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This research develops methods of automating the production of behavioral robotics controllers. Population-based artificial evolution was employed to train neural network-based controllers to play a robotic version of the team game Capture the Flag. The robot agents used processed video data for sensing their environment. To accommodate the 35 to 150 sensor inputs required, large neural networks of arbitrary connectivity and structure were evolved. An intra-population competitive genetic algorithm was used and selection at each generation was based on whether the different controllers won or lost games over the course of a tournament. This paper focuses on the evolutionary neural controller architecture. Evolved controllers were tested in a series of competitive games and transferred to real robots for physical verification.