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In this paper we present the autonomous, walking humanoid robot ELVINA and the first experiments in genetic programming performed with it. A steady state evolutionary strategy is running on the robot’s onboard computer. Individuals are evaluated and fitness scores are automatically determined using the robots onboard digital camera and near-infrared range sensor. The experiments are performed in order to optimize a by hand developed locomotion controller. By using this system, we evolved gait patterns that locomote the robot in a straighter path and in a more robust way, than the previously manually developed gait did.