A fuzzy inference system to determine the number of clones in the clonal selection algorithm
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Artigo de evento
Date
2010
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Proceedings - 2010 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC 2010
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0
Authors
Carraro L.A.
De Castro L.N.
De Re A.M.
De Castro L.N.
De Re A.M.
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Abstract
Artificial Immune Systems (AISs) 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. As an immune response can be elicited even when the binding between an antigen and an antibody is not perfect, an approximate binding might suffice, and a Fuzzy Logic mechanism might be the most appropriate mechanism to control such process. This paper presents a novel hybrid model based on concepts of Immune and Fuzzy Systems with applications to pattern recognition problems. The preliminary results obtained here suggest the proposed model is a promising pattern recognition tool. © 2010 IEEE.
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Keywords
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Artificial Immune System , Clonal selection , Clonal selection algorithms , Clonal selection principle , Fuzzy inference systems , Hybrid model , Immune response , Pattern recognition problems