Artificial intelligence plays an increasingly important role in modern computer games. As the complexity of the games increase, so does the complexity of the AI.
The aim of this dissertation is to investigate how AI for a turnbased computer game can coevolve into playing smarter by combining genetic algorithms with neural networks and using a reinforcement learning regime.
The results have shown that a coevolved AI can reach a high performance in this kind of turnbased strategy games. It also shows that how the data is coded and decoded and which strategy that is used plays a very big role in the final results