Conceptual architecture involves a highly creative exploration of novel ideas, often taken from other disciplines as architects consider radical new forms, materials, textures and colors for buildings. While today's generative AI systems can produce remarkable results, they lack the creativity demonstrated for decades by evolutionary algorithms. SCAPE, our proposed tool, combines evolutionary search with generative AI, enabling users to explore creative and good quality designs inspired by their initial input through a simple point and click interface. SCAPE injects randomness into generative AI, and enables memory, making use of the built-in language skills of GPT-4 to vary prompts via text-based mutation and crossover. We demonstrate that compared to DALL-E 3, SCAPE enables a 67% improvement in image novelty, plus improvements in quality and effectiveness of use; we show that in just 3 iterations SCAPE has a 24% image novelty increase enabling effective exploration, plus optimization of images by users. We use more than 20 independent architects to assess SCAPE, who provide markedly positive feedback.

翻译：概念建筑涉及对新颖思想的高度创造性探索，建筑师常常从其他学科汲取灵感，为建筑构思激进的新形式、材料、纹理和色彩。虽然当前的生成式AI系统能够产生显著成果，但它们缺乏进化算法数十年来展现的创造力。我们提出的工具SCAPE将进化搜索与生成式AI相结合，使用户能够通过简单的点击界面，基于初始输入探索兼具创意与品质的设计方案。SCAPE向生成式AI注入随机性并赋予其记忆能力，通过利用GPT-4内置的语言技能，基于文本的变异和交叉操作来变异提示词。实验表明，与DALL-E 3相比，SCAPE在图像新颖性上提升67%，同时改善了图像质量和用户使用效率；仅需3次迭代，SCAPE的图像新颖性便提升24%，实现了高效探索和用户对图像的优化。我们邀请20余位独立建筑师对SCAPE进行评估，获得了显著的积极评价。