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 :
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