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Multiple-Point Bit Mutation Method of Detector Generation for SNSD Model.

Authors :
Wang, Jun
Yi, Zhang
Zurada, Jacek M.
Lu, Bao-Liang
Yin, Hujun
Tan, Ying
Source :
Advances in Neural Networks - ISNN 2006 (9783540344827); 2006, p340-345, 6p
Publication Year :
2006

Abstract

In self and non-self discrimination (SNSD) model, it is very important to generate a desirable detector set since it decides the performance and scale of the SNSD model based task. By using the famous principle of negative selection in natural immune system, a novel generating algorithm of detector, multiple-point bit mutation method, is proposed in this paper. It utilizes random multiple-point mutation to look for non-self detectors in a large range in the whole space of detectors, such that we can obtain a required detector set in a reasonable computation time. This paper describes the work procedure of the proposed detector generating algorithm. We tested the algorithm by using many datasets and compared it with the Exhaustive Detector Generating Algorithm in details. The experimental results show that the proposed algorithm outperforms the Exhaustive Detector Generating Algorithm both in computational complexities and detection performance. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISBNs :
9783540344827
Database :
Supplemental Index
Journal :
Advances in Neural Networks - ISNN 2006 (9783540344827)
Publication Type :
Book
Accession number :
32862421
Full Text :
https://doi.org/10.1007/11760191_50