Using Hybrid Genetic Algorithms to Solve Two Types of Polygonal Approximation Problems

WANG Bin; SHI Chao-jian

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Using Hybrid Genetic Algorithms to Solve Two Types of Polygonal Approximation Problems

更新时间：2025-07-16

- Using Hybrid Genetic Algorithms to Solve Two Types of Polygonal Approximation Problems
- Acta Electronica Sinica Vol. 35, Issue 8, Pages: 1527-1532(2007)
- 作者机构：
  
  1. 南京财经大学电子商务重点实验室,江苏,南京,210003
  2. 东南大学计算机科学与工程学院,江苏,南京,210096
  3. 上海海事大学商船学院,上海,200135
  4. 复旦大学计算机科学与工程系,上海,200433
  5. 南京财经大学电子商务重点实验室江苏南京,210003
  6. 东南大学计算机科学与工程学院江苏南京,210096
  7. 上海海事大学商船学院上海,200135
  8. 复旦大学计算机科学与工程系上海,200433
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391
- Published：2007
- 稿件说明：
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WANG Bin, SHI Chao-jian. Using Hybrid Genetic Algorithms to Solve Two Types of Polygonal Approximation Problems[J]. Acta Electronica Sinica, 2007, 35(8): 1527-1532.
DOI：

WANG Bin, SHI Chao-jian. Using Hybrid Genetic Algorithms to Solve Two Types of Polygonal Approximation Problems[J]. Acta Electronica Sinica, 2007, 35(8): 1527-1532. DOI：

摘要

多边形近似是一种重要的曲线描述方法.研究用遗传算法求解平面数字曲线的多边形近似碰到的两个主要问题是不可行解难以处理和基本遗传算法局部搜索能力差.针对这两个问题

本文提出了一种组合拆分与合并技术的混合遗传算法(SMGA).它将两种经典算法拆分技术与合并技术引入到对染色体的修复过程.采用这种方法

一个不可行解不仅能得到快速的修复

而且还能被推进到解空间中一个局部较优的位置.它的另外一个优点是:不同于已有的遗传算法

只能解决一类多边形近似问题

SMGA是一种能求解两类多边形近似问题的通用算法.实验结果表明:本文提出的算法比其他同类算法性能更优越.

Abstract

Polygonal approximation of digital curve is an important curve representation method.The main problems existing in genetic algorithm based methods are the difficulty of coping with the infeasible solutions and the poor local search ability.In this paper

we propose a hybrid genetic algorithm combined with split and merge technique (SMGA) for polygonal approximation.Its main idea is that two classical methodssplit and merge techniques are used to repair the infeasible solutions.In this method

an infeasible solution can not only be repaired rapidly

but also be pushed to a local optimal location in the solution space.Another advantage of SMGA is that it can solve two types of polygonal approximation problems.The experimental results demonstrate that SMGA is robust and outperforms the other genetic algorithm based methods.

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