Robust optimization over time by learning problem space characteristics
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 1216
- Type
- D - Journal article
- DOI
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10.1109/TEVC.2018.2843566
- Title of journal
- IEEE Transactions on Evolutionary Computation
- Article number
- -
- First page
- 143
- Volume
- 23
- Issue
- 1
- ISSN
- 1089-778X
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2018
- 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
-
2
- Research group(s)
-
B - LOOM
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work was partially supported by a grant from the Royal Academy of Engineering (NRCP1617-6-125, £50k, 2016-17). A preliminary version was nominated for the best paper award at the EvoApps2018 conference (http://www.evostar.org/2018/programme_bestpapers.php) and received the highest review score in EvoSTOC 2018 (M. Michalis, Conference Chair, mavrovouniotis.michalis@ucy.ac.cy). The proposed methodologies to deal with uncertainties have helped Merseyrail improve its Autumn train service (8.5m passengers/season, Chris Ellery, Manager, cellery@merseyrail.org), when low adhesion due to fallen leaves on the tracks may cause disruption. The work has led to a Rail Safety and Standards Board grant (COF-FCA-04, £100k to LJMU, 2019-2020).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -