  We discuss how to use a Genetic Regulatory Network as an evolutionary
representation to solve a typical GP reinforcement problem, the pole balancing.
The network is a modified version of an Artificial Regulatory Network proposed
a few years ago, and the task could be solved only by finding a proper way of
connecting inputs and outputs to the network. We show that the representation
is able to generalize well over the problem domain, and discuss the performance
of different models of this kind.
