Parallel Hierarchical Genetic Algorithm for Scattered Data Fitting through B-Splines

Curve fitting to unorganized data points is a very challenging problem that arises in a wide variety of scientific and engineering applications. Given a set of scattered and noisy data points, the goal is to construct a curve that corresponds to the best estimate of the unknown underlying relationship between two variables. Although many papers have addressed the problem, this remains very challenging. In this paper we propose to solve the curve fitting problem to noisy scattered data using a parallel hierarchical genetic algorithm and B-splines. We use a novel hierarchical structure to represent both the model structure and the model parameters. The best B-spline model is searched using bi-objective fitness function. As a result, our method determines the number and locations of the knots, and the B-spline coefficients simultaneously and automatically. In addition, to accelerate the estimation of B-spline parameters the algorithm is implemented with two levels of parallelism, taking advantages of the new hardware platforms. Finally, to validate our approach, we fitted curves from scattered noisy points and results were compared through numerical simulations with several methods, which are widely used in fitting tasks. Results show a better performance on the reference methods.

Scattered data fitting by minimal surface

The goal of this paper is to develop a computational model for obtaining the fitting surface to the given scattered data with minimal area. The basic idea of the model is to utilize the B-spline and area minimization. The model is turned into a Tikhonov regularization model finally. By choosing the regularization parameters with the L-curve criterion and the GCV method, respectively, numerical experiments indicate that the model can provide an acceptable compromise between the minimization of the data mismatch term and the area of the surface.