TY - GEN
T1 - An improved genetic algorithm for solving conic fitting problems
AU - Gao, Song
AU - Chunping, Li
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=71049193450&partnerID=8YFLogxK
U2 - 10.1109/CSIE.2009.134
DO - 10.1109/CSIE.2009.134
M3 - Conference contribution
AN - SCOPUS:71049193450
SN - 9780769535074
VL - 4
T3 - 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009
SP - 800
EP - 804
BT - 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009
T2 - 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009
Y2 - 31 March 2009 through 2 April 2009
ER -