A new genetic algorithm based on negative selection

被引：0

作者：

Li, Na-Na ^{[1
,2
]}

Gu, Jun-Hua ^{[2
]}

Liu, Bo-Ying ^{[2
]}

机构：

[1] Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China

[2] Hebei Univ Technol, Tianjin, Peoples R China

来源：

PROCEEDINGS OF 2006 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2006年

关键词：

genetic algorithm; immune system; negative selection; function optimization;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Genetic algorithm offers the common frame of resolving optimization problem by imitating biological evolution based on natural selection. However it has some drawbacks such as slow convergence and being premature. In genetic algorithm, individual generated by genetic operation is a bit random and even sometimes more inferior than its parents. So a new operator-negative selection that can filtrate bad-quality individual is introduced to genetic algorithm to speed up its speed of convergence and improve its global searching ability. With this new operator, a new optimization algorithm based genetic algorithm and negative selection is proposed. Furthermore this paper shows its ability to solve the function optimization problem.

引用

页码：4297 / +

页数：2

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