An Artificial Neural Network for a Tank Targeting System.
Hans W. Guesgen, X. Shi · 2006
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 si-multaneously 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 appro-priate architecture of the network, together with a new fitness function to guide the evolution. Introduction and Background Artificial Neural Networks (ANNs) have been used in a variety of areas during the last thirty years (Meyer 1998; Russell & Norvig 2003; Scapura 1995), more recently in