Abstract: This study presents a new clustering algorithm called Robust Genetic Algorithm with Chromosome Reorganization (Robust GACR) based on genetic methodology. The discussion details key aspects of the proposed methodology, including a chromosome reorganization method and a new crossover operator that exploits a measure of similarity between chromosomes. Adaptive probabilities of crossover and mutation are employed to prevent the convergence of the GA to a local optimum. The performance of the Robust GACR algorithm, GCA, KGA, GA clustering and K-means algorithm are compared through the experiments based on several artificial data sets and real data sets. The K-means is unable to provide the correct clustering. However, the KGA and GCA are correctly clustered only some data sets, but they are unable to detect the correct clusters in all data sets. The Robust GACR is able to detect the clusters reasonably well in all data sets.