Genetic and Evolutionary Computation Conference 2024

Generative Design through Quality-Diversity Data Synthesis and Language Models

TileGPT. (1) A dataset of paired designs and attributes is generated with the MAP-Elites algorithm, which is used to (2) fine-tune a GPT model to produce designs with given attributes. (3) Given a natural language description a simplified design with the described attributes is generated by the GPT model, and (4) given to a constraint satisfaction algorithm, which refines it into a detailed site plan.


Two fundamental challenges face generative models in engineering applications: the acquisition of high-performing, diverse datasets, and the adherence to precise constraints in generated designs.

We propose a novel approach combining optimization, constraint satisfaction, and language models to tackle these challenges in architectural design. Our method uses Quality-Diversity (QD) to generate a diverse, high-performing dataset. We then fine-tune a language model with this dataset to generate high-level designs. These designs are then refined into detailed, constraint-compliant layouts using the Wave Function Collapse algorithm.

Our system demonstrates reliable adherence to textual guidance, enabling the generation of layouts with targeted architectural and performance features. Crucially, our results indicate that data synthesized through the evolutionary search of QD not only improves overall model performance but is essential for the model’s ability to closely adhere to textual guidance. This improvement underscores the pivotal role evolutionary computation can play in creating the datasets key to training generative models for design. Web article at

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