A Fuzzy Inference System to Determine the Number of Clones in a Class of Artificial Immune Systems

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.

[1]  Jonathan M. Garibaldi,et al.  Fuzzy Expert Systems , 2018, Fuzzy Logic Theory and Applications.

[2]  Alan S. Perelson,et al.  The Baldwin effect in the immune system: learning by somatic hypermutation , 1996 .

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

[4]  Brian W. Kernighan,et al.  An Effective Heuristic Algorithm for the Traveling-Salesman Problem , 1973, Oper. Res..

[5]  Lotfi A. Zadeh,et al.  Fuzzy logic, neural networks, and soft computing , 1993, CACM.

[6]  Jonathan Timmis,et al.  Artificial Immune Systems: A New Computational Intelligence Approach , 2003 .

[7]  H. Abbass,et al.  aiNet : An Artificial Immune Network for Data Analysis , 2022 .

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

[9]  T. Kohonen,et al.  Self-organizing semantic maps , 1989, Biological Cybernetics.

[10]  Fabrício Olivetti de França,et al.  Conceptual and Practical Aspects of the aiNet Family of Algorithms , 2010, Int. J. Nat. Comput. Res..

[11]  L. Zadeh.  The role of fuzzy logic in the management of uncertainty in expert systems , 1983 .

[12]  Leandro Nunes de Castro,et al.  The Clonal Selection Algorithm with Engineering Applications 1 , 2000 .

[13]  Constantin V. Negoita,et al.  On Fuzzy Systems , 1978 .

[14]  C. Berek,et al.  The maturation of the immune response. , 1993, Immunology today.

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

[16]  Gerhard Reinelt,et al.  TSPLIB - A Traveling Salesman Problem Library , 1991, INFORMS J. Comput..

[17]  C. Milstein,et al.  Maturation of the immune response. , 1996, Advances in protein chemistry.

[18]  L.N. de Castro,et al.  An artificial immune network for multimodal function optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[19]  Michel Gendreau,et al.  Handbook of Metaheuristics , 2010 .

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