Title :
Simulation research based on a self-adaptive genetic algorithm
Author :
Jing, Jiang ; Li-Dong, Meng ; Shu-Ling, Li ; Lin, Jiang
Author_Institution :
Sch. ofElectrical & Electron. Eng., Shandong Univ. of Technol., Zibo, China
Abstract :
Crossover probability Pc and mutation probability Pm are important parameters of genetic algorithm. Self-adaptive genetic algorithm can reach good balance between convergence speed and global optimum by adjusting Pc and Pm adaptively according to the fitness values difference among individuals. But it is not suitable to the early period of the evolutionary process. The improved self-adaptive GA proposed by this paper can avoid this drawback. And this paper trains a neural network by using the three algorithms respectively. Simulation results show that the improved self-adaptive genetic algorithm is optimal.
Keywords :
genetic algorithms; probability; crossover probability; mutation probability; self-adaptive genetic algorithm; Adaptation model; Gallium; Genetics; crossover probability; genetic algorithm; mutation probability; self-adaptive;
Conference_Titel :
Intelligent Computing and Intelligent Systems (ICIS), 2010 IEEE International Conference on
Conference_Location :
Xiamen
Print_ISBN :
978-1-4244-6582-8
DOI :
10.1109/ICICISYS.2010.5658541