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Genetic algorithm is a soft computing method that works on set of solutions. These solutions are called chromosome and the best one is the absolute solution of the problem. Unfortunately, most of the genetic algorithms are implemented in software and less works have been done on hardware implementation. Our work implements genetic algorithm in hardware. In this work, most of genetic operators are implemented and genetic diversity is preserved. Genetic diversity causes that not only don't this algorithm converge to local optimum but also reaching to global optimum. Without any doubts, proposed approach is so faster than software implementations. Evaluation results also show the proposed approach using diversity in alternate generations is faster than hardware ones.