A Modified Genetic Algorithm and Its Capacity to Invert GOMS Model
遗传算法及其在GOMS模型反演中的应用效果分析

The Li-Strahler Geometry Optical Mutual Shadow(GOMS)model is a simple, yet efficient mechanism for modeling forest canopies as arrays of three-dimensional objects. In GOMS model, the signal received by the sensor is modeled as consisting of reflected light from tree crowns, their shadows and the background within the field of view of the sensor. The model is intrinsically bound to the influence of variation in viewing and illumination geometry, and may be inverted to recover biophysical parameters. However,because the GOMS model is a nonlinear model,difficulties exist to invert it. In this paper, a Modified Genetic Algorithm(MGA) are introduced for the inversion. Compared with the deterministic search method-Sequential Quadratic Programming(SQP),MGA can quickly find promising regions of the search space, but may take a relatively long time to reach the optimal solution. In contrary, SQP can converge to an extreme value quickly, but whether the result is optimal or not depends greatly on the initial value. For this reason, a mixed method is used to invert GOMS model in some cases. The result obtained by MGA is inputted to SQP as initial value. This method significantly increases the power of MGA in terms of solution quality and speed of convergence to the optimal.