[1]罗谦,左桃.基于基因表达式编程的推进学习算法[J].计算机技术与发展,2013,(02):165-169.
　LUO Qian,ZUO Tao.AdaBoost Algorithm Based on GEP[J].,2013,(02):165-169.
点击复制

基于基因表达式编程的推进学习算法()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2013年02期
页码:
165-169
栏目:
智能、算法、系统工程
出版日期:
1900-01-01

文章信息/Info

Title:
AdaBoost Algorithm Based on GEP
文章编号:
1673-629X（2013）02-0165-05
作者:
罗谦1; 左桃2
[1]中国民用航空总局第二研究所 信息公司;[2]中国人民银行 成都分行
Author(s):
LUO Qian; ZUO Tao
关键词:
遗传算法; 基因表达式编程; 机器学习; 推进学习
Keywords:
genetic algorithms; gene expression programming (GEP); machine learning; boosting
文献标志码:
A
摘要:
为进一步改善传统遗传算法在函数发现应用上的搜索能力,提高算法的收敛速度和精度,文中提出了GEPAda-Boost算法.该算法在推进学习AdaBoost算法框架下,利用具有强大函数发现能力的基因表达式编程GEP作为每次迭代过程中的弱学习器,同时引入含分布因子的适应度函数在迭代中筛选出最优假设,最后通过投票策略组合多轮最优假设产生算法结果.丰富的实验结果表明新算法对权重计算和概率分布产生了积极的影响,与朴素GEP算法和GPBoosting算法对比分析发现该算法能分别提升16.7%和40.8%的精度
Abstract:
In order to improve the tradition GA's searching ability in regression problems,enhance the algorithm in convergence rate and precision,propose GEPAdaBoost algorithm. Based on the frame of AdaBoost,the GEPAdaBoost takes GEP as a weak-learner for every iteration by using its power ability of symbolic regression. Then the new fitness function of every iteration can produce good hypothesis based on weight computing and distributing. Finally the algorithm will get optimization result by using voting strategy in multi-hypothe-sis. Experiments show that the new algorithm is more accurate than the traditional GEP algorithms by 16. 7% and GPBoosting algorithms by 40. 8%

相似文献/References:

[1]冯智明,苏一丹,覃华,等.基于遗传算法的聚类与协同过滤组合推荐算法[J].计算机技术与发展,2014,24(01):35.
　FENG Zhi-ming,SU Yi-dan,QIN Hua,et al.Recommendation Algorithm of Combining Clustering with Collaborative Filtering Based on Genetic Algorithm[J].,2014,24(02):35.
[2]余晓光 严洪森 殷乾坤.基于Flexsim的车间调度优化[J].计算机技术与发展,2010,(03):44.
　YU Xiao-guang,YAN Hong-sen,YIN Qian-kun.Workshops Scheduling Optimization Based on Flexsim Simulation[J].,2010,(02):44.
[3]贺计文 宋承祥 刘弘.基于遗传算法的八数码问题的设计及实现[J].计算机技术与发展,2010,(03):105.
　HE Ji-wen,SONG Cheng-xiang,LIU Hong.Design and Implementation of Eight Puzzle Problem Based on Genetic Algorithms[J].,2010,(02):105.
[4]沈珏萍 庄亚明.基于Agent的二级供应链企业自动谈判研究[J].计算机技术与发展,2010,(03):121.
　SHEN Jue-ping,ZHUANG Ya-ming.A Research for Company Automatic Negotiation in Secondary Supply Chain Based on Agent[J].,2010,(02):121.
[5]张磊 王晓军.基于遗传算法的业务流程测试[J].计算机技术与发展,2010,(03):155.
　ZHANG Lei,WANG Xiao-jun.Test of Business Process Based on Genetic Algorithm[J].,2010,(02):155.
[6]曹道友 程家兴.基于改进的选择算子和交叉算子的遗传算法[J].计算机技术与发展,2010,(02):44.
　CAO Dao-you,CHENG Jia-xing.A Genetic Algorithm Based on Modified Selection Operator and Crossover Operator[J].,2010,(02):44.
[7]范维博 周俊 许正良.应用遗传算法求解第一类装配线平衡问题[J].计算机技术与发展,2010,(02):194.
　FAN Wei-bo,ZHOU Jun,XU Zheng-liang.Appication of Genetic Algorithm to Assembly Line Balancing[J].,2010,(02):194.
[8]熊伟平 曾碧卿.几种仿生优化算法的比较研究[J].计算机技术与发展,2010,(03):9.
　XIONG Wei-ping,ZENG Bi-qing.Studies on Some Bionic Optimization Algorithms[J].,2010,(02):9.
[9]余晓光 严洪森.基于禁忌搜索遗传混合算法的装配线平衡[J].计算机技术与发展,2010,(05):5.
　YU Xiao-guang,YAN Hong-sen.Assembly Line Balancing Based on Tabu Search and Genetic Hybrid Algorithm[J].,2010,(02):5.
[10]黄永聪 张旭[] 吴义纯 吴琦 程家兴.改进的径向基函数网络的研究及应用[J].计算机技术与发展,2010,(05):158.
　HUANG Yong-cong,ZHANG Xu,WU Yi-chun,et al.Research and Application of Improved Genetic Algorithm-Based RBFANN[J].,2010,(02):158.

更新日期/Last Update: 1900-01-01