The evolution strategy is an algorithm which simulates the biological evolution mechanism, the people design a group intelligence search algorithm, it has intelligent characteristics of self-organizing, self-adapting, self-learning and so on, but the evolution strategy widely applied in many different scientific domains is less concerned in the regression model parameter estimation. The regression model parameter estimation is generally a mathematical model applied in many academics, such as systematic engineering, automation, mechanical engineering, electric power project and so on, To this day ,it is an important problem for research all the time. Recently, regression parameter estimation has already had some methods, such as least squares method, maximum likelihood method. But these methods are all based on the supposition that smooth searching space has continuous derivatives, and partial searching technology which seeks the optimization in the gradient dropping direction can easily lead to partial extreme value. Based on these, the evolution strategy algorithm is applied in carrying on the parameter estimation, which avoids some insufficiencies in the traditional methods.In view of some problems in traditional parameter estimation method, this article mainly makes use of some characteristics of evolution strategy, especially such as self-adapting searching, global convergence, robustness, moreover makes some improvements. An evolution strategy algorithm which has the advantages of good universal property,high searching efficiency and fast convergence rate is given in this paper. This algorithm are mainly used to estimate the parameters in the distributive density function, linear and non-linear regression analysis, the parameter estimation values compare those obtained by the traditional methods which has the solution precision high, the convergence rate quick and so on the merits .Therefore, this method in the mathematical statistic, aspects systems engineering and so on has the important theory value and the practical application background. |