English
Ciência_Iscte
Publicações
Descrição Detalhada da Publicação
Evolutionary Multi-Criterion Optimization. EMO 2017. Lecture Notes in Computer Science
Ano (publicação definitiva)
2017
Língua
Inglês
País
Suíça
Mais Informação
Web of Science®
Scopus
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Abstract/Resumo
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.
Agradecimentos/Acknowledgements
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Palavras-chave
Evolutionary multiobjective optimization,Multicriteria decision making,OWL ontology,Preference,Protégé
Classificação Fields of Science and Technology
- Ciências Físicas - Ciências Naturais
Registos de financiamentos
| Referência de financiamento | Entidade Financiadora |
|---|---|
| UID/MULTI/0446/2013 | Fundação para a Ciência e a Tecnologia |
