Subtree deactivation control with grammatical Genetic Programming in dynamic environments
Michael O’Neill, Anthony Brabazon, Erik Hemberg · 2008
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 sub-tree representation adopted in Genetic Programming. Results presented on benchmark dynamic problem instances provides evidence to support that such an adaptive advantage exists.