This paper is the result of a literature study carried out by the authors. It is a review of the dierent attempts made to solve the Travelling Salesman Problem with Genetic Algorithms. We present crossover and mutation operators, developed to tackle the Travelling Salesman Problem with Genetic Algorithms with dierent representations such as: binary representation, path representation, adjacency representation, ordinal representation and matrix representation. Likewise, we show the experimental results obtained with dierent standard examples using combination of crossover and mutation operators in relation with path representation. Keywords: Travelling Salesman Problem Genetic Algorithms Binary representation Path representation Adjacency representation Ordinal representation Matrix representation Hybridation. 1 1 Introduction In nature, there exist many processes which seek a stable state. These processes can be seen as natural optimization processes. Over the last-This paper is the result of a literature study carried out by the authors. It is a review of the dierent attempts made to solve the Travelling Salesman Problem with Genetic Algorithms. We present crossover and mutation operators, developed to tackle the Travelling Salesman Problem with Genetic Algorithms with dierent representations such as: binary representation, path representation, adjacency representation, ordinal representation and matrix representation. Likewise, we show the experimental results obtained with dierent standard examples using combination of crossover and mutation operators in relation with path representation. Keywords: Travelling Salesman Problem　Genetic Algorithms　Binary representation　Path representation　Adjacency representation　Ordinal representation　Matrix representation　Hybridation. 1 1 Introduction In nature, there exist many processes which seek a stable state. These processes can be seen as natural optimization processes. Over the last...