In the thesis, we implement neuroevolution for the creation of recurrent neural networks for multiclass classification. Traditional approaches to machine learning rely on human-designed neural network topologies, while we also search for the suitable topology for the given dataset, considering the complexity of the networks. The accuracy of the created networks for classification of datasets with uniformly distributed classes is comparable to the random forests approach, but our solution is inferior in classification of datasets with non-uniform class distributions.
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