Preference-guided evolutionary algorithms for many-objective optimization
- Submitting institution
-
Aston University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 46677630
- Type
- D - Journal article
- DOI
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10.1016/j.ins.2015.09.015
- Title of journal
- Information Sciences
- Article number
- -
- First page
- 236
- Volume
- 329
- Issue
- -
- ISSN
- 0020-0255
- Open access status
- Out of scope for open access requirements
- Month of publication
- February
- Year of publication
- 2016
- 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
-
1
- Research group(s)
-
A - Aston Institute of Urban Technology and the Environment (ASTUTE)
- Citation count
- 32
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper presents a novel framework for incorporating preference information into the structure of evolutionary multi-objective optimisation algorithms, simultaneously tackling two outstanding problems in the area: the challenge of dealing with large numbers of objectives, and the associated problem of multi-criterion decision support. The paper has been very well-received, and has influenced subsequent work by several independent groups developing research on preference modelling and many-objective optimisation, as evidenced by diversity of affiliations in the citations profile of this work, see e.g.: https://doi.org/10.1109/TITS.2017.2776943
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
