  Search-based procedural content generation (PCG) is a well-known method for
level generation in games. Its key advantage is that it is generic and able to
satisfy functional constraints. However, due to the heavy computational costs
to run these algorithms online, search-based PCG is rarely utilized for
real-time generation. In this paper, we introduce mutation models, a new type
of iterative level generator based on machine learning. We train a model to
imitate the evolutionary process and use the trained model to generate levels.
This trained model is able to modify noisy levels sequentially to create better
levels without the need for a fitness function during inference. We evaluate
our trained models on a 2D maze generation task. We compare several different
versions of the method: training the models either at the end of evolution
(normal evolution) or every 100 generations (assisted evolution) and using the
model as a mutation function during evolution. Using the assisted evolution
process, the final trained models are able to generate mazes with a success
rate of 99% and high diversity of 86%. The trained model is many times faster
than the evolutionary process it was trained on. This work opens the door to a
new way of learning level generators guided by an evolutionary process, meaning
automatic creation of generators with specifiable constraints and objectives
that are fast enough for runtime deployment in games.
