The Effect of Initial Population Sampling on the Convergence of Multi-Objective Genetic Algorithms

被引：0
|
作者：
Poles, Silvia [1 ]
Fu, Yan [1 ]
Rigoni, Enrico [1 ]
机构：
[1] ESTECO, Area Sci Pk,Padriciano 99, I-34012 Trieste, Italy
来源：
MULTIOBJECTIVE PROGRAMMING AND GOAL PROGRAMMING: THEORETICAL RESULTS AND PRACTICAL APPLICATIONS | 2009年 / 618卷
关键词：
Convergence; Initial population; MOGA-II; Multi-objective genetic algorithms; NSGA-II;
D O I：
暂无
中图分类号：
C93 [管理学]; O22 [运筹学];
学科分类号：
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要：
This paper aims to demonstrate that the initial population plays an important role in the convergence of genetic algorithms independently from the algorithm and the problem. Using a well-distributed sampling increases the robustness and avoids premature convergence. The observation is proved using MOGA-II and NSGA-II with different sampling methods. This result is particularly important whenever the optimization involves time-consuming functions.
引用
收藏
页码：123 / +
页数：3
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