  Automated machine learning (AutoML) systems commonly ensemble models post hoc
to improve predictive performance, typically via greedy ensemble selection
(GES). However, we believe that GES may not always be optimal, as it performs a
simple deterministic greedy search. In this work, we introduce two novel
population-based ensemble selection methods, QO-ES and QDO-ES, and compare them
to GES. While QO-ES optimises solely for predictive performance, QDO-ES also
considers the diversity of ensembles within the population, maintaining a
diverse set of well-performing ensembles during optimisation based on ideas of
quality diversity optimisation. The methods are evaluated using 71
classification datasets from the AutoML benchmark, demonstrating that QO-ES and
QDO-ES often outrank GES, albeit only statistically significant on validation
data. Our results further suggest that diversity can be beneficial for post hoc
ensembling but also increases the risk of overfitting.
