The Generative Adversarial Random Neural Network

Abstract

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 generation of audio and video content. GANs, as a neural method that generates populations of individuals, have emulated genetic algorithms based on biologically inspired operators such as mutation, crossover and selection. This paper presents the Generative Adversarial Random Neural Network (RNN) with the same features and functionality as a GAN: an RNN Generator produces individuals mapped from a latent space while the RNN Discriminator evaluates them based on the true data distribution. The Generative Adversarial RNN has been evaluated against several input vectors with different dimensions. The presented results are successful: the learning objective of the RNN Generator creates replicas at low error whereas the RNN Discriminator learning target identifies unfit individuals.