Evolving Clonal Adaptive Resonance Theory based on ECOS theory

The present work describes an evolution of the hybrid immune approach called Clonart (Clonal Adaptive Resonance Theory) using ECOS (Evolving Connectionist Systems) architectures. Some improvements were developed to allow the control of the growth of the clusters. Clonart´s architecture is an Evolutionary Algorithm biologically inspired on the use of the Clonal Selection Principle. Therefore, a technique inspired on ART 1 network was combined to store the best antibodies. However, these strategies may create a lot of clusters due to the ART behavior. For that reason, techniques of insertion, aggregation and pruning inspired on ECOS operation were used to control the amount of clusters in Clonart. In this way, old and unnecessary clusters may confuse the Clonart and increase the learning error rate. This behavior was especially important, because many problems need constant retraining. The effectiveness of this approach was evaluated using ten databases from UCI Machine Learning Repository.