Average drift analysis and population scalability
- Submitting institution
-
Nottingham Trent University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 10 - 699875
- Type
- D - Journal article
- DOI
-
10.1109/TEVC.2016.2608420
- Title of journal
- IEEE Transactions on Evolutionary Computation
- Article number
- -
- First page
- 426
- Volume
- 21
- Issue
- 3
- ISSN
- 1089-778X
- Open access status
- Technical exception
- Month of publication
- September
- 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
-
1
- Research group(s)
-
A - Computing and Informatics Research Centre
- Citation count
- 4
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The significance of this paper is a new form of drift analysis, called average drift analysis, for theoretically comparing the running time of individual-based and population-based evolutionary algorithms but without estimating their running time. This work has been recognised in [https://doi.org/10.1109/TEVC.2019.2921547] as it “rigorously analyzed the effect of population size on the computation time of EAs using mutation and elitist selection”, in [https://doi.org/10.1007/s12065-018-0153-5] as one of “more recent studies on the topic”. It was also selected in literature review [https://doi.org/10.1007/978-3-030-29414-4_2]. The research was collaborated with University of Birmingham.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
