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In this paper we’ve compared two GA Genetic Algorithm based approaches for computing the weight matrix of a RBFNN Radial Basis Function Neural Network. The first, named GA, was based in Michalewicz’s Operators for Continuous Genetic Algorithms and the other, named modGA, was based in extending these operators to matricial individuals, consequently proposing new operators. The main objective was verifying if the new approach could reduce the number of iterations (generations) necessary to compute the weight matrix. Six datasets was tested and in 50% of them, that hypothesis was confirmed. AMS Subject Classification: 92B20

MATRICIAL REPRESENTATION OF INDIVIDUALS IN CONTINUOUS GENETIC ALGORITHMS CAN IMPROVE THE EFFICIENCE IN RADIAL BASIS FUNCTIONS NEURAL NETWORKS TRAINING