DocumentCode
3511734
Title
Simulation Research Based on an Improved Genetic Algorithm
Author
Jiang Jing ; Tan, Boxue ; Meng, Lidong ; Jiang, Jing
Author_Institution
Sch. of Electr. & Electron. Eng., Shandong Univ. of Technol., Zibo, China
fYear
2010
fDate
28-29 Oct. 2010
Firstpage
262
Lastpage
265
Abstract
Premature convergence is the main obstacle to the application of genetic algorithm. This paper makes improvement on traditional genetic algorithm by linear scale transformation of fitness function, using self-adaptive crossover and mutation probability and adopting close relative breeding avoidance method. Simulation results show that the improved algorithm outperforms traditional genetic algorithm in terms of convergent speed and the ability to find a global optimum.
Keywords
convergence; genetic algorithms; neural nets; probability; close relative breeding avoidance method; fitness function; genetic algorithm; linear scale transformation; premature convergence; self adaptive crossover probability; self adaptive mutation probability; Artificial neural networks; Convergence; Equations; Gallium; Genetic algorithms; Genetics; Optimization; close relative breeding avoidance; fitness function; genetic algorithm; self-adaptive;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligence Information Processing and Trusted Computing (IPTC), 2010 International Symposium on
Conference_Location
Huanggang
Print_ISBN
978-1-4244-8148-4
Electronic_ISBN
978-0-7695-4196-9
Type
conf
DOI
10.1109/IPTC.2010.76
Filename
5662981
Link To Document