We describe an approach to artificially evolving a drawing robot using implicit fitness functions, which are designed to minimise any direct reference to the line patterns made by the robot. We employ
this approach to reduce the constraints we place on the robot’s autonomy and increase its utility as a test bed for synthetically
investigating creativity. We demonstrate the critical role of neural network architecture in the line patterns generated by
the robot.