  Procedural Content Generation (PCG) methods are valuable tools to speed up
the game development process. Moreover, PCG may also present in games as
features, such as the procedural dungeon generation (PDG) in Moonlighter
(Digital Sun, 2018). This paper introduces an extended version of an
evolutionary dungeon generator by incorporating a MAP-Elites population. Our
dungeon levels are discretized with rooms that may have locked-door missions
and enemies within them. We encoded the dungeons through a tree structure to
ensure the feasibility of missions. We performed computational and user
feedback experiments to evaluate our PDG approach. They show that our approach
accurately converges almost the whole MAP-Elite population for most executions.
Finally, players' feedback indicates that they enjoyed the generated levels,
and they could not indicate an algorithm as a level generator.
