基因学习算法及其在图论中的应用  

Gene Learning Algorithm and Application to Graph Theory

作　　者：金炳尧[1] 何振亚[2] 蔚承建[2] 

机构地区：[1]浙江师范大学计算机科学系,金华321004 [2]东南大学无线电系,南京210096

出　　处：《数据采集与处理》2000年第2期133-137,共5页Journal of Data Acquisition and Processing

摘　　要：在 PBIL算法及自私基因算法的基础上 ,提出了一个适应性更广、搜索能力更强的优化搜索算法。该算法从各基因位的初始等位基因概率出发 ,通过一系列概率采样、选择与搜索、概率修正等操作 ,使搜索空间逐步收敛于最优点。该算法既吸取了遗传算法的群体搜索的特点 ,又吸收了局部搜索算法的局部搜索能力强的优点。最后介绍了该算法在图论中的几个应用实例。A new algorithm called gene learning algorithm(GL) which is developed from PBIL and selfish algorithm, is presented. GL begins with the initial alleles probabilities of every gene location and is followed by a series of repeated processes of sampling, selecting and search, probabilities updating until the stop condition is met. Applications of GL to graph theory are also presented and satisfactory results are obtained.

关 键 词：基因学习算法 PBIL算法 图论 遗传算法 

分 类 号：O157.5[理学—数学] O242.23[理学—基础数学]