AClib: A Benchmark Library for Algorithm Configuration
Abstract
Modern solvers for hard computational problems often expose parameters that permit customization for high performance on specific instance types. Since it is tedious and time-consuming to manually optimize such highly parameterized algorithms, recent work in the AI literature has developed automated approaches for this algorithm configuration problem [1, 3, 10, 11, 13, 16].
Details
- External Organisation(s)
-
University of Freiburg
Free University of Brussels (ULB)
University of British Columbia
University of Potsdam
- Type
- Conference contribution
- Pages
- 36-40
- No. of pages
- 5
- Publication date
- 01.08.2014
- Publication status
- Published
- Peer reviewed
- Yes
- ASJC Scopus subject areas
- Theoretical Computer Science, General Computer Science
- Electronic version(s)
-
https://doi.org/10.1007/978-3-319-09584-4_4 (Access:
Closed
)
Cite
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