| The genetic algorithm is a kind of searching method which simulates the natural evolution. It is simple and easy to implement, especially it dosen't need the special field knowledge and the search only dispend on the fitness, so it has been used in many broad fields. Now the genetic algorithm has got a lot of fruits and more scholars began to pay attention to it. At present, the genetic algorithm has become an important embranchment of the evolutionary computation.The genetic algorithm is still a new developing technology. Despite its success in many domains, its theoretical fundament is relatively weak. There are still lots of problems to be studied and improved. The main works of the paper are as follows:Firstly, briefly introduce the standard genetic algorithm in the general situation of the development, basic concept, basic principle, rationale, convergency, character and its application. The paper summarise detailedly the realization technology about the standard genetic algorithm.Secondly, introduce the mathematical model of the TSP problems and its traditional methods and intelligent optimization; present the concretely analysis and explainations of the TSP problems which based on the genetic algorithm. The explainations make the preparation for using genetic algorithm to solve TSP problems.Finally, At the base of the standard genetic algorithm, combined with the simulated annealing algorithm, we present the genetic annealing evolutionary algorithm; combined with the subarea technique and the 2-opt stochastic search, we present a hybrid genetic algorithm based on subarea to solve large-scale TSP problems; combined with chaos optimization, we propose to the optimization of the function with genetic algorithm based on chaos optimization. The paper simulated these algorithms in MATLAB, compared the results with the traditatonal algorithms, the numerical simulating results indicate that these algorithms proposed are effective in the computation precision and speed. |
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