A Look Inside the Artificial Immune Algorithm Inspired by Clonal Selection Principle

• Autorzy: Trojanowski K. , Grzegorzewski M.
• Języki publikacji: EN

Abstrakty

EN

Artificial Immune Systems inspired by clonal selection principle (called clonal selection algorithms) have already been successfully applied to pattern recognition tasks. In this paper we present our implementation of one of them, called CLONCLAS, and discuss its behavior in application to recognition of a set of binary patterns. The algorithm performs process of learning based on a set of training data including patterns which belong to ten previously unknown classes and finally generates a group of classifiers which are able to assign the testing input patterns to appropriate classes. Our experiments were performed for a set of commonly known similarity measures of binary strings to select the most efficient of them. We also observed a phenomenon of transformation of memory contents in subsequent phases of iterated process of the system learning.

Słowa kluczowe

EN

CLONALG; clonal selection principle; CLONCLAS; pattern recognition

• Wydawca: Wydawnictwo Uniwersytetu Przyrodniczo-Humanistycznego w Siedlcach
• Czasopismo: Studia Informatica : systems and information technology
• Rocznik: 2006
• Tom: Vol. 1(7)
• Strony: 147--160
• Opis fizyczny: Bibliogr. 7 poz., rys., tab., wykr.

Twórcy

autor

Trojanowski K.

• Institute of Computer Science, Polish Academy of Sciences, ul. Ordona 21, 01-237 Warsaw, Poland

autor

Grzegorzewski M.

• Warsaw School of Information Technology ul. Newelska 6, 01-447 Warsaw, Poland

Bibliografia

• 1. Burnet F.M., (1959). The Clonal Selection Theory of Acquired Immunity. Cambridge University Press.
• 2. de Castro L.N., Von Zuben F.J., (2001). Learning and optimization using the clonal selection principle. IEEE Transactions on Evolutionary Computation, Special Issue on Artificial Immune Systems, 6(3): 239-251.
• 3. de Castro L.N., Von Zuben F.J., (2000). The clonal selection algorithm with engineering applications. GECCO’OO, Workshop on Artificial Immune Systems and their Applications, 36-37.
• 4. Nadler M., Smith E.P., (1993). Pattern Recognition Engineering, John Wiley and Sons, New York.
• 5. Newman D.J.. and Hettich S.. and Blake C.L., and Merz C.J.. (1998). UCI Repository of machine learning database [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine. CA: University of California. Department of Information and Computer Science.
• 6. White J.A.. Garret S.M.. (2003). Improved pattern recognition with artificial clonal selection? ICARIS-2003. Springer-Verlag Lecture Notes in Computer Science No.2787. 181-193.
• 7. Trojanowski K.. Jankowiak M.. (2004). Właściwości miar podobieństwa w modelu sieci idiotypowej z binarna reprezentacją wzorców. IPI PAN Reports. Nr 997. Warsaw. Poland (in Polish).