CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Artificial Immune System: State of the Art Approach

by Prashant Kamal Mishra, Mamta Bhusry
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 120 - Number 20
Year of Publication: 2015
Authors: Prashant Kamal Mishra, Mamta Bhusry
10.5120/21344-4357

Prashant Kamal Mishra, Mamta Bhusry . Artificial Immune System: State of the Art Approach. International Journal of Computer Applications. 120, 20 ( June 2015), 25-32. DOI=10.5120/21344-4357

@article{ 10.5120/21344-4357,
author = { Prashant Kamal Mishra, Mamta Bhusry },
title = { Artificial Immune System: State of the Art Approach },
journal = { International Journal of Computer Applications },
issue_date = { June 2015 },
volume = { 120 },
number = { 20 },
month = { June },
year = { 2015 },
issn = { 0975-8887 },
pages = { 25-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume120/number20/21344-4357/ },
doi = { 10.5120/21344-4357 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:06:44.398884+05:30
%A Prashant Kamal Mishra
%A Mamta Bhusry
%T Artificial Immune System: State of the Art Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 120
%N 20
%P 25-32
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

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.

References
  1. D. Dasgupta, J. Zhou and F. Gonzalez, "Artificial immune system (AIS) research in the last five years," proceedings of the IEEE Congress Evolutionary Computation (pp. 123 – 130 vol. 1, Year of Publication: 2003).
  2. Dipankar Das gupta, "An overview of artificial immune systems and their applications", In Dipankar Das gupta (Ed. ), Artificial Immune Systems and Their Applications, (Berlin Heidelberg : Springer-Verlag, 1999, pp. 3–21).
  3. Burnet F. M. , The Clonal Selection Theory of Acquired Immunity. Nashville, Vanderbilt University Press in 1959.
  4. Ayara M. , Timmis J. , Castro L. de, and Duncan R. , Negative Selection: How to Generate Detectors. In 1st International Conference on Artificial Immune Systems, pp. 89-98, September, 2002.
  5. Jerne N. K. , Towards a Network Theory of the Immune System, Annales Immunology, vol. 125C, pp. 373–389, 1974.
  6. L. N. d. Castroand J. Timmis,"Artificial Immune Systems as a Novel Soft Computing Paradigm". Soft Computing Journal, Vol. 7, Issue 8, pp. 526-544, 2003.
  7. L. N. d. Castro and F. J. V. Zuben, Learning and optimization using the clonal selection principle, IEEE Transaction Evolutionary Computation, vol. 6, no. 3,pp. 239-251, Jun. 2002
  8. S. Garrett, Parameter-Free Adaptive Clonal Selection, Congress on Evolutionary Computation, Vol: 1, pp. : 1052- 1058, 2004.
  9. L. Ruochen , D. Haifeng and J. Licheng, Immunity Clonal Strategies, Proceedings of Fifth International Conference on Computational Intelligence and Multimedia applications,( pages 290- 295 Year of Publication: 2003 ).
  10. Y. Yu and C. Hou, A Clonal Selection Algorithm By Using Learning Operator, Proceedings of the Third IEEE International Conference on Machine Learning and Cybernetics, Shanghai, (pages 2924 - 2929 vol. 5 Year of Publication: 2004).
  11. F. Campelo, F. Guimaraes, H. Igarashi and J. Ramirez, A Clonal Selection Algorithm for Optimization in Electromagnetics, IEEE Transactions on Magnetics, VOL. 41, NO. 5, pp. 1736 – 1739, 2005.
  12. V. Cutello, G. Narzisi, G. Nicosia, and M. Pavone, An Immunological Algorithm for Global Numerical Optimization, Artificial Evolution: 7th International Conference, Evolution Artificielle, Springer, LNCS 3871:284-295, 2005.
  13. X. Bian and J. Qiu, Adaptive Clonal Algorithm and Its Application for Optimal PMU Placement, Proceedings of International Conference of IEEE on Communications, Circuits and Systems, ( pages: 2102-2106 Year of Publication: 2005).
  14. V. Cutello, G. Nicosia and M. Pavone, Real Coded Clonal Selection Algorithm for Unconstrained Global Optimization using a Hybrid Inversely Proportional Hyper mutation Operator, The 21st Annual ACM Symposium on Applied Computing, vol. 2, pp. 950-954, 2006.
  15. M. Gong, L. Jiao, L. Zhang, and W. Ma, Improved Real-Valued Clonal Selection Algorithm Based On A Novel Mutation Method, Proceedings of International Symposium on Intelligent Signal Processing and Communication Systems ( pages Year of Publication: 2007 ).
  16. M. Gong, L. Zhang, L. Jiao and W. Ma, Differential Immune Clonal Selection Algorithm, Proceedings of International Symposium on Intelligent Signal Processing and Communication Systems( pages Year of Publication: 2007 ).
  17. J. Dabrowski and M. Kubale, Computer Experiments with a Parallel Clonal Selection Algorithm for the Graph Coloring Problem, IEEE International Symposium on Parallel and Distributed Processing, pp. 1-6, 2008.
  18. H. Lu and M. Zhichun, A Clonal Chaos Adjustment Algorithm for Multi-modal Function Optimization, Proceedings of the 27th Chinese Control Conference, pages , 2008.
  19. A. Ciccazzo, P. Conca, G. Nicosia, G. Stracquadanio, An advanced clonal selection algorithm with ad hoc network-based hypermutation operators for synthesis of topology and sizing of analog electrical circuits, in 7th International Conference on Artificial Immune Systems, Phuket, Thailand, 2008.
