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

Procedural Content Generation using Neuroevolution and Novelty Search for Diverse Video Game Levels