The Grid File: An Adaptable, Symmetric Multikey File Structure
ACM Transactions on Database Systems (TODS)
Coverage and Generalization in an Artificial Immune System
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
Anomaly Detection Using Real-Valued Negative Selection
Genetic Programming and Evolvable Machines
Is negative selection appropriate for anomaly detection?
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Theoretical advances in artificial immune systems
Theoretical Computer Science
V-detector: An efficient negative selection algorithm with "probably adequate" detector coverage
Information Sciences: an International Journal
Design of an Artificial Immune System for fault detection: A Negative Selection Approach
Expert Systems with Applications: An International Journal
Negative selection algorithms on strings with efficient training and linear-time classification
Theoretical Computer Science
Review Article: Recent Advances in Artificial Immune Systems: Models and Applications
Applied Soft Computing
On the use of hyperspheres in artificial immune systems as antibody recognition regions
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
A comparative study of real-valued negative selection to statistical anomaly detection techniques
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Measuring the difficulty of distance-based indexing
SPIRE'05 Proceedings of the 12th international conference on String Processing and Information Retrieval
On Symmetric Boolean Functions With High Algebraic Immunity on Even Number of Variables
IEEE Transactions on Information Theory
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Negative selection algorithm (NSA) is an important algorithm for the generation of artificial immune detectors. However, the randomly generated candidate detectors have to be compared with the whole self set to exclude self reactive detectors. The inefficiency of the comparing process seriously limited the application of immune algorithms. Therefore, a new negative selection algorithm GF-RNSA is proposed in the paper. Firstly, the feature space is divided into a number of grid cells, and then detectors are separately generated in each cell. As candidate detectors just need to compare with the self antigens located in the same cell rather than with the whole self set, the detector training can be more efficient. The theoretical analysis demonstrated that the time complexity of GF-RNSA is effectively reduced that the exponential relationships between self size and time complexity in traditional NSAs is eliminated. The experimental results showed that: not only the time cost of negative selection, but also the time cost of data preprocess and detection are reduced, while the detection accuracy is not much declined.