A novel aggregation-based dominance for Pareto-based evolutionary algorithms to configure software product lines
- Submitting institution
-
Brunel University London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 081-208829-5956
- Type
- D - Journal article
- DOI
-
10.1016/j.neucom.2019.06.075
- Title of journal
- Neurocomputing
- Article number
- -
- First page
- 32
- Volume
- 364
- Issue
- -
- ISSN
- 0925-2312
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2019
- URL
-
http://bura.brunel.ac.uk/handle/2438/19138
- 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
-
4
- Research group(s)
-
2 - Software, Systems & Security (SSS)
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper proposes a novel aggregation-based dominance for Pareto-based evolutionary algorithms to direct the search for high-quality solutions, which was shown to overcome some of the key challenges in applying this type of many-objective optimisation algorithm to software engineering problems. Other researchers (Huilcapi et al. (2020), IEEE Access (https://doi.org/10.1109/ACCESS.2020.2976774.) have taken one outcome of our work, i.e. showing that it is possible to build an optimal Pareto set by applying a Pareto dominance analysis, to support the validity of their proposed paradigm for helping designers to choose optimal Pareto solutions.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
