|Generative Adversarial Networks (GANs) have been proposed as a method to generate multiple replicas from an original version combining a Discriminator and a Generator. The main applications of GANs have been the casual genera-tion of audio and video content. GANs, as a neural method that generates popu-lations of individuals, have emulated genetic algorithms based on biologically inspired operators such as mutation, crossover and selection. This paper pre-sents the Generative Adversarial Random Neural Network (RNN) with the same features and functionality as a GAN: an RNN Generator produces indi-viduals mapped from a latent space while the GAN Discriminator evaluates them based on the true data distribution. The Generative Adversarial RNN has been evaluated against several input vectors with different dimensions. The pre-sented results are successful: the learning objective of the RNN Generator cre-ates replicas at low error whereas the RNN Discriminator learning target identi-fies unfit individuals.|
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