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This paper investigates the effectiveness of hybrids of learning and evolutionary approaches to find weights and topologies for an artificial neural network (ANN) which is used to evaluate board positions for a two-person zero-sum game, the virus game. Two hybrid approaches: evolutionary RPROP (resilient backpropagation) and evolutionary BP (backpropagation) are described and empirically compared...
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...
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