Shift-based density estimation for pareto-based algorithms in many-objective optimization
- Submitting institution
-
Brunel University London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 008-94658-5305
- Type
- D - Journal article
- DOI
-
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
-
http://bura.brunel.ac.uk/handle/2438/12061
- 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)
-
1 - Artificial Intelligence (AI)
- Citation count
- 250
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Pareto‐based methods were long believed not good for solving many‐objective optimisation problems. The paper refuted this belief and enabled it well‐suited for many‐objective optimisation by proposing a general enhancement of its paradigm. The 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, published in the top journal on evolutionary computation, is a Highly Cited paper (Web of Science).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
