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.

翻译：在创意设计中，美学在决定成果质量方面起着关键作用，往往存在多个有价值的可能性，而非单一的“最佳”设计。在计算生成系统的应用中，这一挑战更为复杂，因为潜在结果的数量可能令人难以招架。本文介绍了一种将进化优化与基于AI的图像分类相结合的方法，以执行质量-多样性搜索，从而实现对复杂设计空间的创造性探索。该过程从随机采样基因型空间开始，随后将生成的表型映射到解决方案空间的简化表示，并根据其视觉特征进行评估。这产生了一组跨越解决方案空间的多样且优质的精英成果。接着，通过采样和简单突变逐步更新精英集。我们将该方法应用于一个生成抽象绘画的系统。测试结果表明，与传统以优化为核心的进化方法相比，该系统能够有效进化出具有高美学价值且视觉多样性更高的表型群体。