Advancing Game AI: A Study on Genetic Algorithms and Neuroevolution

This study examines two AI-driven methods namely, Genetic Algorithms and Neuroevolution of Augmenting Topologies (NEAT) for training game-playing agents to outperform human players. Genetic algorithms mimic natural selection and gradually refine strategies through iterative evolution. NEAT extends this approach by dynamically modifying neural networks, and optimizing connectivity and performance. Beyond being an interesting showcase, these techniques have promising applications in game development, particularly in automating game testing and bug testing. We concluded that AI agents can play games well and that this technique has the potential to significantly lessen the timelines for the development of games and improve the game's quality at launch.