A Novel Approach of Detector Generation for Real-Valued Negative Selection Algorithm

Abstract:

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The detector sets generated by Real-Valued Negative Selection Algorithm (RNSA) are usually numerous, without optimization, and can not work under real-time condition. Thus, a novel approach of detector generation for RNSA based on Clonal Selection and Neighborhood Search (CSNS-RNSA) is proposed. Clonal selection of the immune mechanism is introduced to implement global search in a quasi-random sequence. The Gaussian mutation operator is proposed to get the global optimal detection sets of N-dimensional space through Neighborhood search. The resulting detector sets achieved a good coverage of non-self space, and also significantly reduced the number of detector sets, thus overcome the limitations of original RNSA. Finally, experiments verify the effectiveness of the algorithm.

Info:

Periodical:

Edited by:

Dongye Sun, Wen-Pei Sung and Ran Chen

Pages:

3736-3740

DOI:

10.4028/www.scientific.net/AMM.121-126.3736

Citation:

R. H. Hu et al., "A Novel Approach of Detector Generation for Real-Valued Negative Selection Algorithm", Applied Mechanics and Materials, Vols. 121-126, pp. 3736-3740, 2012

Online since:

October 2011

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Price:

$35.00

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