Genetic algorithm with Novel Crossover, selection and health check for clustering

We propose a genetic algorithm for clustering records, where the algorithm contains new approaches for various genetic operations including crossover and selection. We also propose a health check operation that finds sick chromosomes of a population and probabilistically replaces them with healthy chromosomes found in the previous generations. The proposed approaches improve the chromosome quality within a population, which then contribute in achieving good clustering solution. We use fifteen datasets to compare our technique with five existing techniques in terms of two cluster evaluation criteria. The experimental results indicate a clear superiority of the proposed technique over the existing techniques.