BULLETIN of the

POLISH ACADEMY of SCIENCES

TECHNICAL SCIENCES

BULLETIN of the POLISH ACADEMY of SCIENCES: TECHNICAL SCIENCES
Volume 58, Issue 3, September 2010

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Interactive evolutionary multiobjective optimization driven by robust ordinal regression

J.BRANKE, S.GRECO, R.SLOWINSKI, and P.ZIELNIEWICZ
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.
Key words:

evolutionary multiobjective optimization, interactive procedure, robust ordinal regression


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