Genetic Algorithms (GAs) are powerful optimization techniques inspired by the principles of natural selection and genetics. One critical aspect of their success lies in the diversity of solutions within their populations. Diversity ensures exploration of a broader solution space, preventing premature convergence to sub-optimal solutions. Consequently, the evaluation and maintenance of diversity metrics in GA populations have garnered significant attention in the field of evolutionary computation. This article presents the efficiency of diploid genetic algorithms against the haploid variant through measures of population diversity.