Learning Matrices of Evolutionary Operators in Genetic Algorithm

被引：0

作者：

Hao, Guo-Sheng ^{[1
]}

Chen, Chang-Shuai ^{[1
]}

Ling, Ping ^{[1
]}

Zhang, Zhao-Jun ^{[2
]}

Zou, De-Xuan ^{[2
]}

Huang, Yong-Qing ^{[3
]}

机构：

[1] Jiangsu Normal Univ, Sch Comp Sci & Technol, Xuzhou, Jiangsu, Peoples R China

[2] Jiangsu Normal Univ, Sch Elect Engn & Automat, Xuzhou, Jiangsu, Peoples R China

[3] Tongling Univ, Sch Math & Comp, Tongling, Anhui, Peoples R China

来源：

2015 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION | 2015年

关键词：

Genetic algorithm; Learning; Crossover; Mutation; Matrix; OPTIMIZATION; SEARCH; EFFICIENT;

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：

Learning is the core of intelligence algorithm. Genetic algorithm (GA), as an intelligent algorithm, has its own learning mechanism. This paper focuses on the learning matrix of evolutionary operators in GA. From the viewpoint of solution generation, the learning mechanism in GA is studied and the matrix expression of recombination and mutation is given. A new insight of GA from learning viewpoint is provided and paves necessary study foundation for studying of the learning mechanism of GA.

引用

页码：2394 / 2399

页数：6

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