  Procedurally generated video game content has the potential to drastically
reduce the content creation budget of game developers and large studios.
However, adoption is hindered by limitations such as slow generation, as well
as low quality and diversity of content. We introduce an evolutionary
search-based approach for evolving level generators using novelty search to
procedurally generate diverse levels in real time, without requiring training
data or detailed domain-specific knowledge. We test our method on two domains,
and our results show an order of magnitude speedup in generation time compared
to existing methods while obtaining comparable metric scores. We further
demonstrate the ability to generalise to arbitrary-sized levels without
retraining.
