Drawing on a rich literature concerning social learn- ing in animals, this paper presents a variation of Grammatical Evolution (GE) which incorporates one of the most powerful forms of social learning, namely imitation learning. This replaces the traditional method of 'communication' between individuals in GE - crossover - which is drawn from an evolutionary metaphor. The paper provides an introduction to social learning, describes the proposed variant of GE, and tests this on a series of benchmark symbolic regression problems. The results obtained are encouraging being very competitive when compared with canonical GE. It is noted that the literature on social learning provides a number of useful meta-frameworks which can be used in the design of new search algorithms and to allow us to better understand the strengths and weaknesses of existing algorithms. Future work is indicated in this area.
