Pattern Recognition Letters
Fitting Spline Functions to Noisy Data Using a Genetic Algorithm
Proceedings of the 5th International Conference on Genetic Algorithms
Spline Interpolation with Genetic Algorithms
SMA '97 Proceedings of the 1997 International Conference on Shape Modeling and Applications (SMA '97)
Approximation of digital curves with line segments and circular arcs using genetic algorithms
Pattern Recognition Letters
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Cubic Bézier approximation of a digitized curve
Pattern Recognition
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Curve fitting has many applications in lots of domains. The literature is full of fitting methods which are suitable for specific kinds of problems. In this paper we introduce a more general method to cover more range of problems. Our goal is to fit some cubic Bezier curves to data points of any distribution and order. The curves should be good representatives of the points and be connected and smooth. Theses constraints and the big search space make the fitting process difficult. We use the good capabilities of the coevolutionary algorithms in large problem spaces to fit the curves to the clusters of the data. The data are clustered using hierarchical techniques before the fitting process.