Systematic Literature Review of Meta-heuristic Algorithms and their Application in Procedural Content Generation (PCG) in the Context of Computer Games

This study investigated the use of metaheuristics and algorithms inspired by nat- ural processes to determine optimal solutions for procedural content generation (PCG) for the automatic creation of video game content. A systematic review of studies published between 2007 and 2023 was conducted to analyze the use of metaheuristics in PCG. This study explored various types of metaheuristics and common techniques employed. Genetic algorithms emerged as the dominant metaheuristic in PCG research. However, algorithm performance was influenced by parameters, problem complexity, and content objectives. Metaheuristics demon- strated potential for enhancing game design, player experience, and development efficiency. We discuss the advantages of metaheuristics in PCG, including en- hanced game design, improved player experience, and reduced development time and cost. However, challenges remain, such as developing better metrics for suc- cess and balancing the speed of solution finding with the solution quality. Finally, we propose future research directions, including the development of new algo- rithms, the integration of different approaches, and consideration of the ethical and social implications of PCG.