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A genetic algorithm to handle the constrained optimization problem without penalty function term is proposed. The infeasibility degree of a solution (IFD) is defined as the sum of the square value of all the constraints violation to identify the constraints violation of the solutions quantitative. At the end of general GAs operation, an infeasibility degree selection of the current population is designed by checking whether the IFD of a solution is less than or equal to a threshold or not to decide the candidate solution is accepted or rejected. The initial results of solving two typical constrained optimization problems show the promising performance of the proposed method.