Bi-goal evolution for many-objective optimization problems
- Submitting institution
-
Brunel University London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 019-112220-5305
- Type
- D - Journal article
- DOI
-
10.1016/j.artint.2015.06.007
- Title of journal
- Artificial Intelligence
- Article number
- -
- First page
- 45
- Volume
- 228
- Issue
- -
- ISSN
- 0004-3702
- Open access status
- Out of scope for open access requirements
- Month of publication
- July
- Year of publication
- 2015
- URL
-
http://bura.brunel.ac.uk/handle/2438/12033
- 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
- 97
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper proposes a novel idea where a many-objective optimisation problem may be effectively converted into a bi-goal one regarding convergence and diversity, accompanied by a rigorous way of how to design this type of system. This is the first paper published in the AI flagship journal Artificial Intelligence on designing a many‐objective optimiser, and it is now the most cited among all papers (Over 500) published in that journal since its appearance (Web of Science).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
