Applying Genetic Algorithms to Recurrent Neural Networks for Learning Network Parameters and Architecture

M.S. Thesis by Omar Syed (of
Advisor: Prof. Yoshiyasu Takefuji

Recurrent neural networks such as the fully interconnected Hopfield network have the potential of being applied to many input output mapping problems, especially those requiring the outputs to change with time, such as a centeral pattern generator. However, due to the difficulty of training, such networks have not been as extensively utilized as the multilayer feedforward networks which can be trained using the back propagation algorithm. In this thesis we explore the applicability of the genetic algorithm as a search technique to find the network parameters for recurrent neural networks. We also investigate the potential of the genetic algorithm to determine not only the network parameters, but also the number of neurons to use in the network architecture.

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