Learning using an artificial immune system
Journal of Network and Computer Applications - Special issue on intelligent systems: design and applications. Part 2
Principles of a computer immune system
NSPW '97 Proceedings of the 1997 workshop on New security paradigms
Hints for Adaptive Problem Solving Gleaned from Immune Networks
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
An Evolutionary Immune Network for Data Clustering
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
LISA '98 Proceedings of the 12th USENIX conference on System administration
Application areas of AIS: The past, the present and the future
Applied Soft Computing
Immune system approaches to intrusion detection --- a review
Natural Computing: an international journal
Theoretical advances in artificial immune systems
Theoretical Computer Science
Research on Vehicle Image Classifier Based on Concentration Regulating of Immune Clonal Selection
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 06
Associative classification with artificial immune system
IEEE Transactions on Evolutionary Computation
Review Article: Recent Advances in Artificial Immune Systems: Models and Applications
Applied Soft Computing
Quiet in class: classification, noise and the dendritic cell algorithm
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
Clonal selection algorithms: a comparative case study using effective mutation potentials
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
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The Natural Immune System (NIS) is a distributed, multi-layered, adaptive, dynamic, and life-long learning system. The Artificial Immune System (AIS) is a computational system inspired by the principles and processes of the NIS. The field of AIS has obtained some degree of success as a branch of computational intelligence since it emerged in the 1990s. In this paper, we review the models and applications proposed in the last few years. In addition, we present some challenges that the AIS is facing to really distinguish itself from other established systems, in particular, biology-inspired systems (e.g., artificial neural networks and evolutionary algorithms).