DocumentCode
527480
Title
An improved genetic algorithm and its application
Author
Liu Gang ; Xuemei, Wang ; Yang Lina
Author_Institution
Sch. of Inf. Sci. & Eng., Henan Univ. of Technol., Zhengzhou, China
Volume
7
fYear
2010
fDate
10-12 Aug. 2010
Firstpage
3788
Lastpage
3791
Abstract
A novel hybrid learning algorithm based on a genetic algorithm, named simulated annealing genetic algorithm, is put forward in this paper to solve the optimization model of (N+M) fault-tolerant systems. A new adding method based on simulated annealing strategy is first used to generate the initial structure. Then a hybrid algorithm based on genetic algorithm and simulated annealing algorithm is used to adjust all parameters including crossover probability and mutation probability. Simulations are presented to illustrate the performance of the proposed algorithm.
Keywords
fault tolerance; genetic algorithms; learning (artificial intelligence); simulated annealing; crossover probability; fault tolerant system; hybrid learning algorithm; mutation probability; optimization model; simulated annealing genetic algorithm; Biological system modeling; Control systems; Convergence; Fault tolerance; Fault tolerant systems; Simulated annealing; fault-tolerant system; genetic algorithm; optimization model; simulated annealing algorithm;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation (ICNC), 2010 Sixth International Conference on
Conference_Location
Yantai, Shandong
Print_ISBN
978-1-4244-5958-2
Type
conf
DOI
10.1109/ICNC.2010.5582947
Filename
5582947
Link To Document