Application of genetic algorithms to the structure optimization of radial basis probabilistic neural networks

The genetic algorithm (GA) is applied in this paper to select hidden centers of radial basis probabilistic neural networks (RBPNN). The encoding method of individuals for GA, proposed in this paper, embodies not only the number but also the positions of selected centers. In addition, precision control is integrated into definition of the fitness function. Finally, we use the two-dimensional Gaussian distribution classification problem to illustrate the performance of the GA.