Creative Discovery using QD Search

TL;DR

This paper presents a novel method combining evolutionary algorithms and AI-based image classification to enable creative exploration of design spaces, producing diverse and aesthetically valuable outcomes.

Contribution

It introduces a quality-diversity search approach that effectively balances aesthetic quality and diversity in generative design systems.

Findings

Achieves higher aesthetic quality than traditional methods

Generates more diverse visual outcomes

Demonstrates effectiveness on abstract drawing generation

Abstract

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.