ABSTRACT In this paper, an efficient technique is proposed to solve the travelling salesman problem (TSP) using genetic algorithm. We can apply easily genetic algorithm operator to this problem and get the solution easily. Complexity is both in time and space, provided size of the problem an as integer. The solution of the traveling salesman problem is global optimum. There are number of cities and distances are given between them (cities). A Traveling salesman has to visit all of them. The salesman will start from city and after traversing the remaining cities (exactly once) he will reach to his starting position. Main objective of TSP is to find traveling sequence of cities to minimize the traveling distance so that the salesman can traverse the cities exactly one time. Initially we select parent1 & parent2 by Roulette wheel selection philosophy. We have applied one point crossover operator on the parent and produce the new child. Again we apply the mutation operator on offspring and created new child. But the no. of bits (cities) in a chromosome will be inverted by the mutation operator, that is depended on mutation probability (p m ). So, one generation contain 6 individual (chromosome). We have to count fitness (minimum cost) of the individuals in each generation. We have to select two individuals with best (min fitness) fitness for the next generation. Here we see crossover between two good solution may not always yield a better or as good a solution. But since the parents are good, so there is a probability that child will be good. Every time we have to do, identity the good solution in the population and make multiple copies of the good solution
