  Understanding the linear or nonlinear relationship between load and
deformation in structural materials or structural frames is a key to a proper
and a well-represented simulation. This research is dedicated to model a cyclic
load-deformation hysteresis relationship, captured from experimental results,
and utilize it to represent the cyclic hysteresis data. The Genetic Algorithm
is used to find the best parameters to introduce the model and to minimize the
deviation between the simulation and the experimental results. In other words,
the parameters associated with the loading response of any displacement
pattern, are found, while minimizing the deviation between simulation loading
response and the loading data carried out from the experiment. First, to reduce
the data size recorded with measuring devices or Linear Variable Differential
Transformers (LVDTs in short), a data resampling technic is proposed. Second,
the resampled data is used in Genetic Algorithm to seek for the best parameters
to describe the model. This method could be used to train models to predict the
capacity and performance of a materials and frames when they are exposed to any
deformation patterns. Numerous examples are shown at the end of the paper.
