Evolutionary many-objective optimization

被引：0
|
作者：
Ishibuchi, Hisao [1 ]
Tsukamoto, Noritaka [1 ]
Nojima, Yusuke [1 ]
机构：
[1] Osaka Prefecture Univ, Grad Sch Engn, Dept Comp Sci & Intelligent Syst, Naka Ku, Osaka 5998531, Japan
来源：
2008 3RD INTERNATIONAL WORKSHOP ON GENETIC AND EVOLVING FUZZY SYSTEMS | 2008年
关键词：
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：
In this paper, we first explain why many-objective problems are difficult for Pareto dominance-based evolutionary multiobjective optimization algorithms such as NSGA-H and SPEA. Then we explain recent proposals for the handling of many-objective problems by evolutionary algorithms. Some proposals are examined through computational experiments on multiobjective knapsack problems with two, four and six objectives. Finally we discuss the viability of many-objective genetic fuzzy systems (i.e., the use of many-objective genetic algorithms for the design of fuzzy rule-based systems).
引用
收藏
页码：45 / 50
页数：6
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