An Improved Genetic Algorithm For TSP

The genetic algorithm is a kind of searching method which simulates the natural evolution. It is simple and easy to implement, especially it doesn't need the special field knowledge, so it has been used in every broad fields. Now the genetic algorithm has got a lot of fruits and more scholars begin to pay attention to it.Traveling Salesman Problem is a representative NP-Hard problem. It has very important academic significance and actual application value. So it is a research hotspot for several years. Many scholars believe there is no polynomial time completeness algorithm for the NP problem, therefore the approximate algorithm is provided with very important meaning. The TSP becomes a chief criterion for measuring the efficiency of the approximate algorithm.This paper has done some work in the research and application of the genetic algorithm. First, The introduction on GA's theory and its applications .Second, we state the history and the present situation, math models, and the solution. At last, we present the development on genetic algorithm to solve TSP. According to the TSP character, two new crossover operator, Order Insert Crossover operator and Dynamic Order Insert Crossover operator are designed, which combine Order Insert Crossover and uses the greedy selection strategy in the cross of the genetic algorithm .These operator are conducted using the local information effectively and inheriting excellent gene from the parents. It has been proved effective through the optimization computing of some example.