Optimal genetic query algorithm for information retrieval

Authors:
Ziqiang Wang;Boqin Feng
Affiliations:
Department of Computer Science, Xi'an Jiaotong University, Xi'an, P.R.China;Department of Computer Science, Xi'an Jiaotong University, Xi'an, P.R.China
Venue:
ISPA'04 Proceedings of the Second international conference on Parallel and Distributed Processing and Applications
Year:
2004

Citing 5
Cited 0

Machine learning for information retrieval: neural networks, symbolic learning, and genetic algorithms

Journal of the American Society for Information Science
Pivoted document length normalization

SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Applying genetic algorithms to query optimization in document retrieval

Information Processing and Management: an International Journal
Genetic Approach to Query Space Exploration

Information Retrieval
A novel genetic algorithm based on immunity

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

Quantified Score

Hi-index	0.00

Visualization

Abstract

An efficient immune query optimization algorithm for information retrieval is proposed in this paper. The main characteristics of this algorithm are as follows: The genetic individual is a query, each gene corresponds to a weighted term, immune operator is used to avoid degeneracy, local search procedure based on the concept of neighborhood is used to speed up the abilities of finding better query vector. Experimental results show that the proposed algorithm can efficiently improve the performance of the query search.