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Mathematical Problems in Engineering
Volume 2014, Article ID 324645, 12 pages
Research Article

An Immune Clonal Selection Algorithm for Synthetic Signature Generation

State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China

Received 14 March 2014; Accepted 18 May 2014; Published 2 June 2014

Academic Editor: Erik Cuevas

Copyright © 2014 Mofei Song and Zhengxing Sun. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The collection of signature data for system development and evaluation generally requires significant time and effort. To overcome this problem, this paper proposes a detector generation based clonal selection algorithm for synthetic signature set generation. The goal of synthetic signature generation is to improve the performance of signature verification by providing more training samples. Our method uses the clonal selection algorithm to maintain the diversity of the overall set and avoid sparse feature distribution. The algorithm firstly generates detectors with a segmented r-continuous bits matching rule and P-receptor editing strategy to provide a more wider search space. Then the clonal selection algorithm is used to expand and optimize the overall signature set. We demonstrate the effectiveness of our clonal selection algorithm, and the experiments show that adding the synthetic training samples can improve the performance of signature verification.