This chapter illustrates the potential of genetic programming (GP) in the field of macro­ mechanical modeling, addressing the problem of identification of a mechanical model for a material. Two kinds of models are considered. One-dimensional dynamic models are rep­resented via symbolic formulations termed rheological models, which are directly evolved by GP. Three-dimensional static models of hyperelastic materials are expressed in terms of strain energy functions. A model is rated based on the distance between the behavior predicted by the model, and the actual behavior of the material given by a set of me­ chanical experiments. The choice of GP is motivated by strong arguments, relying on the tree-structure of rheological models in the first case, and on the need for first and second order derivatives in the second case. Key issues are the exploration of viable individuals only, and the use of Gaussian mutations to optimize numerical constants.