Procedural Generation of Levels with Controllable Difficulty for a Platform Game Using a Genetic Algorithm

Abstract: This thesis describes the implementation and evaluation of a genetic algorithm (GA) for procedurally generating levels with controllable difficulty for a motion-based 2D platform game. Manually creating content can be time-consuming, and it may be desirable to automate this process with an algorithm, using Procedural Content Generation (PCG). An algorithm was implemented and then refined with an iterative method by conducting user tests. The resulting algorithm is considered a success and shows that using GA's for this kind of PCG is viable. An algorithm able to control difficulty of its output was achieved, but more refinement could be made with further user tests. Using a GA for this purpose, one should find elements that affect difficulty, incorporate these in the fitness function, and test generated content to ensure that the fitness function correctly evaluates solutions with regard to the desired output.