Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 86-100 |
| Seitenumfang | 15 |
| Fachzeitschrift | Artificial intelligence |
| Jahrgang | 272 |
| Frühes Online-Datum | 4 Jan. 2019 |
| Publikationsstatus | Veröffentlicht - Juli 2019 |
| Extern publiziert | Ja |
Abstract
The algorithm selection problem is to choose the most suitable algorithm for solving a given problem instance. It leverages the complementarity between different approaches that is present in many areas of AI. We report on the state of the art in algorithm selection, as defined by the Algorithm Selection competitions in 2015 and 2017. The results of these competitions show how the state of the art improved over the years. We show that although performance in some cases is very good, there is still room for improvement in other cases. Finally, we provide insights into why some scenarios are hard, and pose challenges to the community on how to advance the current state of the art.
ASJC Scopus Sachgebiete
- Geisteswissenschaftliche Fächer (insg.)
- Sprache und Linguistik
- Sozialwissenschaften (insg.)
- Linguistik und Sprache
- Informatik (insg.)
- Artificial intelligence
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
in: Artificial intelligence, Jahrgang 272, 07.2019, S. 86-100.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - The algorithm selection competitions 2015 and 2017
AU - Lindauer, Marius
AU - van Rijn, Jan N.
AU - Kotthoff, Lars
N1 - Funding information: Marius Lindauer acknowledges funding by the DFG ( German Research Foundation ) under Emmy Noether grant HU 1900/2-1 . Lars Kotthoff acknowledges funding from the National Science Foundation (NSF), award number 1813537 .
PY - 2019/7
Y1 - 2019/7
N2 - The algorithm selection problem is to choose the most suitable algorithm for solving a given problem instance. It leverages the complementarity between different approaches that is present in many areas of AI. We report on the state of the art in algorithm selection, as defined by the Algorithm Selection competitions in 2015 and 2017. The results of these competitions show how the state of the art improved over the years. We show that although performance in some cases is very good, there is still room for improvement in other cases. Finally, we provide insights into why some scenarios are hard, and pose challenges to the community on how to advance the current state of the art.
AB - The algorithm selection problem is to choose the most suitable algorithm for solving a given problem instance. It leverages the complementarity between different approaches that is present in many areas of AI. We report on the state of the art in algorithm selection, as defined by the Algorithm Selection competitions in 2015 and 2017. The results of these competitions show how the state of the art improved over the years. We show that although performance in some cases is very good, there is still room for improvement in other cases. Finally, we provide insights into why some scenarios are hard, and pose challenges to the community on how to advance the current state of the art.
KW - Algorithm Selection
KW - Competition Analysis
KW - Meta-Learning
UR - http://www.scopus.com/inward/record.url?scp=85061177910&partnerID=8YFLogxK
U2 - 10.1016/j.artint.2018.10.004
DO - 10.1016/j.artint.2018.10.004
M3 - Article
AN - SCOPUS:85061177910
VL - 272
SP - 86
EP - 100
JO - Artificial intelligence
JF - Artificial intelligence
SN - 0004-3702
ER -