Evolving recurrent neural networks with non-binary encoding

This paper presents an evolutionary approach for the design of feedforward and recurrent neural networks. We show that evolutionary algorithms can be used for the construction of networks for real-world tasks. Therefore, a data structure based genotypic network representation, as well as genetic operators, are introduced. Results from the classification, function approximation and time-series domains are presented.