  In this paper, we present a new procedure to automatically generate
interpretable hyperelastic material models. This approach is based on symbolic
regression which represents an evolutionary algorithm searching for a
mathematical model in the form of an algebraic expression. This results in a
relatively simple model with good agreement to experimental data. By expressing
the strain energy function in terms of its invariants or other parameters, it
is possible to interpret the resulting algebraic formulation in a physical
context. In addition, a direct implementation of the obtained algebraic
equation is possible. For the validation of the proposed approach, benchmark
tests on the basis of the generalized Mooney-Rivlin model are presented. In all
these tests, the chosen ansatz can find the predefined models. Additionally,
this method is applied for the multi-axial loading data set of vulcanized
rubber. Finally, a data set for a temperature-dependent thermoplastic polyester
elastomer is evaluated. In latter cases, good agreement with the experimental
data is obtained.
