Improved genetic algorithm on attribute reducts based on variable precision rough set theory

被引：0

作者：

He, LQ ^{[1
]}

Hu, WB ^{[1
]}

Liu, QS ^{[1
]}

机构：

[1] Chongqing Univ, Automat Acad, Chongqing 400044, Peoples R China

来源：

Proceedings of the 11th Joint International Computer Conference | 2005年

关键词：

variable precision rough set; attribute reducts; genetic algorithms;

D O I：

10.1142/9789812701534_0102

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The main objective of the paper is to introduce a new algorrithm of attribute reducts based on variable precision rough set theory. An improved genetic algorithm(GA) which adopts information entropy as it's fitness function is introduced. The strategy of mixed crossover and two points mutation enlarges the search scope. The cross generation elicit selection and self-adapting strategy make the genetic algorithm converge to the overall optimal solution stably and quickly, which gives it an edge over the normal GA. The effectiveness and the advantage with respect to the norm GA are checked though an example.

引用

页码：451 / 454

页数：4