title: A new genetic algorithm for polygonal approximation.
abstract: In this chapter, the problem of approximating a closed digital curve with a simplified representation by a set of feature points containing almost complete information of the contour, i.e., dominant points, is addressed. We adopt an approach based on genetic algorithms (GAs) since they use parallel search and have good performance in solving optimization problems. The chromosome coincides with an approximating polygon and is represented by a binary string. Each bit, called gene, represents a curve point where dominant points have 1-value. The proposed algorithm enhances the selection and mutation phase avoiding the premature convergence issue. Our method is compared to other similar approaches and its efficiency is clearly demonstrated by experimental results giving a better approximation by lowering the error norm with respect to the original curves.
authors: Department of Mathematics and Computer Science, University of Cagliari, Italy. andrea.morgera@unica.it
authors: Cecilia Di
authors: CD
authors: Ruberto
authors: Andrea
authors: A
authors: Morgera
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D008957:Models, Genetic; D015233:Models, Statistical