Building and using an ontology of preference-based multiobjective evolutionary algorithms.
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 preference-based 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.
Citation:Li, L., Yevseyeva, I., Basto-Fernandes, V., Trautmann, H., Jing, N. and Emmerich, M. (2017) Building and Using an Ontology of Preference-Based Multiobjective Evolutionary Algorithms. In: Trautmann H. et al. (eds) Proceedings of Evolutionary Multi-Criterion Optimization: 9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017. Lecture Notes in Computer Science, vol 10173. Springer, Cham. Pp. pp 406-421
Research Group:Cyber Security Centre