On the Choice of the Update Strength in Estimation-of-Distribution Algorithms and Ant Colony Optimization
- Submitting institution
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The University of Sheffield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2558
- Type
- D - Journal article
- DOI
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10.1007/s00453-018-0480-z
- Title of journal
- Algorithmica
- Article number
- -
- First page
- 1450
- Volume
- 81
- Issue
- 4
- ISSN
- 0178-4617
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2018
- 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 - Algorithms
- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Identifies trade-off between speed and stability for the compact Genetic Algorithm (cGA) and shows lower runtime bounds for all update strengths, leading to first proof that the cGA cannot hill-climb faster than evolutionary algorithms. Preliminary paper (28 GS citations) nominated for best paper (GECCO'16). Led to follow-on work at Denmark’s Technical University and Hasso-Plattner-Institut on UMDA algorithm (doi.org/10.1016/j.tcs.2018.06.004) and established a collaboration with ETH-Zürich (doi.org/10.1145/3205455.3205576). Ecole-Polytechnique and Tsinghua University subsequently presented a quantification of genetic drift (doi.org/10.1109/TEVC.2020.2987361) and an improved, smart-restart version of the cGA (doi.org/10.1145/3377930.3390163).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -