  Generative Adversarial Networks (GANs) have shown great success in generating
high quality images and are thus used as one of the main approaches to generate
art images. However, usually the image generation process involves sampling
from the latent space of the learned art representations, allowing little
control over the output. In this work, we first employ GANs that are trained to
produce creative images using an architecture known as Creative Adversarial
Networks (CANs), then, we employ an evolutionary approach to navigate within
the latent space of the models to discover images. We use automatic aesthetic
and collaborative interactive human evaluation metrics to assess the generated
images. In the human interactive evaluation case, we propose a collaborative
evaluation based on the assessments of several participants. Furthermore, we
also experiment with an intelligent mutation operator that aims to improve the
quality of the images through local search based on an aesthetic measure. We
evaluate the effectiveness of this approach by comparing the results produced
by the automatic and collaborative interactive evolution. The results show that
the proposed approach can generate highly attractive art images when the
evolution is guided by collaborative human feedback.
