# Prototype Discovery using Quality-Diversity

arXiv: https://arxiv.org/abs/1807.09488 · HTML: https://mlsanity.com/paper/1807.09488/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2018.

## Built on by (most cited first, 3 of 3)

- [Generative Design through Quality-Diversity Data Synthesis and Language Models](https://mlsanity.com/paper/2405.09997/) (2024)
- [Rigorous Runtime Analysis of Diversity Optimization with GSEMO on OneMinMax](https://mlsanity.com/paper/2307.07248/) (2023)
- [Full Domain Analysis in Fluid Dynamics](https://mlsanity.com/paper/2505.22275/) (2025)

## Abstract (arXiv)

An iterative computer-aided ideation procedure is introduced, building on recent quality-diversity algorithms, which search for diverse as well as high-performing solutions. Dimensionality reduction is used to define a similarity space, in which solutions are clustered into classes. These classes are represented by prototypes, which are presented to the user for selection. In the next iteration, quality-diversity focuses on searching within the selected class. A quantitative analysis is performed on a 2D airfoil, and a more complex 3D side view mirror domain shows how computer-aided ideation can help to enhance engineers' intuition while allowing their design decisions to influence the design process.