TY  - GEN
PB  - IEEE
SP  - 01
N1  - This version is the author-accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
ID  - discovery10196976
Y1  - 2024/08/08/
TI  - SCAPE: Searching Conceptual Architecture Prompts using Evolution
T3  - IEEE Congress on Evolutionary Computation (CEC)
UR  - https://doi.org/10.1109/CEC60901.2024.10612168
A1  - Lim, Soo Ling
A1  - Bentley, Peter J
A1  - Ishikawa, Fuyuki
KW  - Machine learning algorithms
KW  -  Generative AI
KW  -  Image colour analysis
KW  -  Computational modelling
KW  -  Buildings
KW  -  Evolutionary computation
KW  -  Computer architecture
N2  - 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 three 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.
AV  - public
EP  - 08
CY  - Yokohama, Japan
ER  -