Research And Applications Based On Improved Genetic Algorithm

Since entering the 20 century, computer technology has made rapid development, change is rapid. From birth to the popularity of the computer, it spends a very short time. In this short decades, computer technology to bring the people what's good? There are innumerable, but also irreplaceable. Computer has changed people's way of life, increase the speed of the economic development of countries in the world, on the world political, economic, military and culture in various aspects of the impact profound. It can be said that people from one computer to another world, which brought the world so that people can be more comfortable and happy to live. Computer technology in many of them, on the research and application of genetic algorithm is also quite hot. As the economy continues to develop, genetic algorithm is applied to every aspect of people's lives, has an important role. This article is necessary to further study the genetic algorithm, genetic algorithm can be hoped that more widespread application.Back in the last century of research on the genetic algorithm already appeared, but the genetic algorithm is still carried out most of the improvements for the genetic algorithm research. Because there has been a genetic algorithm, a contradiction is a local optimum and the convergence rate of contradictions. In order to effectively solve the genetic algorithm convergence speed and local optimal solution of the conflict, this paper presents an improved genetic algorithm, respectively, the selection operator of genetic algorithm, crossover operator and mutation operator is improved. This work mainly in the following areas:(1) First, an overview of the working principle of genetic algorithm and genetic algorithm to generate the biological background, then introduces the brief history of the genetic algorithm, genetic algorithms and genetic algorithm development and status of the research significance.(2) Briefly introduced the basic concepts of genetic algorithms and genetic algorithm control of the main steps and parameters, and then introduces the three basic genetic algorithm operators, namely selection operator, crossover operator and mutation operator. More detailed description of their role in the genetic algorithm and mutual relationship.(3) Based on the experimental analysis of the selection operator of the improvements and crossover operators to improve the impact and role of the genetic algorithm, and then made improvements on the mutation operator, combined with experimental analysis of the three operators for the genetic improvement the impact and role of the algorithm. These three basic genetic operators to improve efficiency makes the genetic algorithm, and to a certain extent, the genetic algorithm to solve the convergence speed and local optimal solution of the conflict.Use this article to improve the selection operator can increase the genetic algorithm converge to global optimal solution of the probability, which is not easy to fall into local optimum, thus increasing the probability of finding the optimal solution. Use the crossover operator in the improved genetic algorithm can speed up the convergence rate, thus shortening the time to find the optimal solution. Use paper to improve the mutation operator can enhance the genetic algorithm out of local convergence, while a certain degree of genetic convergence speed and the optimal solution can have a greater impact. Experimental results show that the three operators to improve the binding energy of a faster speed convergence to global optimal solution, it can solve the genetic algorithm convergence rate and the conflict between the local optimal solution. The proposed genetic algorithm can not completely solve the genetic algorithm convergence rate and the contradiction between the local optimal solution, so how can we maximize the efficiency of genetic algorithm to improve and how best to solve this contradiction is that we Genetic algorithms for future research areas of interest.