Abstract:
This paper presents a constrained genetic approach (CGA) for reconstructing the Young's modulus of elastic objects. Qualitative a priori information is incorporated using...Show MoreMetadata
Abstract:
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.
Published in: Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)
Date of Conference: 12-17 May 2002
Date Added to IEEE Xplore: 07 August 2002
Print ISBN:0-7803-7282-4