How Crossover Speeds up Building Block Assembly in Genetic Algorithms
- Submitting institution
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The University of Sheffield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2524
- Type
- D - Journal article
- DOI
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10.1162/EVCO_a_00171
- Title of journal
- Evolutionary Computation
- Article number
- -
- First page
- 237
- Volume
- 25
- Issue
- 2
- ISSN
- 1530-9304
- Open access status
- Out of scope for open access requirements
- Month of publication
- November
- 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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0
- Research group(s)
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A - Algorithms
- Citation count
- 16
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- For 20+ years evolutionary computation has debated the advantages of crossover. This is the first paper to definitively show that crossover is twice as fast at hill-climbing as the best mutation-only algorithms. It led to 8 invited talks in the UK and Germany, an improved algorithm (doi.org/10.1016/j.tcs.2014.11.028) and refined analyses in the EPSRC RIGOROUS project (EP/M004252/1,doi.org/10.1109/TEVC.2017.2745715) and at Sorbonne University (doi.org/10.1007/978-3-319-99259-4_3). Subsequent collaboration with University of Birmingham revealed significant advantages of crossover for hybrid evolutionary algorithms in multimodal optimisation (doi.org/10.1016/j.artint.2020.103345).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -