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Publication Detailed Description
Evolutionary Multi-Criterion Optimization. EMO 2017. Lecture Notes in Computer Science
Year (definitive publication)
2017
Language
English
Country
Switzerland
More Information
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Abstract
Integrating user preferences in Evolutionary Multiobjective Optimization (EMO) is currently a prevalent research topic. There is a large variety of preference handling methods (originated from Multicriteria decision making, MCDM) and EMO methods, which have been combined in various ways. This paper proposes a Web Ontology Language (OWL) ontology to model and systematize the knowledge of preferencebased multiobjective evolutionary algorithms (PMOEAs). Detailed procedure is given on how to build and use the ontology with the help of Protégé. Different use-cases, including training new learners, querying and reasoning are exemplified and show remarkable benefit for both EMO and MCDM communities.
Acknowledgements
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Keywords
Evolutionary multiobjective optimization,Multicriteria decision making,OWL ontology,Preference,Protégé
Fields of Science and Technology Classification
- Physical Sciences - Natural Sciences
Funding Records
| Funding Reference | Funding Entity |
|---|---|
| UID/MULTI/0446/2013 | Fundação para a Ciência e a Tecnologia |
