Quality-diversity for aesthetic evolution
This addresses the challenge for artists and designers in efficiently discovering varied aesthetic outputs in generative systems, though it is incremental as it adapts existing quality-diversity methods to a specific domain.
The paper tackled the problem of exploring diverse high-quality designs in creative generative systems by applying quality-diversity search methods to an agent-based line drawing model, resulting in phenotypes with greater diversity and quality than manual artist searches.
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.