# SCAPE: Searching Conceptual Architecture Prompts using Evolution

arXiv: https://arxiv.org/abs/2402.00089 · HTML: https://mlsanity.com/paper/2402.00089/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 31 Jan 2024.

## Builds on (most cited first, 13 of 13)

- [Large Language Models as Optimizers](https://mlsanity.com/paper/2309.03409/) (2023)
- [EvoPrompting: Language Models for Code-Level Neural Architecture Search](https://mlsanity.com/paper/2302.14838/) (2023)
- [Language Model Crossover: Variation through Few-Shot Prompting](https://mlsanity.com/paper/2302.12170/) (2023)
- [Review of Large Vision Models and Visual Prompt Engineering](https://mlsanity.com/paper/2307.00855/) (2023)
- [MarioGPT: Open-Ended Text2Level Generation through Large Language Models](https://mlsanity.com/paper/2302.05981/) (2023)
- [Investigating Prompt Engineering in Diffusion Models](https://mlsanity.com/paper/2211.15462/) (2022)
- [A Taxonomy of Prompt Modifiers for Text-To-Image Generation](https://mlsanity.com/paper/2204.13988/) (2022)
- [3DALL-E: Integrating Text-to-Image AI in 3D Design Workflows](https://mlsanity.com/paper/2210.11603/) (2022)
- [Best Prompts for Text-to-Image Models and How to Find Them](https://mlsanity.com/paper/2209.11711/) (2022)
- [Using Text-to-Image Generation for Architectural Design Ideation](https://mlsanity.com/paper/2304.10182/) (2023)
- [AI Art in Architecture](https://mlsanity.com/paper/2212.09399/) (2022)
- [COIL: Constrained Optimization in Learned Latent Space: Learning Representations for Valid Solutions](https://mlsanity.com/paper/2202.02163/) (2022)
- [Using a Variational Autoencoder to Learn Valid Search Spaces of Safely Monitored Autonomous Robots for Last-Mile Delivery](https://mlsanity.com/paper/2303.03211/) (2023)

## Built on by (most cited first, 4 of 4)

- [Automated Real-World Sustainability Data Generation from Images of Buildings](https://mlsanity.com/paper/2405.18064/) (2024)
- [CLEAR: Cue Learning using Evolution for Accurate Recognition Applied to Sustainability Data Extraction](https://mlsanity.com/paper/2501.18504/) (2025)
- [Situating AI Agents in their World: Aspective Agentic AI for Dynamic Partially Observable Information Systems](https://mlsanity.com/paper/2509.03380/) (2025)
- [An evolutionary model of animats with VLM-based subjective evaluation](https://mlsanity.com/paper/2608.07537/) (2026)

## Abstract (arXiv)

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.