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Genetic Algorithms (GAs) are powerful general-purpose optimization search algorithms based upon the principles of evolution observed in nature. Mutation operator is one of the GA operators that used to produce new chromosomes or modify some features of it depending on some small probability value. The objective of this operator is to prevent falling of all solutions in population into a local optimum of solved problem. This paper applies several mutation methods to different non-deterministic polynomial (NP) hard problems and compares the results. The problems that will be introduced in this paper are: traveling salesman problem (TSP), 0/1 Knapsack problem, and Shubert function.