|Table of Contents|

Novel Dynamic Population and Anisomerous Crossover Genetic Algorithm


Research Field:
Publishing date:


Novel Dynamic Population and Anisomerous Crossover Genetic Algorithm
ZHANG Jin-ping12LIU Jie1LI Yun-gong1
1.School of Mechanical Engineering and Automation,Northeastern University,Shengyang 110004,China;2.Department of Mechanical Engineering,Shenyang Institute of Chemical Technology,Shenyang 110142,China
genet ic algo rithm real coding crossover opt im ization convergence
The shortcom ings and the essent ia ls o f genetic algorithm are discussed. A novel genetic a lgorithm is proposed whose popu lat ion is dynam ic and whose crossover is anisomerous. In every propagate process, the propagate tmi es of parents random fluctuates in lmi itat ive range, and the size of population is dynam ic. A ccord ing to the principle of balance nature, the size of population is kept at the average of w ave by the crossover and se lection. In order to mi prove the variety and universa lity of the new populat ion in the parameter space, themethod of anisomerous crossover is designed. Based on the new a lgorithm, the method of double select ion is presented. Calculating some typica l instants and comparingw ith other genetic algorithms, the results show that the convergence rate, success rate and ant-i premature of the nove l genetic algorthm are superior.


[ 1] 李纯莲, 王希诚, 赵金城. 基于浮点数编码的信息 熵控制多种群遗传算法[ J]. 南京理工大学学报, 2004, 28( 5): 453- 456.
[ 2] SahabM G, Ashour A F, To ropov V V. A hybrid genetic algor ithm fo r re inforced concrete flat slab bu ildings [ J]. Computers and S tructures, 2005, 83 ( 8 - 9): 551- 559.
[ 3] Tam SM, Cheung K C. Genetic a lgo rithm based defect identifica tion sy stem [ J]. Expert Sy stem s w ith Applications, 2000, 18 ( 1): 17- 25.
[ 4] Re id D J. Genetic a lgor ithms in constrained optim ization [ J ]. M a them atical and Computer M odelling, 1996, 23 ( 5): 87- 111.
[ 5] 任子武, 伞治. 实数遗传算法的改进及性能研究 [ J] . 电子学报, 2007, 35( 2): 269- 274.
[ 6] Fe rentinos K P, A lbr ight L D. Optim a l design of plant lighting system by gene tic algor ithm s [ J]. Eng ineer ing Applications o f Artificial Inte lligence, 2005, 18 ( 4): 473- 484.
[ 7] W ang H siaofan, W u Kuangyao. H ybr id genetic a lgor ithm for optim ization prob lem s w ith pe rmuta tion property [ J]. Compu ters and Operations Research, 2004, 31 ( 14): 2453- 2471.
[ 8] R iechm ann T. Gene tic a lgor ithm learn ing and evo lutionary gam es [ J]. Journa l o f Econom icDynam ics and Contro ,l 2001, 25 ( 6- 7): 1 019- 1 037.
[ 9] H ansen J V. Genetic search m ethods in air tra ffic contro l [ J] . Computers and Operations Research, 2004, 31 ( 3): 445- 459.
[ 10] 董颖, 刘欢杰, 许宝栋, 等. 一种基于实数编码的改 进遗传算法[ J]. 东北大学学报, 2005, 26 ( 4): 219 - 221.


Last Update: 2007-08-30