Transforming Evolutionary Search into Higher-Level Evolutionary Search by Capturing Problem Structure
- Submitting institution
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Aberystwyth University / Prifysgol Aberystwyth
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 6734083
- Type
- D - Journal article
- DOI
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10.1109/TEVC.2014.2347702
- Title of journal
- IEEE Transactions on Evolutionary Computation
- Article number
- -
- First page
- 628
- Volume
- 18
- Issue
- 5
- ISSN
- 1089-778X
- Open access status
- Out of scope for open access requirements
- Month of publication
- August
- Year of publication
- 2014
- URL
-
-
- Supplementary information
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- 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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2
- Research group(s)
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-
- Citation count
- 7
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper revisits the popular notion of building blocks and innovatively combines it with modern runtime analysis to establish classes of modular problems, where multi-scale search algorithms like evolutionary algorithms excel, thereby improving applicability of these algorithms. Has influenced follow-up research, including that conducted by world-leading researchers such as YS Ong and M Gong. IEEE Transactions in Evolutionary Computation is the leading journal in the field, having an acceptance rate around 12%.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -