In this paper, a new encoding scheme is presented for learning the Takagi–Sugeno (T–S) fuzzy model from data by genetic algorithms (GAs). In the proposed encoding scheme, the rule structure (selection of rules and number of rules), the input structure (selection of inputs and number of inputs), and the antecedent membership function (MF) parameters of the T–S fuzzy model are all represented in one chromosome and evolved together such that the optimisation of rule structure, input structure, and MF parameters can be achieved simultaneously. The performance of the developed evolving T–S fuzzy model is first validated by studying the benchmark Box–Jenkins nonlinear system identification problem andnonlinear plant modelling problem, and comparing the obtained results with other existing results. Then, it is applied to approximate the forwardand inverse dynamic behaviours of a magneto-rheological (MR) damper of which identification problem is significantly difficult due to itsinherently hysteretic and highly nonlinear dynamics. It is shown by the validation applications that the developed evolving T–S fuzzy model canidentify the nonlinear system satisfactorily with acceptable number of rules and appropriate inputs.