Using Coevolution and Gradient-based learning for the Virus Game

This paper presents a novel coevolutionary model which is used to create strong game (The Virus Game) playing strategies. We use two approaches to coevolve Artificial Neural Networks (ANN) which evaluate board positions of a two player zero-sum game (The Virus Game). The first approach uses the coevolution with initial population of random ANN and second approach is a novel coevolutionary model with initial population of ANN which are trained using gradient based adaptive learning methods (Backpropagation, RPROP and iRPROP). In our case, the results of coevolutionary experiments show that pre training of the population in coevolution is highly effective in creating stronger game playing strategies than coevolution with random population.