Genetic Algorithms (GAs) are optimization techniques inspired by the idea of evolution. They can sometimes take a long time to find the solution to a problem, but it is not always obvious when, or how to configure their various parameters. Recently, a new GA was introduced  that has a lot of potential for parallelization. This algorithm, called the Mixing Genetic Algorithm, has shown promising results on the well-known Traveling Salesman Problem. In this work, we have compared the effectiveness of the Mixing GA over a traditional GA on three discrete optimization problems: the OneMax problem and two topologies of the Ising Model (Ising Model on Tree and Ising Model on Ring). The comparison has been done for the success rate at the given time, for the given problem size and size of population. The comparison has been done for, both, serial and parallel implementations. Overall, the success rate for the Mixing GA is better than the traditional GA. We have also compared two population selection methods, namely, tournament selection and generational population selection. The tournament selection outperformed generational population selection for all the problems and problem sizes that we experimented with.