  Many creative generative design spaces contain multiple regions with
individuals of high aesthetic value. Yet traditional evolutionary computing
methods typically focus on optimisation, searching for the fittest individual
in a population. In this paper we apply quality-diversity search methods to
explore a creative generative system (an agent-based line drawing model). We
perform a random sampling of genotype space and use individual artist-assigned
evaluations of aesthetic quality to formulate a computable fitness measure
specific to the artist and this system. To compute diversity we use a
convolutional neural network to discriminate features that are dimensionally
reduced into two dimensions. We show that the quality-diversity search is able
to find multiple phenotypes of high aesthetic value. These phenotypes show
greater diversity and quality than those the artist was able to find using
manual search methods.
