Paper Title:
A Novel Approach of Detector Generation for Real-Valued Negative Selection Algorithm
  Abstract

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
Chapter
Chapter 7: Computer Application in Design and Manufacturing (1)
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, P. H. Lou, P. Zhao, "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
$32.00
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