Analysis of diversity mechanisms for optimisation in dynamic environments with low frequencies of change
- Submitting institution
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The University of Sheffield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2424
- Type
- D - Journal article
- DOI
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10.1016/j.tcs.2014.10.028
- Title of journal
- Theoretical Computer Science
- Article number
- -
- First page
- 37
- Volume
- 561
- Issue
- -
- ISSN
- 0304-3975
- Open access status
- Out of scope for open access requirements
- Month of publication
- October
- Year of publication
- 2014
- 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
- 15
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- First formal proof of when and why population-based evolutionary algorithms (EAs) are effective for optimisation in dynamic environments when slow changes of the landscape are deceptive. Major step-change compared to previous work from toy EAs to realistic ones using populations. Has led to further analyses of populations in dynamic environments (doi.org/10.1145/2739480.2754808) including artificial immune systems (doi.org/10.1145/2576768.2598328). The EU-Funded SAGE project (N.618091) established to unify the methodologies used in population genetics and evolutionary computation has compared algorithms inspired by population genetics in the same settings (doi.org/10.1007/s00453-016-0212-1). Output of the EPSRC Postdoctoral Fellowship, EP/H028900/1.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -