Abstract
Regression models are regarded as the most important ones used in statistical models in defining the relation among variables through the available data which can be applied to various sciences . In most of these applications , there is a dependent variable and independent variables and the linear relation between them represents multiple linear regression function . The values of the dependent variable can be predicted when the independent variables take definite values and the model's parameters are estimated through minimizing deviation of squares among the values of the real and estimated data. In this research we use the genetic algorithm to find the minimum sum of error squares, where they are applied to different data in many applications . The genetic algorithm is able to achieve the minimum sum of error squares and estimating the parameters of the model.
