DocumentCode
424238
Title
Information query immune algorithm based on vector space model
Author
Wang, Zi-qiang ; Feng, Bo-Qin
Author_Institution
Dept. of Comput. Sci., Xi´´an Jiaotong Univ., China
Volume
4
fYear
2004
fDate
26-29 Aug. 2004
Firstpage
2515
Abstract
To efficiently satisfy the user query requirements in information retrieval, a novel immune query optimization algorithm for information retrieval is proposed. The core of the immune algorithm lies on constructing the immune operator that is realized by vaccination and immune selection. The strategies and the methods of selecting and constructing a vaccine for the problem are given in the paper. Immune algorithm for query optimization introduces the immune operators to genetic algorithms for query optimization. This algorithm properly deals with the degeneration in conventional genetic algorithms, therefore increases the convergence speed. Experimental results show that the novel algorithm has higher precision and faster computation speed.
Keywords
genetic algorithms; information retrieval; genetic algorithm; immune query optimization algorithm; information query immune algorithm; information retrieval; query optimization; query requirement; vaccination; vector space model; Computer science; Evolution (biology); Genetic algorithms; Immune system; Information retrieval; Machine learning; Machine learning algorithms; Neural networks; Optimization methods; Query processing;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on
Print_ISBN
0-7803-8403-2
Type
conf
DOI
10.1109/ICMLC.2004.1382227
Filename
1382227
Link To Document