Effect of cornea material stiffness on measured intraocular pressure.

Intraocular pressure (IOP) in the human eye as measured by a Goldmann applanation tonometer (GAT) is known to be affected by individual differences in central corneal thickness (CCT). However, data from clinical studies also show considerable scatter in the correlation between measured IOP and CCT. One possible implication of the large observed scatter is that the true IOP (IOPT) also depends significantly on individual variations in the material stiffness properties of the cornea. This hypothesis is explored and evaluated herein using computational simulation of applanation tonometry. A simplified 2D finite element model of the eye, which employs a calibrated nonlinear transversely isotropic material model for the cornea, is developed, and a series of GAT simulations is carried out to study the effect of geometry and material properties of the cornea on the IOP readings obtained via GAT. The results of this parametric study provide a simple correction equation, which quantifies the effect on measured IOP of variations in CCT and corneal material stiffness. In addition, several previously proposed IOP correction equations are compared with the one proposed here.

Implicit representation in genetic algorithms using redundancy.

A new representation combining redundancy and implicit fitness constraints is introduced that performs better than a simple genetic algorithm (GA) and a structured GA in experiments. The implicit redundant representation (IRR) consists of a string that is over-specified, allowing for sections of the string to remain inactive during function evaluation. The representation does not require the user to prespecify the number of parameters to evaluate or the location of these parameters within the string. This information is obtained implicitly by the fitness function during the GA operations. The good performance of the IRR can be attributed to several factors: less disruption of existing fit members due to the increased probability of crossovers and mutation affecting only redundant material; discovery of fit members through the conversion of redundant material into essential information; and the ability to enlarge or reduce the search space dynamically by varying the number of variables evaluated by the fitness function. The IRR GA provides a more biologically parallel representation that maintains a diverse population throughout the evolution process. In addition, the IRR provides the necessary flexibility to represent unstructured problem domains that do not have the explicit constraints required by fixed representations.