Title :
On convergence of Evolutionary Negative Selection Algorithms for anomaly detection
Author :
Luo, Wenjian ; Guo, Peng ; Wang, Xufa
Author_Institution :
Dept. of Comput. Sci. & Technol., Univ. of Sci. & Technol. of China, Hefei
Abstract :
Evolutionary negative selection algorithms (ENSAs) are proposed by combining negative selection model and evolutionary operators. In this paper, the convergence of ENSAs with two different mutation operators is analyzed. The first mutation operator is that only one bit of a detector is selected and flipped with a high probability. The second mutation operator is that every bit of a detector has a positive probability to be flipped. The analysis results show that the ENSAs with different mutation operators have different convergent properties. Especially, the shape of the self set will affect the convergence of ENSAs with the first mutation operator.
Keywords :
artificial immune systems; evolutionary computation; anomaly detection; artificial immune system; evolutionary negative selection algorithms; evolutionary operators; mutation operator; negative selection model; Convergence; Evolutionary computation;
Conference_Titel :
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1822-0
Electronic_ISBN :
978-1-4244-1823-7
DOI :
10.1109/CEC.2008.4631193