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The Cuckoo Search (CS) is a metaheuristic optimization algorithm. It is very simple and has only a few parameters to adjust. Nevertheless, CS not converges easily while using Levy flight for generating a new solution. In order to overcome the problem associated with CS the Cuckoo Search with Differential Evolution (DE) is proposed to cluster the gene expression data. Here, the fractions of worst nests are destroyed and new eggs for the nests are generated by using agent position generation of DE. The global search area is enhanced through DE by finding the true global minimum. DE has fast convergence and few control parameters to adjust.