摘要：Genetic algorithms are a part of evolutionary computing, which is a rapidly growing area of artificial intelligence and finds approximate or accurate solutions to searching problems. The main goal of this work is to give a clear picture of the implementation of genetic algorithm in travelling sales man problem where a traveler has to visit all the cities exactly once. The traveling salesman problem is given finite number of cities along with the cost of travel between each pair of the cities; the solution is to find the cheapest way of visiting all the cities and returning to the starting point. All cities are considered as unique gene and cannot be duplicated. Parent cities are selected according to their fitness. The better cities are the more chances to be selected. The basic idea of mutation in traveling salesman problem is to randomly select two cities and swap them if the new swapped tour length is shorter than the old one. Repeating the processes of selection and mutation cross over provides exact solution to find the shortest path in travelling sales man problem
