  This work is a preliminary study on using Exploratory Landscape Analysis
(ELA) for Quality Diversity (QD) problems. We seek to understand whether ELA
features can potentially be used to characterise QD problems paving the way for
automating QD algorithm selection. Our results demonstrate that ELA features
are affected by QD optimisation differently than random sampling, and more
specifically, by the choice of variation operator, behaviour function, archive
size and problem dimensionality.
