基于自适应遗传算法函数优化与仿真  

Simulationand Adaptive Genetic Algorithm Used in Function Optimization

作　　者：黄江波[1,2] 付志红[2] 

机构地区：[1]长江师范学院物理学与电子工程学院,重庆408100 [2]重庆大学电气工程学院,重庆400044

出　　处：《计算机仿真》2011年第5期237-240,共4页Computer Simulation

基　　金：国家自然科学基金资助项目(40874094)

摘　　要：研究模式识别函数优化问题,遗传算法在人工智能中起重要的作用,但遗传算法的性能深受算法参数的影响。为提高全局性寻优和提高算法的搜索性能,避免算法在寻优搜索中陷入局部极值,研究了遗传算法和自遗传算法算子的工作机理,认为Pc和Pm的大小是和个体的适应度有联系的,算法在运行过程中始终要保护适应度高的个体。提出了一种新的自适应遗传算法用于函数优化中,对三个常用的标准测试函数进行了优化,并将其测试结果与简单遗传算法的进行仿真比较,仿真结果表明自适应机制确实提高了算法的搜索性能,取得了较好效果。Genetic algorithms play a very important role in artificial intelligence.Performance of genetic algorithms is dramaticly influenced by algorithmic settings.To improve the research performance of genetic algorithm and avoid its limitation of local optimization,genetic algorithms is studied and it is found that the size of Pc and Pm is related to the fitness of individuals,and the algorithm should always protect the individuals with high fitness in the running process,A new adaptive genetic algorithm is applied to optimize three standard benchmark functions selected in this paper.The comparison between the results of the present algorithm and simulation of simple genetic algorithm shows that the technique has improved the performance of genetic algorithm.

关 键 词：遗传算法 自适应 函数 搜索 优化 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]