A constrained genetic approach for reconstructing Young's modulus of elastic objects from boundary displacement measurements

This paper presents a constrained genetic approach (CGA) for reconstructing the Young's modulus of elastic objects. Qualitative a priori information is incorporated using a rank based scheme to constrain the admissible solutions. Balance between the fitness function (adhesion to the measurement data) and the penalty function (fidelity to a priori knowledge) is achieved by a stochastic sort algorithm. The over-smoothing of Young's modulus discontinuity is avoided without the need of computing a deterministic weight coefficient. The experiment on synthetic data indicates that the proposed method not only reconstructed reliable Young's modulus from noisy data, but also expedited the convergence process significantly.