A Genetic Algorithm with Self-sizing Genomes for Data Clustering in Dermatological Semeiotics

Medical semeiotics often deals with patient databases and would greatly benefit from efficient clustering techniques. In this paper a new evolutionary algorithm for data clustering, the Self-sizing Genome Genetic Algorithm, is introduced. It does not use a priori information about the number of clusters. Recombination takes place through a brand-new operator, i.e., gene-pooling, and fitness is based on simultaneously maximizing intra-cluster homogeneity and inter-cluster separability. This algorithm is applied to clustering in dermatological semeiotics. Moreover, a Pathology Addressing Index is defined to quantify utility of the clusters making up a proposed solution in unambiguously addressing towards pathologies.