  In creative design, where aesthetics play a crucial role in determining the
quality of outcomes, there are often multiple worthwhile possibilities, rather
than a single ``best'' design. This challenge is compounded in the use of
computational generative systems, where the sheer number of potential outcomes
can be overwhelming. This paper introduces a method that combines evolutionary
optimisation with AI-based image classification to perform quality-diversity
search, allowing for the creative exploration of complex design spaces. The
process begins by randomly sampling the genotype space, followed by mapping the
generated phenotypes to a reduced representation of the solution space, as well
as evaluating them based on their visual characteristics. This results in an
elite group of diverse outcomes that span the solution space. The elite is then
progressively updated via sampling and simple mutation. We tested our method on
a generative system that produces abstract drawings. The results demonstrate
that the system can effectively evolve populations of phenotypes with high
aesthetic value and greater visual diversity compared to traditional
optimisation-focused evolutionary approaches.
