Drift analysis and average time complexity of evolutionary algorithms
Artificial Intelligence
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
An Immunological Approach to Change Detection: Theoretical Results
CSFW '96 Proceedings of the 9th IEEE workshop on Computer Security Foundations
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
An Immunological Approach to Change Detection: Algorithms, Analysis and Implications
SP '96 Proceedings of the 1996 IEEE Symposium on Security and Privacy
A study of drift analysis for estimating computation time of evolutionary algorithms
Natural Computing: an international journal
An evolutionary algorithm to generate hyper-ellipsoid detectors for negative selection
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A hardware/software partitioning algorithm based on artificial immune principles
Applied Soft Computing
A negative selection algorithm for classification and reduction of the noise effect
Applied Soft Computing
Immunological Computation: Theory and Applications
Immunological Computation: Theory and Applications
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Review Article: Recent Advances in Artificial Immune Systems: Models and Applications
Applied Soft Computing
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Evolutionary Negative Selection Algorithms have been proposed and used in artificial immune system community for years. However, there are no theoretical analyses about the average time complexity of such algorithms. In this paper, the average time complexity of Evolutionary Negative Selection Algorithms for anomaly detection is studied, and the results demonstrate that its average time complexity depends on the self set very much. Some simulation experiments are done, and it is demonstrated that the theoretical results approximately agree with the experimental results. The work in this paper not only gives the average time complexity of Evolutionary Negative Selection Algorithms for the first time, but also would be helpful to understand why different immune responses (i.e. primary/cross-reactive/secondary immune response) in biological immune system have different efficiencies.