Artificial Immune Systems: Models, Applications, and challenges

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).

[1]  Alan S. Perelson,et al.  Self-nonself discrimination in a computer , 1994, Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy.

[2]  Alex Alves Freitas,et al.  An Artificial Immune System for Fuzzy-Rule Induction in Data Mining , 2004, PPSN.

[3]  Stephanie Forrest,et al.  Infect Recognize Destroy , 1996 .

[4]  Fernando José Von Zuben,et al.  An Evolutionary Immune Network for Data Clustering , 2000, SBRN.

[5]  Yoshiteru Ishida Fully distributed diagnosis by PDP learning algorithm: towards immune network PDP model , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[6]  Leandro Nunes de Castro,et al.  The Clonal Selection Algorithm with Engineering Applications 1 , 2000 .

[7]  Andrew Watkins,et al.  Exploiting immunological metaphors in the development of serial, parallel and distributed learning algorithms , 2005 .

[8]  Jonathan Timmis,et al.  Theoretical advances in artificial immune systems , 2008, Theor. Comput. Sci..

[9]  Jonathan Timmis,et al.  Exploiting Parallelism Inherent in AIRS, an Artificial Immune Classifier , 2004, ICARIS.

[10]  Jonathan Timmis,et al.  Application Areas of AIS: The Past, The Present and The Future , 2005, ICARIS.

[11]  Julie Greensmith,et al.  Quiet in Class: Classification, Noise and the Dendritic Cell Algorithm , 2011, ICARIS.

[12]  Fernando Niño,et al.  Recent Advances in Artificial Immune Systems: Models and Applications , 2011, Appl. Soft Comput..

[13]  Alex Alves Freitas,et al.  Revisiting the Foundations of Artificial Immune Systems: A Problem-Oriented Perspective , 2003, ICARIS.

[14]  Stephanie Forrest,et al.  Principles of a computer immune system , 1998, NSPW '97.

[15]  Vincenzo Cutello,et al.  Clonal Selection Algorithms: A Comparative Case Study Using Effective Mutation Potentials , 2005, ICARIS.

[16]  Alan S. Perelson,et al.  The immune system, adaptation, and machine learning , 1986 .

[17]  Siu Cheung Hui,et al.  Associative Classification With Artificial Immune System , 2009, IEEE Transactions on Evolutionary Computation.

[18]  N K Jerne,et al.  Towards a network theory of the immune system. , 1973, Annales d'immunologie.

[19]  Yang Wang,et al.  Research on Vehicle Image Classifier Based on Concentration Regulating of Immune Clonal Selection , 2008, 2008 Fourth International Conference on Natural Computation.

[20]  John E. Hunt,et al.  Learning using an artificial immune system , 1996 .

[21]  Hugues Bersini,et al.  Hints for Adaptive Problem Solving Gleaned from Immune Networks , 1990, PPSN.

[22]  Jerne Nk Towards a network theory of the immune system. , 1974 .

[23]  P. Matzinger The Danger Model: A Renewed Sense of Self , 2002, Science.

[24]  Julie Greensmith,et al.  Immune system approaches to intrusion detection – a review , 2004, Natural Computing.

[25]  Jeffrey O. Kephart,et al.  A biologically inspired immune system for computers , 1994 .

[26]  P. Matzinger Tolerance, danger, and the extended family. , 1994, Annual review of immunology.