{"title":"网络优化的遗传算法","authors":"W. Kosinski, D. Mikolajewski","doi":"10.1109/CASoN.2009.19","DOIUrl":null,"url":null,"abstract":"Whereas multicast transmission in one-to-many communications allows the operator to save drastically network resources, it also makes the routing of the traffic flows more complex than in unicast transmissions. The use of the genetic algorithms (GA) is presented, which can considerably reduce the number of solutions to be evaluated and helps to find the appropriate combination of the trees to comply with the bandwidth needs of the group of point-to-point links and then of the group of multicast sessions.","PeriodicalId":425748,"journal":{"name":"2009 International Conference on Computational Aspects of Social Networks","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Genetic Algorithms for Network Optimization\",\"authors\":\"W. Kosinski, D. Mikolajewski\",\"doi\":\"10.1109/CASoN.2009.19\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Whereas multicast transmission in one-to-many communications allows the operator to save drastically network resources, it also makes the routing of the traffic flows more complex than in unicast transmissions. The use of the genetic algorithms (GA) is presented, which can considerably reduce the number of solutions to be evaluated and helps to find the appropriate combination of the trees to comply with the bandwidth needs of the group of point-to-point links and then of the group of multicast sessions.\",\"PeriodicalId\":425748,\"journal\":{\"name\":\"2009 International Conference on Computational Aspects of Social Networks\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-06-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Conference on Computational Aspects of Social Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CASoN.2009.19\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Conference on Computational Aspects of Social Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CASoN.2009.19","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Whereas multicast transmission in one-to-many communications allows the operator to save drastically network resources, it also makes the routing of the traffic flows more complex than in unicast transmissions. The use of the genetic algorithms (GA) is presented, which can considerably reduce the number of solutions to be evaluated and helps to find the appropriate combination of the trees to comply with the bandwidth needs of the group of point-to-point links and then of the group of multicast sessions.
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