摘要
针对遗传算法的来源、基本原理、数学机理、特点进行了论述;然后详细分析了简单遗传算法在应用过程中出现收敛过慢和早熟现象的原因,并简单介绍了一种基于个体适应值的自适应调整交叉率和变异率的自适应遗传算法(AGA)。为了提高遗传算法的收敛性能,在分析其不足后,从三个方面进行改进并提出一种改进算法(IAGA)。最后,针对几种优化问题对所提出的算法和AGA进行了性能比较,证明提出的改进算法在达到最优解的收敛性能方面有了明显的提高。
This paper first introduces and discusses in detail the origin, basic principle, mathematic mechanism and characteristics of genetic algorithm . Secondly,in order to improve the convergence of Genetic Algorithm, the paper analyzes the cause of the slow and premature convergence shortages of the SGA in application and briefly introduces an AGA whose pc and pm are based on the fitness values of the individual. After discussing their deficiency, it modifies them through three aspects and presents an improved one （ Adaptive Genetic Algorithm）. At the end of the paper, based on some optimizations, the performance of the IAGA is compared with that of AGA in order to show that the improved astringency put forward in this paper enhances the convergence of obtaining optimal solution.
出处
《计算机仿真》
CSCD
北大核心
2009年第7期228-231,共4页
Computer Simulation
关键词
简单遗传算法
自适应遗传算法
适应值
Simple genetic algorithm
Adaptive genetic algorithm
Fitness values of the individual
