This document discusses using a genetic algorithm to solve the travelling salesman problem (TSP). It begins with an abstract that outlines representing TSP solutions as chromosomes, using crossover and mutation genetic operators, and selecting chromosomes with minimum costs for the next generation. It then provides more details on the genetic algorithm steps, including initializing a population, selecting parents via roulette wheel selection, applying one-point crossover and mutation, and iterating until finding an optimal solution. Experimental results on an 8 city TSP problem are presented showing minimum, maximum and average costs decreasing over generations as the genetic algorithm converges on an optimal tour.