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Granularity of Knowledge Computed by Genetic Algorithms Based on Rough Sets Theory
被引：0
|作者：
Wenyuan Yang Xiaoping YeYong Tang Pingping Wei Dep of Computer Engineering Zhangzhou Institute of Technology Zhangzhou ChinaDep of Computer Science Sun YatSen University Guangzhou ChinaDep of Math and Computer Science Guizhou Institute of Education Guiyang China
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机构：
来源：
关键词：
granularity of knowledge;
Genetic Algorithms;
Pawlak Model Rough Set Theory;
information table;
D O I：
暂无
中图分类号：
TP182 [专家系统、知识工程];
学科分类号：
1111 ;
摘要：
Rough set philosophy hinges on the granularity of data, which is used to build all its basic concepts, like approximations, dependencies, reduction etc. Genetic Algorithms provides a general frame to optimize problem solution of complex system without depending on the domain of problem.It is robust to many kinds of problems.The paper combines Genetic Algorithms and rough sets theory to compute granular of knowledge through an example of information table. The combination enable us to compute granular of knowledge effectively.It is also useful for computer auto-computing and information processing.
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页码：97 / 101+121
+121
页数：6
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