[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.
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基于基因表达式编程的推进学习算法()
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《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 QianZUO 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 algorithmsgene expression programming (GEP)machine learningboosting
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%

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更新日期/Last Update: 1900-01-01