  This paper argues that generative art driven by conformance to a visual
and/or semantic corpus lacks the necessary criteria to be considered creative.
Among several issues identified in the literature, we focus on the fact that
generative adversarial networks (GANs) that create a single image, in a vacuum,
lack a concept of novelty regarding how their product differs from previously
created ones. We envision that an algorithm that combines the novelty
preservation mechanisms in evolutionary algorithms with the power of GANs can
deliberately guide its creative process towards output that is both good and
novel. In this paper, we use recent advances in image generation based on
semantic prompts using OpenAI's CLIP model, interrupting the GAN's iterative
process with short cycles of evolutionary divergent search. The results of
evolution are then used to continue the GAN's iterative process; we hypothesise
that this intervention will lead to more novel outputs. Testing our hypothesis
using novelty search with local competition, a quality-diversity evolutionary
algorithm that can increase visual diversity while maintaining quality in the
form of adherence to the semantic prompt, we explore how different notions of
visual diversity can affect both the process and the product of the algorithm.
Results show that even a simplistic measure of visual diversity can help
counter a drift towards similar images caused by the GAN. This first experiment
opens a new direction for introducing higher intentionality and a more nuanced
drive for GANs.

Artificial Intelligence;Graphics