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The efficiency of the systems for the automated formation of artificial neural networks is beyond any disputes. Such systems make it possible to automatically generate ready-to-work models without participation of a domain expert while creating a model. The present paper considers a modification of the evolutionary algorithm for the automated formation of artificial neural networks of direct propagation, which helps build recurrent neural networks. The peculiarity of the algorithm lies in the network structure encoding in the form of a tree allowing the network structure to be represented more compactly, that well correlates with text analysis tasks, the dimensions of which vary from hundreds to thousands of features. To test the approach, the problems of classifying textual information were solved. The averaged test results as well as some of the obtained models are presented to demonstrate the approach. It is showed that the algorithm successfully builds models with a recurrent structure. This approach can be potentially improved by adding various recurrent links. [ABSTRACT FROM AUTHOR]