  This article presents an evolutionary approach for synthetic human portraits
generation based on the latent space exploration of a generative adversarial
network. The idea is to produce different human face images very similar to a
given target portrait. The approach applies StyleGAN2 for portrait generation
and FaceNet for face similarity evaluation. The evolutionary search is based on
exploring the real-coded latent space of StyleGAN2. The main results over both
synthetic and real images indicate that the proposed approach generates
accurate and diverse solutions, which represent realistic human portraits. The
proposed research can contribute to improving the security of face recognition
systems.
