R-Metric: Evaluating the Performance of Preference-Based Evolutionary Multi-Objective Optimization Using Reference Points
- Submitting institution
-
University of Exeter
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1805
- Type
- D - Journal article
- DOI
-
10.1109/TEVC.2017.2737781
- Title of journal
- IEEE Transactions on Evolutionary Computation
- Article number
- -
- First page
- 821
- Volume
- 22
- Issue
- 6
- ISSN
- 1089-778X
- Open access status
- Compliant
- Month of publication
- September
- 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
- 21
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper is the first of its kind to provide a systematic framework for evaluating the quality of solutions that approximate a partial region of the Pareto-optimal front according to the preference information elicited by decision maker. The work bridges the gap in quantitative analysis of set-based algorithms for multi-criterion decision making. It has been widely used as the metric in the evolutionary multi-objective optimisation community for performance comparison (e.g., DOI: 10.1109/TEVC.2018.2884133) and the algorithms based on this metric have been applied to solve problems in software engineering (DOI: 10.1016/j.trc.2018.04.008) and airport surface operations (DOI: 10.1016/j.trc.2018.04.008).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
