使用QD搜索进行创造性发现
Creative Discovery using QD Search
AI总结：
本文提出一种结合进化优化与AI图像分类的质量-多样性搜索方法，用于创造性设计探索，在抽象绘画生成系统中验证了其能产生高美学价值和视觉多样性的成果。
摘要：
在创意设计中，美学在决定成果质量方面起着至关重要的作用，通常存在多个有价值的可能性，而非单一的“最佳”设计。在使用计算生成系统时，这一挑战更加复杂，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。该过程首先随机采样基因型空间，然后将生成的表型映射到解空间的简化表示，并根据其视觉特征进行评估。这产生了一个跨越解空间的多样化精英群体。随后，通过采样和简单突变逐步更新精英群体。我们在一个生成抽象绘画的生成系统上测试了我们的方法。结果表明，与传统的以优化为中心的进化方法相比，该系统能够有效地进化出具有高美学价值和更大视觉多样性的表型群体。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
