  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.
