Genetic algorithms for error-bounded polygonal approximation

Yung Nien Sun, Shu Chien Huang

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


A new polygonal approximation algorithm, employing the concept of genetic evolution, is presented. In the proposed method, a chromosome is used to represent a polygon by a binary string. Each bit, called a gene, represents a point on the given curve. Three genetic operators, including selection, crossover, and mutation, are designed to obtain the approximated polygon whose error is bounded by a given norm. Many experiments show that the convergence is guaranteed and the optimal or near-optimal solutions can be obtained. Compared with the Zhu-Seneviratne algorithm, the proposed algorithm successfully reduced the number of segments under the same error condition in the polygonal approximation.

Original languageEnglish
Pages (from-to)297-314
Number of pages18
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number3
Publication statusPublished - 2000 May

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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