... of the decision variables were determined based on the clinical experience. Finally, the problem model was solved by using nondominated sorting genetic algorithms II (NSGA-II) algorithm to give respective optimal protocol for three sizes of cervical tumors. Every protocol was assessed by the evaluation criterion...

.... For this purpose, we implemented an optimization scheme, involving a genetic algorithm (GA) and an inverse finite element analysis (FEA), in order to estimate the material properties of the pubovisceralis muscle (PVM). The datasets of five women were included in this noninvasive analysis. The numerical models...

... or screw loosening. There have been no studies that have addressed the multiobjective design optimization of the pedicle screws. The multiobjective optimization methodology was applied and it consisted of finite element method, Taguchi method, artificial neural networks, and genetic algorithms. Three...

... was devised to produce an FE model of liver tissue. Material constants were identified by a genetic algorithm applied to the experimental results of unconfined compressions of bovine liver. The approach for SPS involves B-spline interpolations of sample data generated from the FE model of liver. Two...

...) and a genetic algorithm (GA) optimizing method. The average initial shear modulus of brainstem matrix of 4-week old pigs was 12.7 Pa , and therefore the brainstem offers little resistance to large shear deformations in the parallel or perpendicular directions, due to the dominant contribution of the matrix...

... and thickness of the cartilage layer. In this study, we developed a micro-indentation testing system to determine the compressive and biphasic mechanical properties of cartilage in the small joints of the mouse. A nonlinear optimization program employing a genetic algorithm for parameter estimation, combined...

... of the objective function and the large number of constraints, the commonly applied gradient-based optimization technique ( 28 29 ) does not converge to a global optimum value. This problem was averted by using a genetic algorithm ( 30 31 ). Our results indicate that variations in the stress distribution...