An Artificial Neural Network for a Tank Targeting System

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In this paper, we apply artificial neural networks to control the targeting system of a robotic tank in a tank-combat computer game (RoboCode). We suggest an algorithm that not only trains the connection weights of the neural network, but simultaneously searches for an optimum network architecture. Our hybrid evolutionary algorithm (PSONet) uses modified particle swarm optimisation to train the connection weights and four architecture mutation operators to evolve the appropriate architecture of the network, together with a new fitness function to guide the evolution.