Interactive evolutionary multiobjective optimization driven by robust ordinal regression

被引：25
|
作者：
Branke, J. [2 ]
Greco, S. [3 ]
Slowinski, R. [1 ,4 ]
Zielniewicz, P. [1 ]
机构：
[1] Poznan Univ Tech, Inst Comp Sci, PL-60965 Poznan, Poland
[2] Univ Warwick, Warwick Business Sch, Coventry CV4 7AL, W Midlands, England
[3] Univ Catania, Fac Econ, I-95131 Catania, Italy
[4] Polish Acad Sci, Syst Res Inst, PL-01447 Warsaw, Poland
来源：
BULLETIN OF THE POLISH ACADEMY OF SCIENCES-TECHNICAL SCIENCES | 2010年 / 58卷 / 03期
关键词：
evolutionary multiobjective optimization; interactive procedure; robust ordinal regression; ALGORITHM; SET;
D O I：
10.2478/v10175-010-0033-3
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
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
引用
收藏
页码：347 / 358
页数：12
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