  This paper describes an approach that combines generative adversarial
networks (GANs) with interactive evolutionary computation (IEC). While GANs can
be trained to produce lifelike images, they are normally sampled randomly from
the learned distribution, providing limited control over the resulting output.
On the other hand, interactive evolution has shown promise in creating various
artifacts such as images, music and 3D objects, but traditionally relies on a
hand-designed evolvable representation of the target domain. The main insight
in this paper is that a GAN trained on a specific target domain can act as a
compact and robust genotype-to-phenotype mapping (i.e. most produced phenotypes
do resemble valid domain artifacts). Once such a GAN is trained, the latent
vector given as input to the GAN's generator network can be put under
evolutionary control, allowing controllable and high-quality image generation.
In this paper, we demonstrate the advantage of this novel approach through a
user study in which participants were able to evolve images that strongly
resemble specific target images.
