{"title":"QoS组播路由的可学习遗传算法","authors":"Feng Xiao-Jun, Liu Fang","doi":"10.1109/ICOSP.2002.1180987","DOIUrl":null,"url":null,"abstract":"By improving the conventional genetic algorithm, we put forward a learnable genetic algorithm combining machine learning and genetic algorithm. The central idea of the algorithm is that it generates new individuals by processes of hypothesis generation and instantiation, rather than by mutation and/or recombination as in conventional genetic algorithms. The algorithm is then used for the bandwidth-delay-constrained least-cost multicast routing problem. The features of this new algorithm are simplicity and effectivity.","PeriodicalId":159807,"journal":{"name":"6th International Conference on Signal Processing, 2002.","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A learnable genetic algorithm for QoS multicast routing\",\"authors\":\"Feng Xiao-Jun, Liu Fang\",\"doi\":\"10.1109/ICOSP.2002.1180987\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"By improving the conventional genetic algorithm, we put forward a learnable genetic algorithm combining machine learning and genetic algorithm. The central idea of the algorithm is that it generates new individuals by processes of hypothesis generation and instantiation, rather than by mutation and/or recombination as in conventional genetic algorithms. The algorithm is then used for the bandwidth-delay-constrained least-cost multicast routing problem. The features of this new algorithm are simplicity and effectivity.\",\"PeriodicalId\":159807,\"journal\":{\"name\":\"6th International Conference on Signal Processing, 2002.\",\"volume\":\"50 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"6th International Conference on Signal Processing, 2002.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICOSP.2002.1180987\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"6th International Conference on Signal Processing, 2002.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICOSP.2002.1180987","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A learnable genetic algorithm for QoS multicast routing
By improving the conventional genetic algorithm, we put forward a learnable genetic algorithm combining machine learning and genetic algorithm. The central idea of the algorithm is that it generates new individuals by processes of hypothesis generation and instantiation, rather than by mutation and/or recombination as in conventional genetic algorithms. The algorithm is then used for the bandwidth-delay-constrained least-cost multicast routing problem. The features of this new algorithm are simplicity and effectivity.