: 0cm 0cm 0pt" In this thesis , we focus on this issue by considering a type of nonlinear transformations of covariates named radial basis functions , in logistic regression model . But the main problem of this model is finding maximum likelihood estimation of its parameters. Existence of nonlinear transformations of covariates in logistic model causes many local extremums in likelihood function . Therefore , finding of global maximum for this function using usual maximum likelihood methods is almost impossible. The proposed method in this thesis for overcoming to this difficulty is a hybrid method which solves the problem of maximum likelihood estimation for these logistic regression models by the use of combining the idea of neural networks , evolutionary algorithms and maximum likelihood methods. This method includes three steps : In first step nonlinear part of model is shown by using a radial basis function neural network , then parameters in this neural network and its structure is determined optimality by using an evolutionary programing algorithm . In second step , best radial basis functions determined in pervious step as new covariates is added to space of the initial covariates . The resulted model in this new space is linear with respect to all covariates . Therefore in final step , the remaining coefficients in model are estimate by using two distinct algorithms . In first algorithm ridge estimators of the coefficients are calculated and therefore is avoided the overfitting problem in model . The second algorithm incrementally constructs the model by automatic covariate selection . Each of these algorithms lead to different models . After estimating the parameters by hybrid method , we compare the resulting logistic model to other 0cm 0cm 0pt" Experimental results show that the proposed method is superior to other conventional methods . 2010 MSC: 62J12 Keywords: logistic regression, radial basis function neural networks, evolutionary algorithms