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 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 an alterantive to the logistic regression model, namely, the GenIQ Model. GenIQ is as an assumption-free, nonparametric methodology based the machine learning genetic programming paradigm. The genetic logistic regression alternative offers a clear advantage over the statistical logistic regression method, whose performance is dependent on theoretical assumptions, a pre-specified parametric model, and data restrictions. Pointedly, the GenIQ Model automatically determines the best set of predictor variables (from the original variables, and newly constructed – genetically data mined – variables) based on a virtually unbiased assessment of all variables under consideration, an achievement not possible with statistical methods.