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A FUZZY INFERENCE SYSTEM TO DETERMINE THE NUMBER OF CLONES IN A CLASS OF ARTIFICIAL IMMUNE SYSTEMS

    https://doi.org/10.1142/S1469026812500058Cited by:0 (Source: Crossref)

    Artificial immune systems are composed of techniques inspired by immunology. The clonal selection principle ensures the organism adaptation to fight invading antigens by an immune response activated by the binding of antigens and antibodies. Since the immune response must correctly allocate the available resources in order to attack an antigen with its best available antibody while trying to learning an even better one, the reproduction rate of each immune cell must be carefully determined. This paper presents a novel fuzzy inference technique to calculate the suitable number of clones for immune inspired algorithms that uses the clonal selection process as the evolutionary process. More specifically, this technique is applied to the CLONALG algorithm for solving pattern recognition tasks and to the copt-aiNet algorithm for solving combinatorial optimization tasks, particularly the Traveling Salesman Problem. The obtained results show that the fuzzy approach makes it possible to automatically determine the number of clones in CLONALG and copt-aiNet, thus eliminating this key user-defined parameter.

    References

    • L. N. de Castro and J. I. Timmis, Soft Comput. — A Fusion Found. Met. Appl. 526 (2003). Google Scholar
    • M. Otto, TrAC: Trends Anal. Chem. 9, 69 (1990). CrossrefGoogle Scholar
    • L. A. Zadeh, Fuzzy Set. Syst. 197 (1983). Google Scholar
    • L. A. Zadeh, Commun. ACM 37, 77 (1994). CrossrefGoogle Scholar
    • R. R. Hightower, F. Stephanie and A. S. Perelson, Adaptive Individuals in Evolving Populations (Addison-Wesley, 1996) pp. 159–167. Google Scholar
    • L. N. de Castro and F. J. Von Zuben, The clonal selection algorithm with engineering applications, Proc. of GECCO'00, Workshop on Artificial Immune Systems and Their Applications (2000) pp. 36–39. Google Scholar
    • L. N.   de Castro and J. I.   Timmis , Artificial Immune Systems: A New Computational Intelligence Approach ( Springer Verlag , 2002 ) . Google Scholar
    • L. N. de Castro and J. I. Timmis, Soft Comput. — A Fusion Found. Met. Appl. 526 (2003). Google Scholar
    • C. Berek and M. Ziegner, Immunol. Today 14, 400 (1993). CrossrefGoogle Scholar
    • N. K. Jerne, Ann. Immunol. (Inst. Pasteur) 125C, 373 (1974). Google Scholar
    • L. N. de Castro and F. J. Zuben, Data Mining: A Heuristic Approach, eds. H. A. Abbas, R. A. Sarker and C. S. Newton (Idea Group Publishing, 2001) pp. 231–259. CrossrefGoogle Scholar
    • L. N. de Castro and F. J. Von Zuben, IEEE Trans. Evol. Comput. 6(3), 239 (2002). CrossrefGoogle Scholar
    • T. N. Hung and M. Sugeno, Fuzzy Systems, The Handbooks of Fuzzy Sets 2 (Kluwer Academic Publishers, 1998) pp. 112–113. Google Scholar
    • L. N. de Castro and J. I. Timmis, An artificial immune network for multimodal function optimization, Proc. IEEE Congress on Evolutionary Computation (2002) pp. 699–674. Google Scholar
    • F. O. Françaet al., Handling time-varying TSP instances, Proc. IEEE Congress on Evolutionary Computation (2006) pp. 9735–9742. Google Scholar
    • F. W.   Glover and G. A.   Kochenberger , Handbook of Metaheuristics ( Kluwer Academic Publishers , 2002 ) . Google Scholar
    • L. N. de Castro and J. I. Timmis, An artificial immune network for multimodal function optimization, Proc. IEEE Congress on Evolutionary Computation1 (2002) pp. 699–674. Google Scholar
    • S. Lin and B. W. Kernighan, Oper. Res. 498 (1973). Google Scholar
    • URL1 "Neural networks benchmarks" — Machine learning databases of the Institute of Computer Science of the University of Irvine, CA, ftp://ftp.ics.uci.edu/pub/machine-learning-databases . Google Scholar
    • H. Ritter and T. Kohonen, Biol. Cybernet. 61, 241 (1989). CrossrefGoogle Scholar
    • TSPLIB — "A Traveling Salesman Problem Library," http://www.iwr.uniheidelberg.de/groups/comopt/soft/TSPLIB95/TSPLIB.html . Google Scholar
    • F. O. Françaet al., Int. J. Nat. Comput. Res. 1 (2010). Google Scholar
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