Simulation research based on a self-adaptive genetic algorithm
Jing Jiang, Meng Li-dong, Li Shuling -, Lin Jiang · 2010
Crossover probability Pcand mutation probability Pmare important parameters of genetic algorithm. Self-adaptive genetic algorithm can reach good balance between convergence speed and global optimum by adjusting Pcand Pmadaptively 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.