Well, sort of. Genetic programming with probabilistic modeling and sampling instead of crossover/mutation (i.e. it an estimation-of-distribution algorithm), where programs are represented in reduced (normal) form and which variables to use in the probabilistic models is determined adaptively and changes as the search progresses... ;->

Genetic programming, with mutations guided by learned patterns from a corpus of data? (or that's what I imagine it to be)