  Quality-Diversity (QD) algorithms are designed to generate collections of
high-performing solutions while maximizing their diversity in a given
descriptor space. However, in the presence of unpredictable noise, the fitness
and descriptor of the same solution can differ significantly from one
evaluation to another, leading to uncertainty in the estimation of such values.
Given the elitist nature of QD algorithms, they commonly end up with many
degenerate solutions in such noisy settings. In this work, we introduce Archive
Reproducibility Improvement Algorithm (ARIA); a plug-and-play approach that
improves the reproducibility of the solutions present in an archive. We propose
it as a separate optimization module, relying on natural evolution strategies,
that can be executed on top of any QD algorithm. Our module mutates solutions
to (1) optimize their probability of belonging to their niche, and (2) maximize
their fitness. The performance of our method is evaluated on various tasks,
including a classical optimization problem and two high-dimensional control
tasks in simulated robotic environments. We show that our algorithm enhances
the quality and descriptor space coverage of any given archive by at least 50%.
