Genetic algorithms (GAs) are a class of global optimization methods. It has been used to solve combinatorial problems. Among the difficulties in GAs, the parameter setting and the choice of the crossover operator adapted to the problem. In this paper, we studied the influence of these operators on the performance of the GAs by making a comparative study with different adapted operators to the Fixed Charge Transportation Problem (FCTP) and described the genetic algorithm to find an optimal solution. In addition, we proposed a new crossover operator for solving the FCTP. The experimental results show that the choice of adequate crossover is important to solve each combinatorial problem by genetic algorithm. Moreover, the GA with our developed crossover operator is more efficient.
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