Improving multiclass pattern recognition with a co-evolutionary RBFNN

A new hybrid scheme of the radial basis function neural network (RBFNN) model and the co-operative co-evolutionary algorithm (Co-CEA) is presented for multiclass classification tasks. This combination of the conventional RBFNN training algorithm and the proposed Co-CEA enforces the strength of both methods. First, the decaying radius selection clustering (DRSC) method is used to obtain the initial hidden nodes of the RBFNN model, which are further partitioned into modules of hidden nodes by the K-means method. Then, subpopulations are initialized on modules, and the Co-CEA evolves all subpopulations to find the optimal RBFNN structural parameters. Matrix-form mixed encoding and special crossover and mutation operators are designed. Finally, the proposed algorithm is tested on 14 real-world classification problems from the UCI machine learning repository, and experimental results illustrate that the algorithm is able to produce RBFNN models that have better prediction accuracies and simpler structures than conventional algorithms of classification.