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TOWARDS EVOLVING HETEROGENEOUS GROUP CONTROL MICRO IN RTS GAMES
We use genetic algorithms to evolve control tactics for groups made up of two types of units in real-time strategy games. The space of possible tactics consists of two sets of parameters. The first set of parameters controls target selection, kiting and fleeing in a control algorithm while the second set of parameters specifies potential fields directing unit movement and an influence map specifying attacklocation. Results indicate that the genetic algorithm was able to evolve control tactics that performed better against a baseline control algorithm and against random values of these parameters.