The GMDH is a combinatorial multi-layer algorithm in which a network of layers and nodes is generated using a number of inputs from the data stream being evaluated. The GMDH was first proposed by Alexy G. Ivakhnenko (Ivakhnenko, 1971). The GMDH network topology has been traditionally determined using a layer by layer pruning process based on a pre-selected criterion of what constitutes the best nodes at each level. The goal is to obtain a mathematical model of the object under study. The GMDH creates adaptively models from data in form of networks of optimized transfer functions in a repetitive generation of layers of alternative models of growing complexity and corresponding model validation and fitness selection until an optimal complex model which is not too simple and not too complex has been created. Neither, the number of neurons and the number of layers in the network, nor the actual behavior of each created neuron are predefined. All these are adjusted during the process of selforganization by the process itself. As a result, an explicit analytical model representing relevant relationships between input and output variables is available immediately after modeling. This model contains the extracted knowledge applicable for interpretation, prediction, classification or diagnosis problems (Onwubolu, 2007).