  In the context of generative models, text-to-image generation achieved
impressive results in recent years. Models using different approaches were
proposed and trained in huge datasets of pairs of texts and images. However,
some methods rely on pre-trained models such as Generative Adversarial
Networks, searching through the latent space of the generative model by using a
gradient-based approach to update the latent vector, relying on loss functions
such as the cosine similarity. In this work, we follow a different direction by
proposing the use of Covariance Matrix Adaptation Evolution Strategy to explore
the latent space of Generative Adversarial Networks. We compare this approach
to the one using Adam and a hybrid strategy. We design an experimental study to
compare the three approaches using different text inputs for image generation
by adapting an evaluation method based on the projection of the resulting
samples into a two-dimensional grid to inspect the diversity of the
distributions. The results evidence that the evolutionary method achieves more
diversity in the generation of samples, exploring different regions of the
resulting grids. Besides, we show that the hybrid method combines the explored
areas of the gradient-based and evolutionary approaches, leveraging the quality
of the results.
