The implementation of Genetic Algorithm assumes that the variables from problem can be represented in the form of binary strings typically known as Chromosomes. A feasible solution is also encoded in the form of a chromosome. The initial population for the Genetic Algorithm is a random set of chromosomes. These chromosomes are evaluated using an objective function. The chromosomes representing better solutions are given higher probability to reproduce offspring in next generation. Chromosomes with lower scores are removed from the solution set. The pseudocode for the Genetic algorithm used in our experiments is given in Table 1.