A Hybrid Particle Swarm Genetic Algorithm for Classification
The shortcomings about present genetic algorithm applying to classification are analyzed. Using the method of minimum propagating tree can cluster complex shape and non-overlap sample candidate solutions into races. The algorithm regulates optimization with race method and controls individuals in a micro way with race crossover. We also mixed crossover operator based on the thought of particle swarm optimization in genetic algorithm. With these operators the speed of convergence and population diversity are well balanced. Meanwhile, according to the classified questions characteristic, we designed corresponding encoding method, fitness function, and used sowing seeds way to create initial population to get better classification precision; At last, through the international data sets and classical functions, and compared with other algorithms classified effects, the results are given to illustrate the effectiveness of this algorithm.
Rui Ding Hongbin Dong Xianbin Feng Guisheng Yin
School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150080, Chi School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150080, Chi Department of Computer Science, Mudanjiang Normal University, Mudanjiang Heilongjiang 157000, China National Science Park, Harbin Engineering University, Harbin 150001, China
国际会议
三亚
英文
1351-1355
2009-04-24（万方平台首次上网日期，不代表论文的发表时间）