Shift-Based Density Estimation for Pareto-Based Algorithms in Many-Objective Optimization
- Submitting institution
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The University of Birmingham
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 91072662
- Type
- D - Journal article
- DOI
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10.1109/TEVC.2013.2262178
- Title of journal
- IEEE Transactions on Evolutionary Computation
- Article number
- -
- First page
- 348
- Volume
- 18
- Issue
- 3
- ISSN
- 1089-778X
- Open access status
- Out of scope for open access requirements
- Month of publication
- June
- Year of publication
- 2014
- 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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2
- Research group(s)
-
-
- Citation count
- 250
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work changed the way people think about the classical Pareto method in multiobjective optimisation. Despite being the predominant method, it was long believed unsuitable in many-objective optimisation. This work refuted this belief and showed it to be well-suited to many-objective optimisation by proposing a general enhancement of its paradigm.
This work has now become a benchmark technique in the area, inspiring over 100 follow-up studies and being applied to various optimisation scenarios in many disciplines. The paper is published in the top evolutionary computation journal.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -