Hyper-parameter tuning for the (1+ (λ, λ)) GA
- Submitting institution
-
University of St Andrews
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 269135273
- Type
- E - Conference contribution
- DOI
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10.1145/3321707.3321725
- Title of conference / published proceedings
- Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '19)
- First page
- 889
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- July
- Year of publication
- 2019
- 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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1
- Research group(s)
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A - Artificial Intelligence
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The (1+ (λ, λ)) GA plays an important role in theoretical research for evolutionary computation and has been extensively studied in recent years. In this work, we demonstrated, for the first time, that empirical techniques from automated algorithm configuration can bring in-depth insights into the understanding of the algorithm and can support new theoretical findings. The work bridges the gap between theoretical and empirical communities in evolutionary computation.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
