Designing Radial Basis Function Networks for Classification Using Differential Evolution

The construction of a quality RBF network for a specific application can be a time-consuming process as the modeller must select both a suitable set of inputs and a suitable RBF network structure. Evolutionary methodologies offer the potential to automate all or part of these steps. This study illustrates how a hybrid RBFN-DE system can be constructed, and applies the system to a number of datasets. The utility of the resulting RBFNs on these classification problems is assessed and the results from the RFBN-DE hybrids are shown to be competitive against the best performance on these datasets using alternative classification methodologies.