Real-Valued negative selection algorithm with variable-sized self radius

Authors:
Jinquan Zeng;Weiwen Tang;Caiming Liu;Jianbin Hu;Lingxi Peng
Affiliations:
School of Computer Science & Engineering, University of Electronic Science and Technology of China, Chengdu, China, Sichuan Communication Research Planning & Designing Co., Ltd, Chengdu, C ...;Sichuan Communication Research Planning & Designing Co., Ltd, Chengdu, China;Laboratory of Intelligent Information Processing and Application, Leshan Normal University, Leshan, China;School of Electronics & Information, Nantong University, Nantong, China;Department of Computer and Education Software, Guangzhou University, Guangzhou, China
Venue:
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
Year:
2012

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Abstract

Negative selection algorithm (NSA) generates the detectors based on the self space. Due to the drawbacks of the current representation of the self space in NSAs, the generated detectors cannot enough cover the non-self space and at the same time, cover some of the self space. In order to overcome the drawbacks, a new scheme of the representation of the self space is introduced with variable-sized self radius, which is called VSRNSA. Using the variable-sized self radius to represent the self space, we can generate the more quality detectors. The algorithm is tested using the well-known real world datasets; preliminary results show that the new approach enhances NSAs in increasing detection rates and decrease false alarm rates, and without increase in complexity.