Escaping local optima using crossover with emergent diversity
- Submitting institution
-
The University of Birmingham
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 42926073
- Type
- D - Journal article
- DOI
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10.1109/TEVC.2017.2724201
- Title of journal
- IEEE Transactions on Evolutionary Computation
- Article number
- -
- First page
- 484
- Volume
- 22
- Issue
- 3
- ISSN
- 1089-778X
- Open access status
- Compliant
- Month of publication
- August
- 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
-
7
- Research group(s)
-
-
- Citation count
- 27
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper proves that sexual reproduction speeds up evolution. This is significant because the role of sex has been a major open problem in evolutionary computation and the "queen of open problems" in biology. There were examples were sexual reproduction reduces escape times from local optima, but only with artificially enforced
population diversity.
This paper shows through a rigorous mathematical analysis that population diversity emerges naturally via interaction between mutation and sexual recombination, and sufficiently to quickly escape local optima.
This paper was a PPSN best paper award nominee, and published in IEEE-TEVC, a leading AI/CS journal.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -