Application of the Clonal Selection Algorithm in artificial immune systems for shape recognition

Artificial Immune Systems (AISs) emerged in 1990s. The capability of AISs for learning new information, recalling what has been learned and recognizing a decentralized pattern are reasons why numerous models have been developed, implemented and used in various types of problems. This paper describes the implementation of AISs in solving an image classification problem. The Clonal Selection Algorithm has been chosen to evolve solutions in the form of antibodies during the recognizing process. In this approach, three types of shape were treated as antigens. Two experiments have been undertaken and the results show that the algorithm can perform with an accuracy of up to 95%. As a conclusion, the study showed that the Clonal Selection Algorithm can be applied to shape recognition problems.