Abstract
Gelenbe (1989) has modeled the neural network using an analogy with queuing theory. This model (called random neural network) calculates the probability of activation of the neurons in the network. Recently, we have proposed a recognition algorithm based on the random neural network. In this paper, we propose to solve the patterns recognition problem using an evolutionary learning on the random neural network. The evolutionary learning is based in a hybrid algorithm that trains the random neural network by integrating a genetic algorithm with the gradient descent rule-based learning algorithm of the random neural network. This hybrid learning algorithm optimizes the random neural network on the basis of its topology and its weights distribution.
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