Multi-objective test problems, linkages, and evolutionary methodologies

被引:0
作者
Deb, Kalyanmoy [1 ]
Sinha, Ankur [1 ]
Kukkonen, Saku [2 ]
机构
[1] Indian Inst Technol, Dept Mech Engn, Kanpur Genet Algorithms Lab, Kanpur 208016, Uttar Pradesh, India
[2] Lappeenranta Univ Technol, Dept Informat Technol, FIN-53851 Lappeenranta, Finland
来源
GECCO 2006: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOL 1 AND 2 | 2006年
关键词
evolutionary multi-objective optimization; linkages; test problems; recombination operator; NSGA-II; generalized differential evolution;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Existing test problems for multi-objective optimization are criticized for not having adequate linkages among variables. In most problems, the Pareto-optimal solutions correspond to a fixed value of certain variables and diversity of solutions comes mainly from a random variation of certain other variables. In this paper, we introduce explicit linkages among variables so as to develop difficult two and multi-objective test problems along the lines of ZDT and DTLZ problems. On a number of such test problems, this paper compares the performance of a number of EMO methodologies having (i) variable-wise versus vector-wise recombination operators and (ii) spatial versus unidirectional recombination operators. Interesting and useful,conclusions on the use of above operators are made from the study.
引用
收藏
页码:1141 / +
页数:2
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