A Learning Automata-Based Multiobjective Hyper-Heuristic
- Submitting institution
-
University of Nottingham, The
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1321870
- Type
- D - Journal article
- DOI
-
10.1109/TEVC.2017.2785346
- Title of journal
- IEEE Transactions on Evolutionary Computation
- Article number
- -
- First page
- 59
- Volume
- 23
- Issue
- 1
- ISSN
- 1089-778X
- Open access status
- Compliant
- Month of publication
- December
- 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
-
2
- Research group(s)
-
-
- Citation count
- 12
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This innovative study presents the state-of-the-art general-purpose hyper-heuristic approach to multi-objective optimisation, providing effective, efficient and reusable intelligent/adaptive software components applicable to even unseen problems. The approach is investigated across multiple problem domains, including the real-world problem of vehicle crashworthiness. This work has the potential to shape the international agenda in multi-objective optimisation and selection hyper-heuristics. The research was delivered as an invited keynote talk at the ICAIAME 2020 conference and a seminar at the FSM University discussing the study. Within top 10% of the highly cited papers in the field of Computer Science - InCites Essential Science Indicators.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
