  We wish to explore the contribution that asocial and social learning might
play as a mechanism for self-adaptation in the search for variable-length
structures by an evolutionary algorithm. An extremely challenging, yet simple
to understand problem landscape is adopted where the probability of randomly
finding a solution is approximately one in a trillion. A number of learning
mechanisms operating on variable-length structures are implemented and their
performance analysed. The social learning setup, which combines forms of both
social and asocial learning in combination with evolution is found to be most
performant, while the setups exclusively adopting evolution are incapable of
finding solutions.
