A Coevolutionary Model for The Virus Game

In this paper, coevolution is used to evolve artificial neural networks (ANN) which evaluate board positions of a two player zero-sum game (the virus game). The coevolved neural networks play at a level that beats a group of strong hand-crafted AI players. We investigate the performance of coevolution starting from random initial weights and starting with weights that are tuned by gradient based adaptive learning methods (backpropagation, RPROP and iRPROP). The results of coevolutionary experiments show that pre training of the population is highly effective in this case

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