Algorithm selection using deep learning without feature extraction
- Submitting institution
-
Edinburgh Napier University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1732831
- Type
- E - Conference contribution
- DOI
-
10.1145/3321707.3321845
- Title of conference / published proceedings
- GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion
- First page
- 198
- Volume
- -
- Issue
- -
- ISSN
- -
- Open access status
- -
- Month of publication
- July
- Year of publication
- 2019
- 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)
-
-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper proposed a novel algorithm selection that avoids the need to generate features. It won the Best Paper Award at GECCO 2019 in the Combinatorial Optimisation track. The technique provided the underpinning for a 42 month, £388K EPSRC grant EP/V026534/1 called Keep Learning that was awarded in December 2020 that integrates state-of-art machine-learning with combinatorial optimisation techniques and is the subject of a work-package in the grant (part of a joint proposal with St Andrews, total value £766K).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -