Artificial Immune System: State of the Art Approach

The inspiration of framing the artificially developed immune system (AIS) is done through the biological immune system which compromise of signified information processing and self-adapting system. Since it originated in the 1990s, the branch of AIS gets a significant success in the field of Computational Intelligence. Present paper insights major works in the area of AIS and explore current advancements in applied system since past years. It has been observed that the particular research focused on three major considerable algorithms of AIS: (1) clonal selection algorithms (2) negative selection algorithm (3) artificial immune networks. However, computer scientists and engineers are motivated by the biological immune system to evolve new models and problem solving approaches. Developed AIS applications in extensive amount have received a lot of researcher’s attention who were planning to establish models based on immune system and techniques in order to provide solutions for complicated problems of engineering. This paper presents a survey of current models of AIS and its algorithms.

[1]  Jongsoo Lee,et al.  GA BASED SIMULATION OF IMMUNE NETWORKS APPLICATIONS IN STRUCTURAL OPTIMIZATION , 1997 .

[2]  Min Jiang,et al.  Automatic Modeling of Complex Functions with Clonal Selection-Based Gene Expression Programming , 2007, Third International Conference on Natural Computation (ICNC 2007).

[3]  Andreas Stafylopatis,et al.  Data Mining based on Gene Expression Programming and Clonal Selection , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[4]  James Cannady,et al.  A self-adaptive negative selection approach for anomaly detection , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[5]  Hirotada Ohashi,et al.  A negative selection algorithm for classification and reduction of the noise effect , 2009, Appl. Soft Comput..

[6]  Jonathan Timmis,et al.  Artificial immune systems as a novel soft computing paradigm , 2003, Soft Comput..

[7]  Fernando José Von Zuben,et al.  Learning and optimization using the clonal selection principle , 2002, IEEE Trans. Evol. Comput..

[8]  Simon M. Garrett Parameter-free, adaptive clonal selection , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[9]  Chao-Zhen Hou,et al.  A clonal selection algorithm by using learning operator , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[10]  Xiaomeng Bian,et al.  Adaptive Clonal Algorithm and Its Application for Optimal PMU Placement , 2006, 2006 International Conference on Communications, Circuits and Systems.

[11]  Zhou Ji,et al.  Artificial immune system (AIS) research in the last five years , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[12]  Rogério de Lemos,et al.  Negative Selection: How to Generate Detectors , 2002 .

[13]  Julie Greensmith,et al.  Sensing Danger: Innate Immunology for Intrusion Detection , 2007, Inf. Secur. Tech. Rep..

[14]  Ayi Purbasari,et al.  Designing Artificial Immune System Based on Clonal Selection: Using Agent-Based Modeling Approach , 2013, 2013 7th Asia Modelling Symposium.

[15]  Hongwei Dai,et al.  Bi-direction quantum crossover-based clonal selection algorithm and its applications , 2014, Expert Syst. Appl..

[16]  Hong-Zhou Tan,et al.  A Hybrid Artificial Immune Network with Swarm Learning , 2007, 2007 International Conference on Communications, Circuits and Systems.

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

[18]  Dipankar Dasgupta,et al.  An Overview of Artificial Immune Systems and Their Applications , 1993 .

[19]  Vincenzo Cutello,et al.  Real coded clonal selection algorithm for unconstrained global optimization using a hybrid inversely proportional hypermutation operator , 2006, SAC.

[20]  Licheng Jiao,et al.  Artificial immune kernel clustering network for unsupervised image segmentation , 2008 .

[21]  Wei Wang,et al.  A Complex Artificial Immune System , 2008, 2008 Fourth International Conference on Natural Computation.

[22]  Xian-Lun Tang,et al.  A novel intrusion detection method based on clonal selection clustering algorithm , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[23]  Senhua Yu,et al.  Conserved Self Pattern Recognition Algorithm , 2008, ICARIS.

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

[25]  Giuseppe Nicosia,et al.  An Advanced Clonal Selection Algorithm with Ad-Hoc Network-Based Hypermutation Operators for Synthesis of Topology and Sizing of Analog Electrical Circuits , 2008, ICARIS.

[26]  Jiao Licheng,et al.  Immunity clonal strategies , 2003, Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications. ICCIMA 2003.

[27]  Yong Peng,et al.  Hybrid learning clonal selection algorithm , 2015, Inf. Sci..

[28]  Maoguo Gong,et al.  Differential immune clonal selection algorithm , 2007, 2007 International Symposium on Intelligent Signal Processing and Communication Systems.

[29]  Vincenzo Cutello,et al.  An Immunological Algorithm for Global Numerical Optimization , 2005, Artificial Evolution.

[30]  Mark James Neal,et al.  Meta-stable Memory in an Artificial Immune Network , 2003, ICARIS.

[31]  Maoguo Gong,et al.  Improved real-valued clonal selection algorithm based on a novel mutation method , 2007, 2007 International Symposium on Intelligent Signal Processing and Communication Systems.

[32]  Yang Hua-ling A self-adaptive negative selection approach for anomaly detection , 2006 .

[33]  Fabio A. González,et al.  TECNO-STREAMS: tracking evolving clusters in noisy data streams with a scalable immune system learning model , 2003, Third IEEE International Conference on Data Mining.

[34]  Jia Lv Study on Chaos Immune Network Algorithm for Multimodal Function Optimization , 2007, Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007).

[35]  Maoguo Gong,et al.  An efficient negative selection algorithm with further training for anomaly detection , 2012, Knowl. Based Syst..

[36]  Fernando José Von Zuben,et al.  An Immune Learning Classifier Network for Autonomous Navigation , 2003, ICARIS.

[37]  Dipankar Dasgupta,et al.  Artificial neural networks and artificial immune systems: similarities and differences , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[38]  Chen Chen,et al.  The application of artificial immune network in load classification , 2008, 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies.

[39]  H. Igarashi,et al.  A clonal selection algorithm for optimization in electromagnetics , 2005, IEEE Transactions on Magnetics.

[40]  Jamie Paul Twycross,et al.  Integrated innate and adaptive artificial immune systems applied to process anomaly detection , 2007 .

[41]  Hong Lu,et al.  A Clonal Chaos Adjustment Algorithm for multi-modal function optimization , 2008, 2008 27th Chinese Control Conference.

[42]  Mehmet Karaköse,et al.  Chaotic-based hybrid negative selection algorithm and its applications in fault and anomaly detection , 2010, Expert Syst. Appl..

[43]  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.

[44]  Jacek Dabrowski,et al.  Computer experiments with a parallel clonal selection algorithm for the Graph Coloring Problem , 2008, 2008 IEEE International Symposium on Parallel and Distributed Processing.

[45]  Alex Alves Freitas,et al.  AISEC: an artificial immune system for e-mail classification , 2003, IEEE Congress on Evolutionary Computation.