Abstract
In this paper, we proposed a different genetic model for optimizing both network architecture and connection weights of Discrete-Time Recurrent Neural Networks in evolutionary process. Empirical studies show that our model can efficiently generate the appropiate network size and topology for small applications. We have used two experiments: a parity function and a finite state machine for detection of sequences.
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Marín, F.J., Sandoval, F. (1993). Genetic synthesis of discrete-time recurrent neural network. In: Mira, J., Cabestany, J., Prieto, A. (eds) New Trends in Neural Computation. IWANN 1993. Lecture Notes in Computer Science, vol 686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56798-4_144
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DOI: https://doi.org/10.1007/3-540-56798-4_144
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