Quality-diversity for aesthetic evolution

TL;DR

This paper demonstrates that quality-diversity search methods can effectively explore creative generative systems, discovering diverse and high-quality aesthetic designs beyond manual search capabilities.

Contribution

The study introduces a novel application of quality-diversity algorithms to aesthetic evolution in generative art, combining neural network-based diversity measures with artist-in-the-loop evaluations.

Findings

Quality-diversity search finds multiple high-aesthetic-value phenotypes.

Discovered phenotypes exhibit greater diversity and quality than manual search.

Neural network effectively discriminates features for diversity measurement.

Abstract

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.