Identifying Good Algorithm Parameters in Evolutionary Multi- and Many-Objective Optimisation: A Visualisation Approach
- Submitting institution
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University of Plymouth
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 894
- Type
- D - Journal article
- DOI
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10.1016/j.asoc.2019.105902
- Title of journal
- Applied Soft Computing
- Article number
- 105902
- First page
- -
- Volume
- 88
- Issue
- -
- ISSN
- 1568-4946
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2019
- 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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1
- Research group(s)
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-
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper introduces a method for benchmarking different evolutionary algorithms and presenting the results with a novel visualisation method. This is important, as benchmarking an optimiser is a useful way of identifying the best set of algorithm parameters for a given problem, but which has until now required significant experience and knowledge about evolutionary computation to undertake. This approach removes the prior knowledge needed by visualising the results in an intuitive fashion.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -