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

  • Ying Tan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)

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.

Keywords

Negative Selection Detector Generate Clonal Selection Algorithm Detector Candidate Negative Selection Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ying Tan
    • 1
  1. 1.Department of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefeiP.R. China

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