On Approximating K-MPE of Bayesian Networks Using Genetic Algorithm

N. Sriwachirawat, S. Auwatanamongkol
{"title":"On Approximating K-MPE of Bayesian Networks Using Genetic Algorithm","authors":"N. Sriwachirawat, S. Auwatanamongkol","doi":"10.1109/ICCIS.2006.252340","DOIUrl":null,"url":null,"abstract":"This paper presents a new genetic algorithm that efficiently finds k-MPE of Bayesian networks. The algorithm is based on niching method and is designed to utilize multifractral characteristic and clustering property of Bayesian networks to improve a search toward solutions. Benchmark tests are performed to evaluate the effectiveness of the algorithm and compare its performance with other niching genetic algorithms. The results from the tests show that the new algorithm outperforms the others for both running time and accuracy","PeriodicalId":296028,"journal":{"name":"2006 IEEE Conference on Cybernetics and Intelligent Systems","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 IEEE Conference on Cybernetics and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCIS.2006.252340","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

Abstract

This paper presents a new genetic algorithm that efficiently finds k-MPE of Bayesian networks. The algorithm is based on niching method and is designed to utilize multifractral characteristic and clustering property of Bayesian networks to improve a search toward solutions. Benchmark tests are performed to evaluate the effectiveness of the algorithm and compare its performance with other niching genetic algorithms. The results from the tests show that the new algorithm outperforms the others for both running time and accuracy
用遗传算法逼近贝叶斯网络的K-MPE
本文提出了一种新的遗传算法,可以有效地找到贝叶斯网络的k-MPE。该算法基于小生境方法,旨在利用贝叶斯网络的多重分形特征和聚类特性来提高对解的搜索速度。通过基准测试来评估算法的有效性,并将其与其他小生境遗传算法的性能进行比较。测试结果表明,新算法在运行时间和精度上都优于其他算法
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信