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There are several challenges when modeling artificial intelligence methods for autonomous players on games (bots). NEAT is one of the models that, combining genetic algorithms and neural networks, seek to describe a bot behavior more intelligently. In NEAT, a neural network is used for decision making, taking relevant inputs from the environment and giving real-time decisions. In a more abstract way, a genetic algorithm is applied for the learning step of the neural networks’ weights, layers, and parameters. This paper proposes the use of relative position as the input of the neural network, based on the hypothesis that the bot profit will be improved.

NEAT Snake: a both evolutionary and neural network adaptation approach