A Hyper-heuristic Approach to Automated Generation of Mutation Operators for Evolutionary Programming
- Submitting institution
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Queen Mary University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 487
- Type
- D - Journal article
- DOI
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10.1016/j.asoc.2017.10.002
- Title of journal
- Applied Soft Computing
- Article number
- -
- First page
- 162
- Volume
- 62
- Issue
- -
- ISSN
- 1568-4946
- Open access status
- Compliant
- Month of publication
- October
- 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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1
- Research group(s)
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-
- Citation count
- 13
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Introduces and investigates a novel method to automatically generate novel probability distributions for use in optimization algorithms. This supersedes previous approaches in which probability distributions are constructed manually. The new method consistently outperforms the manual approach. This approach forms the backbone of a Hyper-heuristics workshop presented at GECCO (running from 2015-2020), the largest conference on Evolutionary Computing, and is the theme of tutorials at GECCO (running from 2015-2020), CEC, PPSN; the top 3 conferences on Evolutionary Computing. This specific paper also underpins a Chinese collaboration which prompted a year-long visit of the collaborator to QMUL.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -