2013 | 4(30) | 196-225
Article title

From immunology to modeling, processing and analysis of data

Title variants
Od immunologii do modelowania, przetwarzania i analiz danych
Languages of publication
Observation of nature is often the inspiration for unconventional and innovative ideas, proposals, suggestions, overall concepts for carrying out modeling phenomena and technical processes, economic, processing and analysis of statistical data. Artificial neural networks, genetic algorithms and evolutionary algorithms were created as an imitation of biological solutions. In recent years, the rapid development of immunological algorithms which are based on the action of the human immune system, has occurred. These algorithms are used in mathematics, engineering, problem solving in decision making, management, economics, finance. At the beginning of this article we describe the action of the human immune system, which is the basis for the construction of immune algorithms. Then we present the most important mechanisms influencing the operation and construction of the algorithms based on biological mechanisms of the immune system, in particular the negative selection, immune network, clonal selection. Then we describe how the elements and mechanisms of artificial immune system algorithms are represented to enable modeling, processing and analysis of data. In the last part of the article to illustrate the wide range of possibilities of using immune algorithms to solve various sorts of problems from a completely different fields, we present examples of applications that involve the assessment of bank borrowers, the task of coloring a graph belonging to combinatorial optimization problems, a multi-dimensional classification, fault detection.
  • Uniwersytet Warszawski
  • Warszawski Uniwersytet Medyczny
  • Uniwersytet Warszawski
  • Aickelin U., Cayzer S., The Danger Theory and Its Application to Artificial Immune Systems, Proceedings of the 1st Internal Conference on Artificial Immune Systems (ICARIS-2002), s. 141-148,Canterbury 2002.
  • Brabazon A., O′Neill M., Biologically Inspired Algorithms for Financial Modelling, Springer-Verlag, Berlin, Heidelberg 2006.
  • Chang S.-Y., Yeh T.-Y., An artificial immune classifier for credit scoring analysis, “Applied Soft Computing” 2012, 12, s. 611-618.
  • Dasgupta D., Niño L.F., Immunological Computation. Theory and Applications, Taylor & Francis Group, LLC, (Auerbach/CRC is an imprint of Taylor & Francis Group), Boca Raton, London, New York 2009.
  • Dasgupta D., Yu S., Nino F., Recent advances in artificial immune systems: Models and applications, “Applied Soft Computing” 2011, 11, s. 1574-1587.
  • De Castro L.N., Fundamentals of Natural Computing. Basic Concepts, Algorithms, and Applications, Chapter 6: Immunocomputing, Taylor & Francis Group, LLC, (Chapman & Hall/CRC is an imprint of Taylor & Francis Group), Boca Raton, London, New York 2006.
  • De Castro L.N., Von Zuben F.J., Learning and optimization using the clonal selection principle, “IEEE Transactions on Evolutionary Computation” 2002, 6 (3), s. 239-251.
  • De Castro L.N, Von Zuben F.J., The Clonal Selection Algorithm with Engineering Applications, Genetic and Evolutionary Computation Conference GECCO’OO – Workshop Proceedings, Las Vegas 2000.
  • Forrest S., Perelson A.S., Allen L., Cherukuri R., Self-Nonself Discrimination in a Computer, IEEE Symposium on Research in Security and Privacy, Los Alamos 1994.
  • Gołąb J., Jakóbisiak M., Lasek W., Stokłosa T. (red.), Immunologia, Wydawnictwo Naukowe PWN, wyd. 6, Warszawa 2012.
  • Hunt J.E., Cooke D.E., Learning using an artificial immune system, “Journal of Network and Computer Applications” 1996, 19, s. 189-212.
  • Ishida Y., Fully Distributed Diagnosis by PDP Learning Algorithm: Towards Immune Network PDP Model, IEEE International Joint Conference on Neural Networks, San Diego 1990.
  • Kołodziejczyk J., Obliczenia z wykorzystaniem sztucznej inteligencji, wykład V – Sztuczne systemy immunologiczne, [dostęp: 29.12.2012].
  • Lasek W., Immunologia. Podstawowe zagadnienia i aktualności, wyd. 2, Wydawnictwo Naukowe PWN, Warszawa 2009.
  • Laurentys C.A., Ronacher G., Palhares R.M., Caminhas W.M., Design of an artificial immune system for fault detection: A negative selection approach, “Expert Systems with Applications” 2010, 37, s. 5507-5513.
  • Płaskonka J., Zastosowanie sztucznych systemów immunologicznych w zagadnieniach optymalizacji, 2011,łaskonka_Immune_Systems.pdf [dostęp: 28.12.201].
  • Praczyk T., Zastosowanie sztucznych systemów immunologicznych do wykrywania ech radarowych o niskim poziomie sygnału, „Logistyka” 2011, 6, s. 3461-3467.
  • Symantec. The Digital Immune System. Enterprise-Grade Anti_Virus Automation in the 21st century, Technical Brief, [dostęp: 28.12.2012].
  • Świtalska A., Sztuczne systemy immunologiczne – zastosowanie w optymalizacji kombinatorycznej, [dostęp: 28.12.2012].
  • Timmis J., Neal M., Hunt J., An artificial immune system for data analysis, “BioSystems” 2000, 55, s. 143-150.
  • Wajs W., Święcicki M., Wais P., Wojtowicz H., Janik P., Zastosowanie sieci immunologicznej do drążenia danych. Data Mining with Artificial Immune System, “Bio-Algorithms and Med-Systems.
  • Journal edited by Medical College – Jagiellonian University” 2005, vol. 1, no. 1/2, s. 51-56.
  • Wierzchoń S.T., Sztuczne systemy immunologiczne. Teoria i zastosowania, Akademicka Oficyna Wydawnicza EXIT, Warszawa 2001.
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