eprintid: 10196976
rev_number: 8
eprint_status: archive
userid: 699
dir: disk0/10/19/69/76
datestamp: 2024-09-16 09:56:35
lastmod: 2024-09-16 10:13:14
status_changed: 2024-09-16 09:56:35
type: proceedings_section
metadata_visibility: show
sword_depositor: 699
creators_name: Lim, Soo Ling
creators_name: Bentley, Peter J
creators_name: Ishikawa, Fuyuki
title: SCAPE: Searching Conceptual Architecture Prompts using Evolution
ispublished: pub
divisions: UCL
divisions: B04
divisions: F48
keywords: Machine learning algorithms, Generative AI, Image colour analysis, Computational modelling, Buildings, Evolutionary computation, Computer architecture
note: This version is the author-accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: 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.
date: 2024-08-08
date_type: published
publisher: IEEE
official_url: https://doi.org/10.1109/CEC60901.2024.10612168
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2309493
doi: 10.1109/CEC60901.2024.10612168
isbn_13: 979-8-3503-0836-5
lyricists_name: Bentley, Peter
lyricists_id: PJBEN84
actors_name: Bentley, Peter
actors_id: PJBEN84
actors_role: owner
full_text_status: public
pres_type: paper
series: IEEE Congress on Evolutionary Computation (CEC)
publication: 2024 IEEE Congress on Evolutionary Computation, CEC 2024 - Proceedings
volume: 2024
place_of_pub: Yokohama, Japan
pagerange: 01-08
event_title: 2024 IEEE Congress on Evolutionary Computation (CEC)
event_dates: 30 Jun 2024 - 5 Jul 2024
book_title: 2024 IEEE Congress on Evolutionary Computation, CEC 2024 - Proceedings
citation:        Lim, Soo Ling;    Bentley, Peter J;    Ishikawa, Fuyuki;      (2024)    SCAPE: Searching Conceptual Architecture Prompts using Evolution.                     In:  2024 IEEE Congress on Evolutionary Computation, CEC 2024 - Proceedings.  (pp. 01-08).  IEEE: Yokohama, Japan.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10196976/1/2024086077.pdf