Computer Science and Information Engineering, World Congress on (2009)
Los Angeles, California USA
Mar. 31, 2009 to Apr. 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.134
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.
Genetic Algorithm, Conic Fitting Problem
Song Gao, Chunping Li, "An Improved Genetic Algorithm for Solving Conic Fitting Problems", Computer Science and Information Engineering, World Congress on, vol. 04, no. , pp. 800-804, 2009, doi:10.1109/CSIE.2009.134