  We describe a search-based approach to generating new levels for bullet hell
games, which are action games characterized by and requiring avoidance of a
very large amount of projectiles. Levels are represented using a
domain-specific description language, and search in the space defined by this
language is performed by a novel variant of the Map-Elites algorithm which
incorporates a feasible- infeasible approach to constraint satisfaction.
Simulation-based evaluation is used to gauge the fitness of levels, using an
agent based on best-first search. The performance of the agent can be tuned
according to the two dimensions of strategy and dexterity, making it possible
to search for level configurations that require a specific combination of both.
As far as we know, this paper describes the first generator for this game
genre, and includes several algorithmic innovations.
