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Li, L., Yevseyeva, I., Basto-Fernandes, V., Trautmann, H., Jing, N. & Emmerich, M. (2017). Building and using an ontology of preference-based multiobjective evolutionary algorithms. In Schütze, O., Rudolph, G., Klamroth, K., Jin, Y., Trautmann, H., Grimme, C. and Wiecek, M. (Ed.), Evolutionary Multi-Criterion Optimization. EMO 2017. Lecture Notes in Computer Science. (pp. 406-421). Münster, Germany: Springer.

I. Yevseyeva et al.,  "Building and using an ontology of preference-based multiobjective evolutionary algorithms", in Evolutionary Multi-Criterion Optimization. EMO 2017. Lecture Notes in Computer Science, Schütze, O., Rudolph, G., Klamroth, K., Jin, Y., Trautmann, H., Grimme, C. and Wiecek, M., Ed., Münster, Germany, Springer, 2017, vol. 10173, pp. 406-421

@inproceedings{yevseyeva2017_1775753823662,
	author = "Li, L. and Yevseyeva, I. and Basto-Fernandes, V. and Trautmann, H. and Jing, N. and Emmerich, M.",
	title = "Building and using an ontology of preference-based multiobjective evolutionary algorithms",
	booktitle = "Evolutionary Multi-Criterion Optimization. EMO 2017. Lecture Notes in Computer Science",
	year = "2017",
	editor = "Schütze, O., Rudolph, G., Klamroth, K., Jin, Y., Trautmann, H., Grimme, C. and Wiecek, M.",
	volume = "10173",
	number = "",
	series = "",
	doi = "10.1007/978-3-319-54157-0_28",
	pages = "406-421",
	publisher = "Springer",
	address = "Münster, Germany",
	organization = "",
	url = "https://link.springer.com/book/10.1007/978-3-319-54157-0"
}

TY  - CPAPER
TI  - Building and using an ontology of preference-based multiobjective evolutionary algorithms
T2  - Evolutionary Multi-Criterion Optimization. EMO 2017. Lecture Notes in Computer Science
VL  - 10173
AU  - Li, L.
AU  - Yevseyeva, I.
AU  - Basto-Fernandes, V.
AU  - Trautmann, H.
AU  - Jing, N.
AU  - Emmerich, M.
PY  - 2017
SP  - 406-421
DO  - 10.1007/978-3-319-54157-0_28
CY  - Münster, Germany
UR  - https://link.springer.com/book/10.1007/978-3-319-54157-0
AB  - 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.
ER  -