  20. Purbasari, A. , Iping, S. S. ; Santoso, O. S. ; Mandala, R. , Designing Artificial Immune System Based on Clonal Selection: Using Agent-Based Modeling Approach, in 7th asia Modelling Symposium (AMS), pages 11-15, 2013.
  21. Hongwei Dai, Yu Yang, Hui Li, Cunhua Li, Bi-direction quantum crossover-based clonal selection algorithm and its applications, in Elsevier's Expert Systems with Applications, Volume 41, Issue 16, Pages 7248–7258, 2014.
  22. Yong Peng and Bao-Liang Lu , Hybrid learning clonal selection algorithm, Information Sciences, vol. 296(1) pages 128-146, 2015.
  23. S. Forrest, A. S. Perelson, L. Allen, and R. Cherukuri, Self-Nonself Discrimination in a Computer. In Proceedings of IEEE Symposium on Research in Security and Privacy (pages 202-212 Year of Publication: 1994).
  24. L. Gonzalez and J. Cannady, A Self-Adaptive Negative Selection Approach for Anomaly Detection, Congress on Evolutionary Computation, Volume 2, Page(s): 1561 – 1568, 2004.
  25. K. Igawa and H. Ohashi, A Negative Selection Algorithm for Classification and Reduction of the Noise Effect, Application Soft Computation, 2008.
  26. Ilhan Aydin, , Mehmet Karakose , Erhan Akin , Chaotic-based hybrid negative selection algorithm and its applications in fault and anomaly detection, in Expert Systems with Applications Volume 37, Issue 7, Pages 5285–5294, 2010.
  27. Maoguo Gong, , , Jian Zhang, Jingjing Ma, Licheng Jiao, An efficient negative selection algorithm with further training for anomaly detection, in Knowledge-Based Systems, Volume 30, June 2012, Pages 185–191, 2012.
  28. M. Neal, Met-Stable Memory in an Artificial Immune Network, Proceedings of the 2nd International Conference on Artificial Immune Systems, Springer (pages 168-180 Year of Publication: 2003).
  29. A. Secker, A. Freitas, and J. Timmis, AISEC: an Artificial Immune System for E-mail Classification, Proceedings of the Congress on Evolutionary Computation of IEEE, (pages 131-139 Year of Publication: 2003).
  30. J. LV, Study on Chaos Immune Network Algorithm for Multimodal Function Optimization, Fourth International Conference on Fuzzy Systems and Knowledge Discovery, Vol. 3, On page(s): 684-689, 2007.
  31. Wenlong Huang, , Licheng Jiao, "Artificial immune kernel clustering network for unsupervised image segmentation", in Progress in Natural Science, Volume 18, Issue 4, Pages 455–461,2008.
  32. S. Yu, D. Dasgupta, Conserved Self Pattern Recognition Algorithm, in: 7th International Conference on Artificial Immune Systems, Phuket, Thailand, 2008.
  33. U. Aickelin, J. Green smith, Sensing Danger: Innate Immunology for Intrusion Detection, The University of Nottingham, Nottingham, UK, 2007.
  34. J. P. Twycross, Integrated innate and adaptive artificial immune systems applied to process anomaly detection, in: School of Computer Science, University of Nottingham, UK, Nottingham, 2007.
  35. W. Wang, S. Gao, Z. Tang, A complex artificial immune system, in: 2008 Fourth International Conference on Natural Computation, 2008.
  36. P. Hajela, J. Yoo, and J. Lee, GA based simulation of immune networks – applications in structural optimization. Journal of Engineering Optimization, no. 29, pp. 131–149, 1997.
  37. D. Dasgupta, Artificial neural networks and artificial immune systems: Similarities and differences. In IEEE International Conference on Systems, Man and Cybernetics, Orlando, FL, pp. 873–878, 1997.
  38. Nasraoui O. , Uribe C. , Coronel C. , and Gonzalez F. , TECHNO-STREAMS: Tracking Evolving Clusters in Noisy Data Streams with a Scalable Immune System Learning Model. Proceedings of the Third IEEE International Conference on Data Mining,(page(s) 235- 242 Year of Publication: 2003).
  39. Vergas P. , Castro L. de, Michelan R. , and F. Zuben, An Immune Learning Classifier Network for Autonomous Navigation, International Conference on Artificial Immune Systems, Edimburgo. Lecture Notes in Computer Science. Berlin Heidelberg: Springer-Verlag, v. 2787. pp. 69 – 80, 2003.
  40. Xian J. , Lang F. , and Tang X. , A Novel Intrusion Detection Method Based on Clonal Selection Clustering Algorithm, Proceedings of the 4th International Conference on Machine Learning and Cybernetics, Vol. 6, On page(s): 3905- 3910 Vol. 6, 2005.
  41. Karakasis V. and Stafylopatis A. , Data Mining based on Gene Expression Programming and Clonal Selection", 2006 IEEE Congress on Evolutionary Computation, Canada, July 16-21, on page(s): 514-521. , 2006.
  42. Fu J. , Li Z. , and Tan H. , 2007. A Hybrid Artificial Immune Network with Swarm Learning. 2007 International Conference on Communications, Circuits and Systems (ICCCAS'07), 11-13 July, Kokura, on page(s): 910-914.
  43. Gan Z. , Li G. , Yang Z. , and Jiang M. , 2007. Automatic Modeling of Complex Functions with Clonal Selection-based Gene Expression Programming. Third International Conference on Natural Computation (ICNC 2007), Haikou, 24-27 Aug. , Volume: 4, On page(s): 228-232.
  44. Gu Danzhen, Ai Qian ; Chen Chen, "The application of artificial immune network in load classification", in the 3rdElectric Utility Deregulation and Restructuring and Power Technologies of IEEE, Page(s): 1394 – 1398, 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Artificial immune systems clonal selection negative selection immune networks.

Powered by PhDFocusTM