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
相关论文
共 50 条
  • [21] Robust ordinal regression for value functions handling interacting criteria
    Greco, Salvatore
    Mousseau, Vincent
    Slowinski, Roman
    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2014, 239 (03) : 711 - 730
  • [22] Evolutionary Multiobjective Optimization of Winglets
    Teixeira, Mateus A. M.
    Goulart, Fillipe
    Campelo, Felipe
    GECCO'16: PROCEEDINGS OF THE 2016 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, 2016, : 1021 - 1028
  • [23] Interactive Evolutionary Multiobjective Optimization using Dominance-based Rough Set Approach
    Greco, Salvatore
    Matarazzo, Benedetto
    Slowinski, Roman
    2010 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2010,
  • [24] Interactive Multiobjective Optimization: A Review of the State-of-the-Art
    Xin, Bin
    Chen, Lu
    Chen, Jie
    Ishibuchi, Hisao
    Hirota, Kaoru
    Liu, Bo
    IEEE ACCESS, 2018, 6 : 41256 - 41279
  • [25] Using Indifference Information in Robust Ordinal Regression
    Branke, Juergen
    Corrente, Salvatore
    Greco, Salvatore
    Gutjahr, Walter J.
    EVOLUTIONARY MULTI-CRITERION OPTIMIZATION, PT II, 2015, 9019 : 205 - 217
  • [26] Robust ordinal regression in preference learning and ranking
    Salvatore Corrente
    Salvatore Greco
    Miłosz Kadziński
    Roman Słowiński
    Machine Learning, 2013, 93 : 381 - 422
  • [27] Robust ordinal regression induced by lp -centroid
    Tian, Qing
    Zhang, Wenqiang
    Wang, Liping
    Chen, Songcan
    Yin, Hujun
    NEUROCOMPUTING, 2018, 313 : 184 - 195
  • [28] Multiobjective Patient Stratification Using Evolutionary Multiobjective Optimization
    Li, Xiangtao
    Wong, Ka-Chun
    IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2018, 22 (05) : 1619 - 1629
  • [29] Dimensionality reduced robust ordinal regression applied to life cycle assessment
    Balugani, Elia
    Lolli, Francesco
    Pini, Martina
    Ferrari, Anna Maria
    Neri, Paolo
    Gamberini, Rita
    Rimini, Bianca
    EXPERT SYSTEMS WITH APPLICATIONS, 2021, 178
  • [30] A repository of real-world datasets for data-driven evolutionary multiobjective optimization
    He, Cheng
    Tian, Ye
    Wang, Handing
    Jin, Yaochu
    COMPLEX & INTELLIGENT SYSTEMS, 2020, 6 (01) : 189 - 197
12345