Abstract-- We investigate the usefulness of a subtree deactivation control mechanism which is open to evolutionary learning. It is hypothesised that this representation confers an adaptive advantage in dynamic environments over the standard subtree representation adopted in Genetic Programming. Results presented on benchmark dynamic problem instances provides evidence to support that such an adaptive advantage exists.