QoS组播路由的可学习遗传算法

Feng Xiao-Jun, Liu Fang
{"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}
引用次数: 0

摘要

通过对传统遗传算法的改进,提出了一种机器学习与遗传算法相结合的可学习遗传算法。该算法的核心思想是,它通过假设生成和实例化的过程产生新的个体,而不是像传统的遗传算法那样通过突变和/或重组。然后将该算法应用于带宽延迟受限的最小代价组播路由问题。该算法具有简单、高效的特点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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