GA Implementation in traveling salesman problem

The GA is used to solve the Traveling salesman problem that is a well-known combinatorial problem using novel crossover approaches. Traveling salesman problem is a hybrid computing framework under the broader scope of operation equivalent to the standard transport real-world problems [11]. For applying GA in traveling salesman problems, the encoding mechanism generates a sequence which is then considered a chromosome comprised of a set of items. Such pieces are recognized as genes that make up the chromosomes. The chromosomes are made up of the gene. Selection, crossover, and mutation are the steps of the GAs used in the Traveling Salesman Problems. Chromosomes are the sequence of places reached by salespersons. Consider the following example, S = (S1, S2, S3, …, Sn) implies that salespersons travel from S1 to S2, S2 to S3, S3 to Sn. There are six places that the salesman will be going through. These places are P1, P2, P3, P4, P5, and P6. This trip starts at P1 and finishes at the same place P1 [12]. Figure 6 indicates the distance between places. The salesman travels according to table 1.