Abstract
In this paper, we propose a architecture of Genetic Algorithms (GAs)-based Polynomial Neural Networks(PNN), discuss a comprehensive design methodology and carry out a series of numeric experiments. GA-based design procedure at each stage (layer) of PNN leads to the selection of preferred nodes (or PNs) with optimal parameters (such as the number of input variables, input variables, and the order of the polynomial) available within PNN. A detailed design procedure is discussed in detail. To evaluate the performance of the GA-based PNN, the model is experimented with by using Medical Imaging System (MIS) data for application to Multi-variable software process. A comparative analysis shows that the proposed GA-based PNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.