Genetic evolution of radial basis function coverage using orthogonal niches

A well-performing set of radial basis functions (RBFs) can emerge from genetic competition among individual RBFs. Genetic selection of the individual RBFs is based on credit sharing which localizes competition within orthogonal niches. These orthogonal niches are derived using singular value decomposition and are used to apportion credit for the overall performance of the RBF network among individual nonorthogonal RBFs. Niche-based credit apportionment facilitates competition to fill each niche and hence to cover the training data. The resulting genetic algorithm yields RBF networks with better prediction performance on the Mackey-Glass chaotic time series than RBF networks produced by the orthogonal least squares method and by k-means clustering