# Comparing reliability of grid-based Quality-Diversity algorithms using artificial landscapes

arXiv: https://arxiv.org/abs/1908.08020 · HTML: https://mlsanity.com/paper/1908.08020/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2019.

## Built on by (most cited first, 1 of 1)

- [Quality-diversity in dissimilarity spaces](https://mlsanity.com/paper/2211.12337/) (2022)

## Abstract (arXiv)

Quality-Diversity (QD) algorithms are a recent type of optimisation methods that search for a collection of both diverse and high performing solutions. They can be used to effectively explore a target problem according to features defined by the user. However, the field of QD still does not possess extensive methodologies and reference benchmarks to compare these algorithms. We propose a simple benchmark to compare the reliability of QD algorithms by optimising the Rastrigin function, an artificial landscape function often used to test global optimisation methods.