An Evolutionary Approach for PCG in a Cooperative Puzzle Platform Game

Procedural content generation (PCG) is a popular topic in game research and practice, however, the generation of content for cooperative games, specifically content that requires collaboration between both players to be completed, is still underdeveloped. In this work, we contribute to the body of knowledge of PCG for cooperative games, describing our approach for generating levels, for the cooperative game Geometry Friends, based on genetic algorithms and the definition of cooperative constraints. We present the evaluation conducted to test the quality of a sample of levels generated and the appropriateness of the constraints that define areas of reach for each player in the game. The evaluation showed that the constraints given to the generation algorithm are able to express different levels of cooperation in the levels generated, according to the subjective assessment of players.