多目标遗传局部搜索中局部搜索初始解的选择

H. Ishibuchi, Tadsahi Yoshida, T. Murata
{"title":"多目标遗传局部搜索中局部搜索初始解的选择","authors":"H. Ishibuchi, Tadsahi Yoshida, T. Murata","doi":"10.1109/CEC.2002.1007053","DOIUrl":null,"url":null,"abstract":"In multiobjective genetic local search (MOGLS) algorithms, the local search is usually applied to all offsprings generated by genetic operations. This paper proposes an idea of selecting only good offsprings as initial solutions for the local search. Simulation results show that the proposed idea significantly improves the search ability of MOGLS algorithms.","PeriodicalId":184547,"journal":{"name":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"25","resultStr":"{\"title\":\"Selection of initial solutions for local search in multiobjective genetic local search\",\"authors\":\"H. Ishibuchi, Tadsahi Yoshida, T. Murata\",\"doi\":\"10.1109/CEC.2002.1007053\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In multiobjective genetic local search (MOGLS) algorithms, the local search is usually applied to all offsprings generated by genetic operations. This paper proposes an idea of selecting only good offsprings as initial solutions for the local search. Simulation results show that the proposed idea significantly improves the search ability of MOGLS algorithms.\",\"PeriodicalId\":184547,\"journal\":{\"name\":\"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2002-05-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"25\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEC.2002.1007053\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEC.2002.1007053","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 25

摘要

在多目标遗传局部搜索(MOGLS)算法中,局部搜索通常应用于遗传操作产生的所有子代。本文提出了一种只选择好的子代作为局部搜索初始解的思想。仿真结果表明,该方法显著提高了MOGLS算法的搜索能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Selection of initial solutions for local search in multiobjective genetic local search
In multiobjective genetic local search (MOGLS) algorithms, the local search is usually applied to all offsprings generated by genetic operations. This paper proposes an idea of selecting only good offsprings as initial solutions for the local search. Simulation results show that the proposed idea significantly improves the search ability of MOGLS 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
期刊最新文献
Development of FPGA based adaptive image enhancement filter system using genetic algorithms Intelligent predictive control of a power plant with evolutionary programming optimizer and neuro-fuzzy identifier Blocked stochastic sampling versus Estimation of Distribution Algorithms Distinguishing adaptive from non-adaptive evolution using Ashby's law of requisite variety An artificial immune network for multimodal function optimization
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1