Multi-objective Optimization Algorithm Based on Gene Expression Programming and Virus Evolution

被引：0
|
作者：
Wang, Weihong [1 ]
Du, Yanye [1 ]
Li, Qu [2 ]
机构：
[1] Zhejiang Univ Technol, Coll Comp Sci, Hangzhou, Zhejiang, Peoples R China
[2] Zhejiang Univ, Coll Comp Sci, Zhejiang, Peoples R China
来源：
ADVANCED RESEARCH ON AUTOMATION, COMMUNICATION, ARCHITECTONICS AND MATERIALS, PTS 1 AND 2 | 2011年 / 225-226卷 / 1-2期
关键词：
Evolutionary Multi-objective Optimization (EMO); Gene Expression Programming (GEP); Virus Evolution;
D O I：
10.4028/www.scientific.net/AMR.225-226.372
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Evolutionary Multi-objective Optimization (EMO) is a hot research direction nowadays and one of the state-of-the-art evolutionary multi-objective optimization algorithms -NSGA-II has gain wide attention and application in many fields. Gene Expression Programming (GEP) has a powerful search capability, but falls into local optimum easily. Based on the transformed GEP, NSGA-II and the virus evolution mechanism, a new multi-objective evolutionary algorithm GEP Virus NSGA-II is proposed. With the infection operation of virus population, the diversity of the host population is increased, and it's much easier to jump out of the local optimum. And this algorithm has got good experimental results on 9 standard test problems.
引用
收藏
页码：372 / +
页数：2
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