  Action-Adventure games have several challenges to overcome, where the most
common are enemies. The enemies' goal is to hinder the players' progression by
taking life points, and the way they hinder this progress is distinct for
different kinds of enemies. In this context, this paper introduces an extended
version of an evolutionary approach for procedurally generating enemies that
target the enemy's difficulty as the goal. Our approach advances the enemy
generation research by incorporating a MAP-Elites population to generate
diverse enemies without losing quality. The computational experiment showed the
method converged most enemies in the MAP-Elites in less than a second for most
cases. Besides, we experimented with players who played an Action-Adventure
game prototype with enemies we generated. This experiment showed that the
players enjoyed most levels they played, and we successfully created enemies
perceived as easy, medium, or hard to face.
