Investigation of a New Artificial Immune System Model Applied to Pattern Recognition

The discovery of new functionalities through the study of human physiology has contributed toward the evolution of Artificial Immune Systems. The present work investigates a new architecture through observations of natural immunological behavior, for which application to known algorithms contributed toward an improved performance. This paper considers a boarding where the antibodies are grouped in an organized way and from an evolutionary process the antibodies that belong to these groupings can improve the adaptive immune reply to a determined antigen. Thus, antibodies of the same class are in the same grouping. Others techniques were implemented such as Clonalg, MLP and K-NN to compare this new model.