# BOP-Elites, a Bayesian Optimisation Approach to Quality Diversity Search with Black-Box descriptor functions

arXiv: https://arxiv.org/abs/2307.09326 · HTML: https://mlsanity.com/paper/2307.09326/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2023.

## 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)
- [SpecAHD: Localize to Specialize for Automated Heuristic Design in Large-Scale Routing Problems](https://mlsanity.com/paper/2607.23676/) (2026)
- [Trust Region-Based Bayesian Optimisation to Discover Diverse Solutions](https://mlsanity.com/paper/2511.00750/) (2025)

## Abstract (arXiv)

Quality Diversity (QD) algorithms such as MAP-Elites are a class of optimisation techniques that attempt to find many high performing points that all behave differently according to a user-defined behavioural metric. In this paper we propose the Bayesian Optimisation of Elites (BOP-Elites) algorithm. Designed for problems with expensive black-box fitness and behaviour functions, it is able to return a QD solution-set with excellent final performance already after a relatively small number of samples. BOP-Elites models both fitness and behavioural descriptors with Gaussian Process (GP) surrogate models and uses Bayesian Optimisation (BO) strategies for choosing points to evaluate in order to solve the quality-diversity problem. In addition, BOP-Elites produces high quality surrogate models which can be used after convergence to predict solutions with any behaviour in a continuous range. An empirical comparison shows that BOP-Elites significantly outperforms other state-of-the-art algorithms without the need for problem-specific parameter tuning.