A Competitive Divide-and-Conquer Algorithm for Unconstrained Large-Scale Black-Box Optimization
- Submitting institution
-
The University of Leeds
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- UOA11-4055
- Type
- D - Journal article
- DOI
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10.1145/2791291
- Title of journal
- ACM Transactions on Mathematical Software
- Article number
- 13
- First page
- -
- Volume
- 42
- Issue
- 2
- ISSN
- 0098-3500
- Open access status
- Out of scope for open access requirements
- Month of publication
- June
- Year of publication
- 2016
- 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
-
3
- Research group(s)
-
B - AI (Artificial Intelligence)
- Citation count
- 94
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper achieved 100% accuracy in identifying problem structure of 90% of test cases from a set of widely-used functions in numerical optimization. Accurate structural information resulted in solving 30% of test cases to optimality and improving the state-of-the-art results on 65% of the cases. The paper identified an important source of error in structural analysis of continuous functions, which helped its successor to achieve better generalizability on a wider range of functions. The software accompanying this paper has been downloaded 600+ times from MathWorks File Exchange.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
