Abstract: Data fitting with B-splines is a challenging problem in reverse engineering for CAD/CAM, virtual reality,data visualization, and many other fields. It is well-known that the fitting improves greatly if knotsare considered as free variables. This leads, however, to a very difficult multimodal and multivariatecontinuous nonlinear optimization problem, the so-called knot adjustment problem. In this context, thepresent paper introduces an adapted elitist clonal selection algorithm for automatic knot adjustment ofB-spline curves. Given a set of noisy data points, our method determines the number and location of knotsautomatically in order to obtain an extremely accurate fitting of data. In addition, our method minimizesthe number of parameters required for this task. Our approach performs very well and in a fully automaticway even for the cases of underlying functions requiring identical multiple knots, such as functions withdiscontinuities and cusps. To evaluate its performance, it has been applied to three challenging testfunctions, and results have been compared with those from other alternative methods based on AIS andgenetic algorithms. Our experimental results show that our proposal outperforms previous approachesin terms of accuracy and flexibility. Some other issues such as the parameter tuning, the complexity ofthe algorithm, and the CPU runtime are also discussed.

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