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.

生成对抗网络（GANs）在生成高质量的图像方面取得了巨大成功，并因此成为生成艺术图像的主要方法之一。本研究首先使用 Creative Adversarial Networks（CANs）架构的 GAN 进行训练，然后采用演化方法在模型的潜在空间中导航以发现图像。通过使用自动美学和交互式人类评估指标对生成的图像进行评估，并提出一种基于多个参与者的合作评估的人类交互式评估案例。此外，我们还尝试了一种智能变异算子，通过基于美学度量的局部搜索来提高图像的质量。通过比较自动和协作交互进化产生的结果来评估该方法的有效性。结果表明，当演化过程由协同人类反馈引导时，所提出的方法能够生成高度吸引人的艺术图像。