This paper describes the techniques that have been used by the winning entry of the 2007 IEEE Congress on Evolutionary Computation (CEC2007) and the CIG2007 car racing competitions. The challenge is to race against an opponent around a track, trying to get as many points as possible. Previous research on similar problems are mostly based on either state-based or action-based controller architectures trained with machine learning techniques. In this paper, a hybrid controller architecture is presented, combining both the advantages of the existing architectures. The main component of the controller is designed as fuzzy systems whose membership functions are changeable according to the context. Finally, the competition results are given.
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