Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
532769 | Journal of Visual Communication and Image Representation | 2009 | 12 Pages |
Abstract
A genetic algorithm for solving min − ε polygonal approximation and min − # polygonal approximation is proposed in this paper. It combines traditional split-and-merge techniques with a novel chromosome-repairing scheme to cope with constraints. Due to this combination of techniques we call our new method SMCR. In this new scheme an infeasible solution cannot only be easily transformed into a feasible one, but also be optimized. The experimental results show that the proposed SMCR has higher performance than the other GA-based methods and some non-GA-based methods.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Bin Wang, Huazhong Shu, Limin Luo,