
Open)