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}
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.