遗传算法中参数与算法性能关系的分析..docVIP

摘要 遗传算法(Genetic Algorithm ，GA ) 是由美国J. Holland 教授提出的一类借鉴生物界自然选择和自然遗传机制的随机化有哪些信誉好的足球投注网站算法.它起源于达尔文的进化论， ， ， Abstract Genetic Algorithm (Genetic Algorithm, GA) is proposed by Professor J. Holland, a class of random search algorithm learn from biological natural selection and natural genetic mechanisms. It originated in Darwins theory of evolution, the computational model of genetic selection and natural selection simulation of Darwins biological evolution process.Its main characteristic is the exchange of information between the individual groups search strategies and groups,the search is not based on gradient information. It is particularly applicable to the handling of difficult to resolve complex and nonlinear problems of traditional search methods can be widely used in combinatorial optimization, machine learning, adaptive control, planning, design and artificial life. In this paper,the use of genetic algorithm optimization toolbox of different functions were tested to study the influence of different parameters on the performance of the GA algorithm. First introduced the research background of the genetic algorithm, the history of the development and direction of development. Second, the brief introduction to the main content of the genetic algorithm and the steps and processes. Third, respectively, the use of Genetic Algorithm and one-dimensional unconstrained nonlinear function, two-dimensional constrained nonlinear function, as well as two-dimensional constrained nonlinear function test, and validate the superiority of the GA algorithm. Finally, the analysis of different test parameters on the performance of GA algorithm. By test and analysis results are as follows: the convergence of certain other parameters, (1) With the increasing population size, the GA algorithm solving problem of continuously enhance, at the same time to obtain the optimal value of speed to accelerate. (2) With the increase of breeding algebra, the GA algorithm to solve the convergence of