Gene Expression Programming

Baddrud Z. Laskar, Swanirbhar Majumder
{"title":"Gene Expression Programming","authors":"Baddrud Z. Laskar, Swanirbhar Majumder","doi":"10.4018/978-1-5225-2375-8.CH010","DOIUrl":null,"url":null,"abstract":"Gene expression programming (GEP) introduced by Candida Ferreira is a descendant of genetic algorithm (GA) and genetic programming (GP). It takes the advantage of both the optimization and search technique based on genetics and natural selection as GA and its programmatic Darwinian counterpart GP. It is gaining popularity because; it has to some extent eradicated the ‘cons' of both while keeping in the ‘pros'. It is still a new technique not much explored since its introduction in 2001. In this chapter both GA and GP is first discussed followed by the elaborate discussion of GEP. This is followed up by the discussion on research work done is different fields using GEP as a tool followed up by GEP architectures. Finally, here GEP has been used for detection of age from facial features as a soft computing based optimization problem using genetic operators.","PeriodicalId":345892,"journal":{"name":"Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms","volume":"136 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"40","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/978-1-5225-2375-8.CH010","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 40

Abstract

Gene expression programming (GEP) introduced by Candida Ferreira is a descendant of genetic algorithm (GA) and genetic programming (GP). It takes the advantage of both the optimization and search technique based on genetics and natural selection as GA and its programmatic Darwinian counterpart GP. It is gaining popularity because; it has to some extent eradicated the ‘cons' of both while keeping in the ‘pros'. It is still a new technique not much explored since its introduction in 2001. In this chapter both GA and GP is first discussed followed by the elaborate discussion of GEP. This is followed up by the discussion on research work done is different fields using GEP as a tool followed up by GEP architectures. Finally, here GEP has been used for detection of age from facial features as a soft computing based optimization problem using genetic operators.
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
基因表达编程
基因表达编程(Gene expression programming, GEP)是由Candida Ferreira提出的遗传算法(genetic algorithm, GA)和遗传规划(genetic programming, GP)发展而来的。它既利用了遗传算法和基于自然选择的优化和搜索技术,又利用了程序化的达尔文遗传算法。它越来越受欢迎是因为;它在一定程度上消除了两者的“缺点”,同时保留了“优点”。自2001年推出以来,这仍然是一项新技术,并没有得到多少探索。本章首先讨论遗传算法和遗传算法,然后详细讨论遗传算法。接下来是对不同领域的研究工作的讨论,使用GEP作为工具,然后是GEP架构。最后,本文将GEP作为一个基于遗传算子的软计算优化问题用于面部特征的年龄检测。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A Novel Approach for Business Process Model Matching Using Genetic Algorithms A Modified Kruskal's Algorithm to Improve Genetic Search for Open Vehicle Routing Problem Missing Value Imputation Using ANN Optimized by Genetic Algorithm Optimization of Windspeed Prediction Using an Artificial Neural Network Compared With a Genetic Programming Model The Genetic Algorithm
×
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