Abstract We consider a form of phenotype plasticity in Genetic Programming (GP). This takes the form of a set of real-valued numerical parameters associated with each individual, an optimisation (or learning) algorithm for adapting their values, and an inheritance strategy for propagating learned parameter values to o spring. We show that plastic GP has signi cant bene ts including faster evolution and adaptation in changing environments compared with non-plastic GP. The paper also considers the di erences between Darwinian and Lamarckian inheritance schemes and shows that the former is superior in dynamic environments.