Abstract
We present an interactive evolutionary multiple objective optimization (MOO) method incorporating preference information of several decision makers into the evolutionary search. It combines NSGA-II, a well-known evolutionary MOO method, with some interactive value-based approaches based on the principle of ordinal regression. We introduce several variants of the method distinguished by an elitist function indicating a comprehensive value that each solution represents to the group members. The experimental results confirm that all proposed approaches are able to focus the search on the group-preferred solutions, differing, however, with respect to both part of the Pareto front to which they converge as well as the convergence speed measured in terms of a change of utilitarian value of the returned solutions.
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Acknowledgments
The authors acknowledge financial support from the Poznan University of Technology, grant DS-MLODA KADRA (2015).
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Kadziński, M., Tomczyk, M. (2015). Using Ordinal Regression for Interactive Evolutionary Multiple Objective Optimization with Multiple Decision Makers. In: Kamiński, B., Kersten, G., Szapiro, T. (eds) Outlooks and Insights on Group Decision and Negotiation. GDN 2015. Lecture Notes in Business Information Processing, vol 218. Springer, Cham. https://doi.org/10.1007/978-3-319-19515-5_15
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DOI: https://doi.org/10.1007/978-3-319-19515-5_15
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