Biological evolution is good at dealing with environmental changes: Nature ceaselessly repeats its experiments and is not misled by any explicit memory of the past. This contrasts with artificial evolution most often considering a fixed milieu, where re-generating an individual does not bring any further information. This paper aims at avoiding such uninformative operations, via some explicit memory of the past evolution: the best and the worst individuals previously met by evolution are respectively memorized within two virtual individuals. Evolution may then use these virtual individuals as social models, to be imitated or rejected. In mimetic evolution, standard crossover and mutation are replaced by a single operator, social mutation, which moves individuals farther away or closer toward the models. This new scheme involves two main parameters: the social strategy (how to move individuals with respect to the models) and the social pressure (how far the offspring go toward or away from the models). Experiments on large-sized binary problems are detailed and discussed.