  This paper introduces a new system to design constructive level generators by
searching the space of constructive level generators defined by Marahel
language. We use NSGA-II, a multi-objective optimization algorithm, to search
for generators for three different problems (Binary, Zelda, and Sokoban). We
restrict the representation to a subset of Marahel language to push the
evolution to find more efficient generators. The results show that the
generated generators were able to achieve good performance on most of the
fitness functions over these three problems. However, on Zelda and Sokoban,
they tend to depend on the initial state than modifying the map.
