Logistic regression is a popular technique for classifying individuals into two mutually exclusive and exhaustive categories, for example: buy-not buy or responder-non-responder. It is the workhorse of response modeling as its results are considered the gold standard. Moreover, it is used as the benchmark for assessing the superiority of newer techniques, such as a Genetic Logistic Regression Model, also known as the GenIQ Model. In database marketing, response to a prior solicitation is the binary class variable (defined by responder and non-responder), and the logistic regression model is built to classify an individual as either most likely or least likely to respond to a future solicitation. The purpose of this article is to present the Genetic Logistic Regression Model as an assumption-free, nonparametric methodology, i.e., model-free, based on Darwin's Principle of Survival of the Fittest, and natural genetic operations - namely, genetic programming. The genetic-based logistic regression offers a clear advantage over the statistical logistic regression method, whose performance is dependent on theoretical assumptions and data restrictions. The Genetic Logistic Regression determines the best set of predictors based on a simultaneous and virtually unbiased assessment of all variables, an achievement not possible with current statistical logistic regression.