Configuring irace using surrogate configuration benchmarks
- Submitting institution
-
University of St Andrews
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 269135185
- Type
- E - Conference contribution
- DOI
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10.1145/3071178.3071238
- Title of conference / published proceedings
- GECCO '17 : Proceedings of the Genetic and Evolutionary Computation Conference
- First page
- 243
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- July
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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3
- Research group(s)
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A - Artificial Intelligence
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Automated algorithm configuration includes general-purpose techniques for finding the best design choices and parameter settings for any given algorithm. Automated algorithm configurators such as irace have a wide range of applications across several domains. However, those configurators also have their own parameters and choosing the right values for them can help to unlock their potential. This work was the first to show that a systematic study of algorithm configurator’s parameters was possible using surrogate benchmarks. The paper received a best paper award at GECCO 2017.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
