New Two-Parametric Mutation Operator for Inductive Modelling Using Combinatorial-Genetic Algorithm

O. Moroz, V. Stepashko
{"title":"New Two-Parametric Mutation Operator for Inductive Modelling Using Combinatorial-Genetic Algorithm","authors":"O. Moroz, V. Stepashko","doi":"10.1109/ACIT54803.2022.9913199","DOIUrl":null,"url":null,"abstract":"Usually genetic algorithm (GA) uses crossover and mutation operator for solving optimization problems. But recent studies show that sometimes GA can be just as efficient using only one of these operators. In this paper, two-parametric mutation operator is proposed to generate models in combinatorial-genetic algorithm (COMBI-GA) when solving inductive modelling tasks. The algorithm efficiency is compared when using a pair of a crossover and a(0→1)-bit mutation or only two-parametrical (0↔1)-bit mutation when solving a real-world problem.","PeriodicalId":431250,"journal":{"name":"2022 12th International Conference on Advanced Computer Information Technologies (ACIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 12th International Conference on Advanced Computer Information Technologies (ACIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACIT54803.2022.9913199","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Usually genetic algorithm (GA) uses crossover and mutation operator for solving optimization problems. But recent studies show that sometimes GA can be just as efficient using only one of these operators. In this paper, two-parametric mutation operator is proposed to generate models in combinatorial-genetic algorithm (COMBI-GA) when solving inductive modelling tasks. The algorithm efficiency is compared when using a pair of a crossover and a(0→1)-bit mutation or only two-parametrical (0↔1)-bit mutation when solving a real-world problem.
用组合遗传算法进行归纳建模的新双参数变异算子
遗传算法通常使用交叉和变异算子来求解优化问题。但最近的研究表明,有时仅使用这些算子中的一个,遗传算法也可以同样有效。本文在求解归纳建模任务时,提出了组合遗传算法(COMBI-GA)中的双参数变异算子来生成模型。在解决实际问题时,比较使用一对交叉和(0→1)位突变或仅使用两参数(0→1)位突变的算法效率。
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:604180095
Book学术官方微信