摘要
遗传算法(Genetic Algorithm, GA)是一种模拟自然界生物进化过程与机制的一种优化搜索算法,有着广泛的应用前景。但是,简单遗传算法(Simple Genetic Algorithm,SGA)的收敛速度较慢,稳定性差,容易“过早收敛”。针对这些问题,本文提出了相应的解决办法,称为快速自适应遗传算法(Fast Self-Adaptive Genetic Algorithm, FSAGA),并通过仿真说明了算法的收敛快速性和全局收敛性都有了明显的改善。
Genetic Algorithm (GA) is a general purpose stochastic optimization method for search problems, which is invented to mimic some of the processes and mechanisms observed in natural evolution. But Simple Genetic Algorithms instinct deficiency like the unusually slow convergence, bad stability and easily-oriented prematurity has become the biggest obstacle for its further application. To solve the above problems, the corresponding solution—FSAGA (Fast Self-Adaptive Genetic Algorithm) is presented in this paper. Through simulation, it has shown that the convergent speed and global convergence are clearly improved.
出处
《系统仿真学报》
CAS
CSCD
2004年第1期122-125,共4页
Journal of System Simulation
关键词
遗传算法
收敛速度
全局最优
替代策略
交叉和变异
genetic algorithm
convergent speed
global optimization
replacement strategy
crossover and mutation
