基于全流程并行遗传算法的贝叶斯网络结构学习
TP181; 为解决海量数据情况下学习贝叶斯网络(Bayesian network,BN)结构的算法性能急剧降低问题,基于Spark框架设计了一种全流程并行遗传算法用于BN结构学习(简称为SparkGA-BN).SparkGA-BN包含互信息计算并行化、遗传算子并行化和适应度评分并行化3个部分.互信息并行计算可以高效减少搜索空间;在演化前增加对种群信息与选择信息的广播来对全种群执行选择操作.选择与交叉算子共用选择信息以并行执行,从而高效演化并减少数据落盘时间.对约束和评分两阶段产生的中间数据作记忆化存储,提升数据复用率和全局执行效率.实验结果表明,所提算法在执行效率和学习准确率方面均优于对比算...
Saved in:
| Published in | 系统工程与电子技术 Vol. 46; no. 5; pp. 1703 - 1711 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
江南大学人工智能与计算机学院,江苏无锡 214122
01.05.2024
|
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | TP181; 为解决海量数据情况下学习贝叶斯网络(Bayesian network,BN)结构的算法性能急剧降低问题,基于Spark框架设计了一种全流程并行遗传算法用于BN结构学习(简称为SparkGA-BN).SparkGA-BN包含互信息计算并行化、遗传算子并行化和适应度评分并行化3个部分.互信息并行计算可以高效减少搜索空间;在演化前增加对种群信息与选择信息的广播来对全种群执行选择操作.选择与交叉算子共用选择信息以并行执行,从而高效演化并减少数据落盘时间.对约束和评分两阶段产生的中间数据作记忆化存储,提升数据复用率和全局执行效率.实验结果表明,所提算法在执行效率和学习准确率方面均优于对比算法. |
|---|---|
| AbstractList | TP181; 为解决海量数据情况下学习贝叶斯网络(Bayesian network,BN)结构的算法性能急剧降低问题,基于Spark框架设计了一种全流程并行遗传算法用于BN结构学习(简称为SparkGA-BN).SparkGA-BN包含互信息计算并行化、遗传算子并行化和适应度评分并行化3个部分.互信息并行计算可以高效减少搜索空间;在演化前增加对种群信息与选择信息的广播来对全种群执行选择操作.选择与交叉算子共用选择信息以并行执行,从而高效演化并减少数据落盘时间.对约束和评分两阶段产生的中间数据作记忆化存储,提升数据复用率和全局执行效率.实验结果表明,所提算法在执行效率和学习准确率方面均优于对比算法. |
| Abstract_FL | To solve the problem of algorithm performance degradation in Bayesian network(BN)structure learning in case of massive data,a full process parallel genetic algorithm(GA)for BN structure learning is proposed based on the Spark framework(SparkGA-BN).SparkGA-BN includes three parts:parallel calculation of mutual information,parallelization of genetic operators,and parallelization of fitness evaluation.Parallel computation of mutual information is employed to reduce the search space.Broadcasting is used to perform selection operation on the entire population by propagating population information and selection information before evolution.Selection and crossover operators share selection information to evolve efficiently and reduce disk write time.Intermediate data generated during the constraint and scoring stages are stored in memory to improve data reuse and overall execution efficiency.Experimental results show that the proposed algorithm outperforms the comparison algorithms in terms of execution efficiency and learning accuracy. |
| Author | 蔡一鸣 方伟 马力 陆恒杨 |
| AuthorAffiliation | 江南大学人工智能与计算机学院,江苏无锡 214122 |
| AuthorAffiliation_xml | – name: 江南大学人工智能与计算机学院,江苏无锡 214122 |
| Author_FL | MA Li CAI Yiming LU Hengyang FANG Wie |
