Multi-Objective level generator generation with Marahel
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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 a good performance on most of the fitness functions over these three problems but on Zelda and Sokoban they tend to depend on the initial state than modifying the map.
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