Clustering using Cellular Genetic Algorithms

The goal of the clustering process is to find groups of similar patterns in multidimensional data. In this work, the clustering problem is approached using cellular genetic algorithms. The population structure adopted in the cellular genetic algorithm contributes to the population genetic diversity preventing the premature convergence to local optima. The performance of the proposed algorithm is evaluated on 13 test databases. An extension to the basic algorithm was also investigated to handle instances containing non-linearly separable data. The algorithm is compared with nine non-evolutionary classification techniques from the literature, and also compared with three nature inspired methodologies, namely Particle Swarm Optimization, Artificial Bee Colony, and the Firefly Algorithm. The cellular genetic algorithm attains the best result on a test database. A statistical ranking of the compared methods was made, and the proposed algorithm is ranked fifth overall.