Applied Research Of Genetic Algorithm And Its Using In Traveling Salesman Problem

Genetic Algorithm (GA) is an algorithm which is highly parallel, stochastic and auto-adapted searching. It is profits from one kind which the biological choice and the evolution mechanism. Especially, it qualifies in the questions that complex and non-linear for tradition searching algorithm. Its two most major outstanding features are conceal parallelism and the global search. To the genetic algorithm and the application research of it is one hot spot of the intelligent computation stratosphere.The TSP question is one of most classical NP-hard combination optimization questions, and it is also a standard question to test algorithm performance. In the reality, there are many application questions can be summed up or converted into TSP. Therefore solve this problem is significance with both the theory and practical. To large-scale problems, the traditional solution method is too inadequate. In recent years, the researchers have used some new algorithms, such as GA, SA, and AS They achieved good results by these algorithms.The main work and innovations:1. Used the most short-path's mathematics nature and statistics rule to designing crossover operator and mutation operator, and obtained heuristic order crossover operator and heuristic mutation operator, and compared these operators with OX, CX and ERC. Experimented on the dynamic properties of size of the gene, probability of mutation and crossover, and analyzed doped operator and the optimal solution for each generation entry and exit evolution of the algorithm.2. When the program was programmed, the existing data structure and algorithm of the STL and Boost were extensive used, and the design pattern knowledge was used to make programming more flexible and efficient.3. Applied the advanced GA to holes drilling order of machining, and achieved good practical results.