There exists no memory of biologic evolution besides the individuals themselves. Indeed, the biologic milieu can change and a previously unfit action or individual can come to be more fit; it would be most dangerous to rely on the memory of the past. This contrasts with artificial evolution most often considering a fixed milieu: the generation of an unfit individual previously explored is only a waste of time. This paper aims at constructing a memory of evolution, and using it to avoid such fruitless explorations. A new evolution scheme, called mimetic evolution, gradually constructs two models along evolution, respectively memorizing the best and the worst individuals of the past generations. Standard crossover and mutation are replaced by mimetic mutation: individuals are attracted or repelled by these models. Mimetic evolution is extended from binary to continuous search spaces. Results of experiments on large-sized problems are detailed and discussed.