A genetic algorithm with chromosome-repairing for min-# and min-ε polygonal approximation of digital curves

Authors:
Bin Wang;Huazhong Shu;Limin Luo
Affiliations:
School of Computer Science and Engineering, Southeast University, No. 2 Sipailou, Nanjing 210096, China;School of Computer Science and Engineering, Southeast University, No. 2 Sipailou, Nanjing 210096, China;School of Computer Science and Engineering, Southeast University, No. 2 Sipailou, Nanjing 210096, China
Venue:
Journal of Visual Communication and Image Representation
Year:
2009

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Abstract

A genetic algorithm for solving min-@e 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.