Current concept learners are limited in their applicability as they generally rely on comparatively poor knowledge representation facilities (e.g. attribute value pairs, flattened horn clauses). The work carried out in support of my thesis has involved extending concept learning to a higher order setting by developing a novel representation based on closed Escher terms for highly structured data. The added expressiveness offered by the proposed representation results in an explosion of the search space, which is compounded by the increased complexity of its structure. This paper describes an investigation into the use of genetic programming techniques to allow the exploitation of higher-order features during the induction of structured concept descriptions.