Interactive evolutionary multiobjective optimization driven by robust ordinal regression

• Autorzy: Branke J. , Greco S. , Słowiński R. , Zielniewicz P.
• Języki publikacji: EN

Abstrakty

EN

This paper presents the Necessary-preference-enhanced Evolutionary Multiobjective Optimizer (NEMO), which combines an evolutionary multiobjective optimization with robust ordinal regression within an interactive procedure. In the course of NEMO, the decision maker is asked to express preferences by simply comparing some pairs of solutions in the current population. The whole set of additive value functions compatible with this preference information is used within a properly modified version of the evolutionary multiobjective optimization technique NSGA-II in order to focus the search towards solutions satisfying the preferences of the decision maker. This allows to speed up convergence to the most preferred region of the Pareto-front.

Słowa kluczowe

EN

evolutionary multiobjective optimization; interactive procedure; robust ordinal regression

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2010
• Tom: Vol. 58, nr 3
• Strony: 347--358
• Opis fizyczny: Bibliogr. 32 poz., rys.

Twórcy

autor

Branke J.

autor

Greco S.

autor

Słowiński R.

autor

Zielniewicz P.

• Warwick Business School, University of Warwick, UK

Bibliografia

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