Interactive Decomposition Multi-Objective Optimisation via Progressively Learned Value Functions
- Submitting institution
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University of Exeter
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 6346
- Type
- D - Journal article
- DOI
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10.1109/TFUZZ.2018.2880700
- Title of journal
- IEEE Transactions on Fuzzy Systems
- Article number
- -
- First page
- 849
- Volume
- 27
- Issue
- 5
- ISSN
- 1941-0034
- Open access status
- Compliant
- Month of publication
- November
- 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
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3
- Research group(s)
-
-
- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper developed a unified and general framework for interactive multi-objective optimisation, which can be adapted to any evolutionary multi-objective optimisation frameworks. It bridges the gap between the traditional multi-criterion decision-making (MCDM) and the emerging data-driven optimisation thus further boost the interests in both MCDM and evolutionary multi-objective optimisation communities to find synergies that enable a human-in-the-loop computing paradigm. The work inspired many follow-up works (e.g., DOI: 10.1016/j.swevo.2019.100602 and DOI: 10.1016/j.ins.2020.05.103) and has been applied to label correlation analysis for multi-label classifier chain in machine learning (DOI: 10.1016/j.ins.2020.12.010) and tourist itineraries (DOI: 10.1016/j.eswa.2020.113563).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -