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.