| Author_FL_xml | – sequence: 1 fullname: CAI Yiming – sequence: 2 fullname: MA Li – sequence: 3 fullname: LU Hengyang – sequence: 4 fullname: FANG Wie |
| Author_xml | – sequence: 1 fullname: 蔡一鸣 – sequence: 2 fullname: 马力 – sequence: 3 fullname: 陆恒杨 – sequence: 4 fullname: 方伟 |
| BookMark | eNo9j7tKA0EYRqeIYIx5Cythx39mdvZSiQRvELBRsAuzOzMhQTbgKF66QKwURUQD4iU2kkYt1EASfZu9xLcwolgd-IrzcaZQLmpECqEZAphQBnyujmvGRJgAEIuDs4kpUBsDx5TlUP5_nkRFY2oBcMJcDq6dR_PJ_SAenCZH3fS9mXWPk35v9HDy1WzHH53suZ2-XmbXrdHbbXLWS69ess_zbHiTDS_Su1by9Bj3O9NoQosto4p_LKCNpcX10opVXlteLS2ULUOAMysknuRSKd8NFWch2FprjymmbOlL6QilPSHtABzHJ5yPi6hHpe-4gacAhBasgGZ_vXsi0iKqVuqN3e1o_FjZ36mGB_Kwbn6SgQNl7BvLlWhc |
| ClassificationCodes | TP181 |
| ContentType | Journal Article |
| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| Copyright_xml | – notice: Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| DBID | 2B. 4A8 92I 93N PSX TCJ |
| DOI | 10.12305/j.issn.1001-506X.2024.05.23 |
| DatabaseName | Wanfang Data Journals - Hong Kong WANFANG Data Centre Wanfang Data Journals 万方数据期刊 - 香港版 China Online Journals (COJ) China Online Journals (COJ) |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| DocumentTitle_FL | Full process parallel genetic algorithm for Bayesian network structure learning |
| EndPage | 1711 |
| ExternalDocumentID | xtgcydzjs202405023 |
| GrantInformation_xml | – fundername: (国家自然科学基金); (国家自然科学基金); (国家自然科学基金); (国家自然科学基金) funderid: (国家自然科学基金); (国家自然科学基金); (国家自然科学基金); (国家自然科学基金) |
| GroupedDBID | -0Y 2B. 4A8 5XA 5XJ 92E 92I 93N ABJNI ACGFS ALMA_UNASSIGNED_HOLDINGS CCEZO CUBFJ CW9 PSX TCJ TGP U1G U5S |
| ID | FETCH-LOGICAL-s1053-c18d5dee97ce53c04fff83e3e4d9dd6aef8ad4b0669155305282d967b8e00afa3 |
| ISSN | 1001-506X |
| IngestDate | Thu May 29 04:00:31 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Issue | 5 |
| Keywords | Bayesian network(BN) genetic algorithm(GA) 遗传算法 贝叶斯网络 parallel structure learning 并行结构学习 Spark 结构学习 structure learning |
| Language | Chinese |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-s1053-c18d5dee97ce53c04fff83e3e4d9dd6aef8ad4b0669155305282d967b8e00afa3 |
| PageCount | 9 |
| ParticipantIDs | wanfang_journals_xtgcydzjs202405023 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-05-01 |
| PublicationDateYYYYMMDD | 2024-05-01 |
| PublicationDate_xml | – month: 05 year: 2024 text: 2024-05-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationTitle | 系统工程与电子技术 |
| PublicationTitle_FL | Systems Engineering and Electronics |
| PublicationYear | 2024 |
| Publisher | 江南大学人工智能与计算机学院,江苏无锡 214122 |
| Publisher_xml | – name: 江南大学人工智能与计算机学院,江苏无锡 214122 |
| SSID | ssib051375074 ssib002263377 ssib001102898 ssib057620160 ssib023168126 ssib023646287 ssj0042237 |
| Score | 2.40518 |
| Snippet | TP181; 为解决海量数据情况下学习贝叶斯网络(Bayesian... |
| SourceID | wanfang |
| SourceType | Aggregation Database |
| StartPage | 1703 |
| Title | 基于全流程并行遗传算法的贝叶斯网络结构学习 |
| URI | https://d.wanfangdata.com.cn/periodical/xtgcydzjs202405023 |
| Volume | 46 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LaxRBEG7yANGD-MQ3AdMnmTgz3TPTfZKZ3VmCGC8mkFuYZ9TDCmYDmlsgnhRFRAPiI14kF_WgBpLor_AvZHfjv7CqdnZnJCJRWJrenqqu-qpmpquGfjA2nggrsyw3MpQTJYZUeWREcYpFlsQeZD4x7bM9dd2dnJFXZ53ZoeEflVlLi614Iln647qS__EqtIFfcZXsP3h20Ck0QB38CyV4GMp9-ZiHDtcNHvg8lFiqEFuUw33FQ5cHULd46OFfFeClQPPA5aHivsVVjYcaCbRH7DXum0QcUguwC64dbNHQs0SuQHJdJxEN6sfl2uV-A2mCOtckKwi4rvUrgmhCYget6tx3SZYGWdWwmOgFshSMDdIWlHEq-gOjIoygkkR0vT61iVKUj5M2UBwAGUxXRq2BFuAW3CaChoovShLQBn41AuZzHVSvaDARIUVD2dQ_oFAlCdkAAPVMqBvVzyi2LCct0o1PZrUKeKpOhgYMoKD3u4F8curABC7qgS2AQKCtS1sodJhv_eY5MEGPveiQYPg2PE4VBcim4Eik98j3umeqS7YlLbvyORg30DIck86CHIxnxSfdW9UZAzQ4WZ4pKoGO5fWGuT2DKGSlDo2iKGNiIGMCzYbb3NqiDB4GUzrvteaT--nS7QWkMh0IBYfZqA25mz3CRv361LUbZZSOQW0ly4cMQIhyObSNZ6lZZVaARxy4dplFOJaAMLfMWiBjtnGfxH4AJiHipTOV-oofYON9WJf_AoqW7TXzqDlfiTCnj7DDRWo45vee86NsaOnmMXaosmHocXal_XZrZ-tx-8F65-tyd_1he3Nj992jn8urO9_Wuh9XO5-fd1-u7H553X6y0Xnxqfv9aXf7VXf7WefNSvvD-53NtRNsphFO1yaN4ggUYwESH2EklkqdNMu0l2SOSEyZ57kSmchkqtPUjbJcRamMIW_QdACYYys71a4Xq8w0ozwSJ9lI804zO8XGYPjOXWlHbhTFUuZp5HmecPMEdxA1s1SdZhcL-HPFK25hbq9Tz-yL6iw7WD5j59hI6-5idh6C91Z8obgZfgFjgbUP |
| linkProvider | EBSCOhost |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=%E5%9F%BA%E4%BA%8E%E5%85%A8%E6%B5%81%E7%A8%8B%E5%B9%B6%E8%A1%8C%E9%81%97%E4%BC%A0%E7%AE%97%E6%B3%95%E7%9A%84%E8%B4%9D%E5%8F%B6%E6%96%AF%E7%BD%91%E7%BB%9C%E7%BB%93%E6%9E%84%E5%AD%A6%E4%B9%A0&rft.jtitle=%E7%B3%BB%E7%BB%9F%E5%B7%A5%E7%A8%8B%E4%B8%8E%E7%94%B5%E5%AD%90%E6%8A%80%E6%9C%AF&rft.au=%E8%94%A1%E4%B8%80%E9%B8%A3&rft.au=%E9%A9%AC%E5%8A%9B&rft.au=%E9%99%86%E6%81%92%E6%9D%A8&rft.au=%E6%96%B9%E4%BC%9F&rft.date=2024-05-01&rft.pub=%E6%B1%9F%E5%8D%97%E5%A4%A7%E5%AD%A6%E4%BA%BA%E5%B7%A5%E6%99%BA%E8%83%BD%E4%B8%8E%E8%AE%A1%E7%AE%97%E6%9C%BA%E5%AD%A6%E9%99%A2%2C%E6%B1%9F%E8%8B%8F%E6%97%A0%E9%94%A1+214122&rft.issn=1001-506X&rft.volume=46&rft.issue=5&rft.spage=1703&rft.epage=1711&rft_id=info:doi/10.12305%2Fj.issn.1001-506X.2024.05.23&rft.externalDocID=xtgcydzjs202405023 |
| thumbnail_s | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Fxtgcydzjs%2Fxtgcydzjs.jpg |