Publications

The purpose of this article is to demonstrate the predictive power and features of a new genetic programming (GP) model -- the GenIQ Model(c) -- an alternative model to the statistical ordinary least squares and logistic regression models.

The GenIQ Model, which is based on the assumption-free, nonparametric GP paradigm inspired by Darwin's Principle of Survival of the Fittest, offers theoretical and usable advantages over the two statistical regression models, which are long-standing, and widely used. [1, 2, 3] GenIQ automatically (requiring no programming despite the suggestive term programming in GP) evolves a model by letting the data define it: The GenIQ Model is data-defined. [4, 5] As well, GenIQ has four usable features, which are unique in the way they automatically and simultaneously begin and carry through to completion: 1) Data mine,
2) Variable selection, and
3) Set forth the model equation itself - such that,
4) The decile table, a measure of model performance, is optimized.

[6, 7] The open-worked GenIQ Model and its wordbook are both generally regarded as not demanding on newcomers to GP modeling.

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