Improved time complexity analysis of the Simple Genetic Algorithm
- Submitting institution
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The University of Sheffield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2532
- Type
- D - Journal article
- DOI
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10.1016/j.tcs.2015.01.002
- Title of journal
- Theoretical Computer Science
- Article number
- -
- First page
- 21
- Volume
- 605
- Issue
- -
- ISSN
- 0304-3975
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- Year of publication
- 2015
- 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
- 45
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- It rigorously explains why a standard genetic algorithm (GA) should not apply the originally proposed fitness proportional selection operator. It improves upon the state-of-the-art (doi.org/10.1016/j.tcs.2013.06.015) by providing a stronger technique for exponential lower bounds compared to the only other existing technique (doi.org/10.1016/j.tcs.2013.06.015). Led to techniques for complementary upper bounds for efficient GAs (doi.org/10.1109/TEVC.2017.2753538, doi.org/10.1109/TEVC.2017.2724201, doi.org/10.1109/TEVC.2017.2745715). Fundamental for securing EPSRC Fellowship (EP/M004252/1, £1,266,592). Regularly presented in tutorials at IEEE-CEC and ACM-GECCO.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -