A fast genetic algorithm for solving the maximum clique problem

Suqi Zhang, Jing Wang, Qing Wu, Jin Zhan
{"title":"A fast genetic algorithm for solving the maximum clique problem","authors":"Suqi Zhang, Jing Wang, Qing Wu, Jin Zhan","doi":"10.1109/ICNC.2014.6975933","DOIUrl":null,"url":null,"abstract":"Aiming at the defects of Genetic Algorithm (GA) for solving the Maximum Clique Problem (MCP) in more complicated, long-running and poor generality, a fast genetic algorithm (FGA) is proposed in this paper. A new chromosome repair method on the degree, elitist selection based on random repairing, uniform crossover and inversion mutation are adopted in the new algorithm. These components can speed up the search and effectively prevent the algorithm from trapping into the local optimum. The algorithm was tested on DIMACS benchmark graphs. Experimental results show that FGA has better performance and high generality.","PeriodicalId":208779,"journal":{"name":"2014 10th International Conference on Natural Computation (ICNC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 10th International Conference on Natural Computation (ICNC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNC.2014.6975933","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

Abstract

Aiming at the defects of Genetic Algorithm (GA) for solving the Maximum Clique Problem (MCP) in more complicated, long-running and poor generality, a fast genetic algorithm (FGA) is proposed in this paper. A new chromosome repair method on the degree, elitist selection based on random repairing, uniform crossover and inversion mutation are adopted in the new algorithm. These components can speed up the search and effectively prevent the algorithm from trapping into the local optimum. The algorithm was tested on DIMACS benchmark graphs. Experimental results show that FGA has better performance and high generality.
求解最大团问题的快速遗传算法
针对遗传算法求解最大团问题(MCP)复杂、耗时长、通用性差的缺点,提出了一种快速遗传算法。该算法采用了一种新的基于程度的染色体修复方法、基于随机修复的精英选择、均匀交叉和反转突变。这些成分可以加快搜索速度,有效地防止算法陷入局部最优。在DIMACS基准图上对算法进行了测试。实验结果表明,该算法具有较好的性能和较高的通用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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