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Efficient parallel genetic immune clonal algorithm for information retrieval from textual web documents

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成果类型:
期刊论文
作者:
Yang, Gelan;Wang, Yuanzhi;Shu, Wanneng;Jin, Huixia
通讯作者:
Shu, W.(shuwanneng@yahoo.com.cn)
作者机构:
[Jin, Huixia; Yang, Gelan] Department of Computer Science, Hunan City University, Yiyang, Hunan, 413000, China
[Shu, Wanneng] College of Computer Science, South-Central University for Nationalities, Wuhan 430074, China
[Wang, Yuanzhi] School of Computer and Information, Anqing Normal College, Anqing 246011, China
通讯机构:
College of Computer Science, South-Central University for Nationalities, China
语种:
英文
关键词:
Clonal Selection;Genetic Algorithm;Information Retrieval;Vector Space Model
期刊:
The Journal of Information and Computational Science
ISSN:
1548-7741
年:
2009
卷:
6
期:
2
页码:
837-844
机构署名:
本校为第一机构
院系归属:
信息与电子工程学院
摘要:
The large amount of digital information increasingly available in our society makes information retrieval research one of the most exciting and important fields. In this paper, a new adaptive method of mining web documents is proposed. We give an algorithm which combines genetic algorithm and clonal selection algorithm based on vector space model. This algorithm avoids the disadvantage of web documents by using pure genetic algorithm which can not be utilized accurately. Experimental results indicate that this adaptive method significantly improves the performance...

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