A new parameter identification method of soft biological tissue combining genetic algorithm with analytical optimization

The main goal of this paper is to provide simple parameter identification process for most of hyperelastic constitutive laws in biomechanics of soft tissues. The advantage of our approach lies on its rapidity and effectiveness, by reducing analytically the number of parameters to identify in the model during the identification procedure. With the use of genetic algorithms, the search for an adequate initial guess point is avoided and the space solution of the objective function is reduced to meet only parameters that cannot be calculated analytically. As an example, we focus on models that predict arterial wall behaviour such as laws based on Fung’s type energy function (Holzapfel, 2006) [14] and (Holzapfel et al., 2000 model) [15]. Our approach is applied on uniaxial extension tests and the results are compared with available data in the literature.