An improved genetic algorithm for solving conic fitting problems

Song Gao, Li Chunping

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This paper presents an improved Genetic Algorithm for solving Conic Fitting problem. We first use several parallel small-populations Genetic Algorithms to obtain initial population, which has better average fitness. The range of mutation operator is also set to be gradually reduced with the growing of generation to guarantee the proportion of outstanding individuals within the population. An experiment shows that our improvements on Genetic Algorithm can remarkably increase the average fitness of population during evolution and enhance the performance of the algorithm as a whole.

LanguageEnglish
Title of host publication2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009
Pages800-804
Number of pages5
Volume4
DOIs
Publication statusPublished - 2009
Event2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009 - Los Angeles, CA, United States
Duration: 31 Mar 20092 Apr 2009

Conference

Conference2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009
CountryUnited States
CityLos Angeles, CA
Period31/03/092/04/09

ASJC Scopus subject areas

  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems
  • Software

Cite this

Gao, S., & Chunping, L. (2009). An improved genetic algorithm for solving conic fitting problems. In 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009 (Vol. 4, pp. 800-804). [5171106] https://doi.org/10.1109/CSIE.2009.134