Level-based analysis of genetic algorithms and other search processes
- Submitting institution
-
The University of Birmingham
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 42521691
- Type
- D - Journal article
- DOI
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10.1109/TEVC.2017.2753538
- Title of journal
- IEEE Transactions on Evolutionary Computation
- Article number
- -
- First page
- 707
- Volume
- 22
- Issue
- 5
- ISSN
- 1089-778X
- Open access status
- Compliant
- Month of publication
- September
- 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
-
3
- Research group(s)
-
-
- Citation count
- 27
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Computational complexity analyses of evolutionary algorithms (EAs) used to disregard the population and assume a population size of one. Techniques developed to analyse single-individual EAs couldn't explain the intricate behaviour of populations. This paper introduced the level-based technique allowing analysis of population-based EAs. The reviewers wrote "a breakthrough in the time complexity of randomized search heuristics" and "an important milestone
in our field".
Tutorials on the technique were given in major conferences: CEC2015, PPSN2016-2020, GECCO2016-2020. The conference version was nominated for the best paper award. The journal version appeared in IEEETEVC, the CS journal with the highest impact factor (10).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -