一个启发生物的遗传算法,用于社区采矿

Yitong Lu, Mingxin Liang, Chao Gao, Yuxin Liu, Xianghua Li
{"title":"一个启发生物的遗传算法,用于社区采矿","authors":"Yitong Lu, Mingxin Liang, Chao Gao, Yuxin Liu, Xianghua Li","doi":"10.1109/FSKD.2016.7603255","DOIUrl":null,"url":null,"abstract":"The community structure as a vital property for complex networks contributes a lot for understanding and detecting inherent functions of real networks. However, existing algorithms which are ranging from the optimization-based to model-based strategies still need to be strengthened further in terms of their robustness and accuracy. In this paper, a kind of multi-headed slime molds, Physarum, is used for optimizing genetic algorithm (GA), due to its intelligence of generating foraging networks based on bioresearches. Thus, a Physarum-based Network Model (PNM) is proposed based on the Physarum-based Model, which shows an ability of recognizing inter-community edges. Combining PNM with a genetic algorithm, a novel genetic algorithm, called PNGACD, is putting forward to enhance the GA's efficiency, in which a priori edge recognition of PNM is integrated into the phase of initialization. Moreover, experiments in six real-world networks are used to evaluate the efficiency of the proposed method. Results show that there is a remarkable improvement in term of the robustness and accuracy, which demonstrates that PNGACD has a better performance, compared with the existing algorithms.","PeriodicalId":373155,"journal":{"name":"2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A bio-inspired genetic algorithm for community mining\",\"authors\":\"Yitong Lu, Mingxin Liang, Chao Gao, Yuxin Liu, Xianghua Li\",\"doi\":\"10.1109/FSKD.2016.7603255\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The community structure as a vital property for complex networks contributes a lot for understanding and detecting inherent functions of real networks. However, existing algorithms which are ranging from the optimization-based to model-based strategies still need to be strengthened further in terms of their robustness and accuracy. In this paper, a kind of multi-headed slime molds, Physarum, is used for optimizing genetic algorithm (GA), due to its intelligence of generating foraging networks based on bioresearches. Thus, a Physarum-based Network Model (PNM) is proposed based on the Physarum-based Model, which shows an ability of recognizing inter-community edges. Combining PNM with a genetic algorithm, a novel genetic algorithm, called PNGACD, is putting forward to enhance the GA's efficiency, in which a priori edge recognition of PNM is integrated into the phase of initialization. Moreover, experiments in six real-world networks are used to evaluate the efficiency of the proposed method. Results show that there is a remarkable improvement in term of the robustness and accuracy, which demonstrates that PNGACD has a better performance, compared with the existing algorithms.\",\"PeriodicalId\":373155,\"journal\":{\"name\":\"2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/FSKD.2016.7603255\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FSKD.2016.7603255","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

社区结构作为复杂网络的一个重要属性,对理解和检测真实网络的内在功能有很大的帮助。然而,现有的算法从基于优化的策略到基于模型的策略,在鲁棒性和准确性方面还需要进一步加强。本文以多头黏菌绒泡菌为研究对象,利用其基于生物研究生成觅食网络的智能,对遗传算法进行优化。在此基础上,提出了一种基于绒泡菌的网络模型(PNM),该模型具有识别群落间边缘的能力。为了提高遗传算法的效率,将PNM与遗传算法相结合,提出了一种新的遗传算法PNGACD,该算法将PNM的先验边缘识别集成到初始化阶段。此外,在六个真实网络中进行了实验,以评估所提出方法的有效性。结果表明,与现有算法相比,PNGACD具有更好的鲁棒性和准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A bio-inspired genetic algorithm for community mining
The community structure as a vital property for complex networks contributes a lot for understanding and detecting inherent functions of real networks. However, existing algorithms which are ranging from the optimization-based to model-based strategies still need to be strengthened further in terms of their robustness and accuracy. In this paper, a kind of multi-headed slime molds, Physarum, is used for optimizing genetic algorithm (GA), due to its intelligence of generating foraging networks based on bioresearches. Thus, a Physarum-based Network Model (PNM) is proposed based on the Physarum-based Model, which shows an ability of recognizing inter-community edges. Combining PNM with a genetic algorithm, a novel genetic algorithm, called PNGACD, is putting forward to enhance the GA's efficiency, in which a priori edge recognition of PNM is integrated into the phase of initialization. Moreover, experiments in six real-world networks are used to evaluate the efficiency of the proposed method. Results show that there is a remarkable improvement in term of the robustness and accuracy, which demonstrates that PNGACD has a better performance, compared with the existing algorithms.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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