The identification of genetic factors/features that predict complex diseases is an important goal of human genetics. The commonality of gene-gene interactions in the underlying genetic architecture of common diseases presents a daunting analytical challenge. Previously, we introduced a grammatical evolution neural network (GENN) approach that has high power to detect such interactions in the absence of any marginal main effects. While the success of this method is encouraging, it elicits questions regarding the evolutionary process of the algorithm itself and the feasibility of scaling the method to account for the immense dimensionality of datasets with enormous numbers of features. When the features of interest show no main effects, how is GENN able to build correct models? How and when should evolutionary parameters be adjusted according to the scale of a particular dataset? In the current study, we monitor the performance of GENN during its evolutionary process using different population sizes and numbers of generations. We also compare the evolutionary characteristics of GENN to that of a random search neural network strategy to better understand the benefits provided by the evolutionary learning process - including advantages with respect to chromosome size and the representation of functional versus non-functional features within the models generated by the two approaches. Finally, we apply lessons from the characterization of GENN to analyses of datasets containing increasing numbers of features to demonstrate the scalability of the method.