Learning of fuzzy decision regions using genetic algorithm

A method for nonparametric learning of complex fuzzy decision regions in n-dimensional feature space is proposed. An n-dimensional fuzzy decision region is approximated by a union of hyperellipsoids. By explicitly parameterizing these hyperellipsoids, the decision region can be determined by estimating the parameters of each hyperellipsoid. The genetic algorithm is applied to estimate the parameters of each region component. With the global optimization ability of GA, the decision region to be learned can be arbitrarily complex including linearly inseparable, nonconvex and disconnected ones.