An Efficient Cooperative Co-Evolutionary Gene Expression Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Cheng:2018:SmartWorld,

• author = "Tiantian Cheng and Jinghui Zhong",
• booktitle = "2018 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)",
• title = "An Efficient Cooperative Co-Evolutionary Gene Expression Programming",
• year = "2018",
• pages = "1422--1427",
• abstract = "Gene Expression Programming (GEP) is a popular and powerful evolutionary optimization technique for automatic generation of computer programs. In this paper, a Cooperative Co-evolutionary framework is proposed to improve the performance of GEP. The proposed framework consists of three components to find high-quality computer programs. One component focusing on searches for both structures and coefficients of computer programs, while the other two components focus on optimizing the structures and coefficients, respectively. The three components are working cooperatively during the evolution process. The proposed framework is tested on twelve symbolic regression problems and two real-world regression problems. Experimental results demonstrated that the proposed method can offer enhanced performances over two state-of-the-art algorithms in terms of solution accuracy and search efficiency.",
• keywords = "genetic algorithms, genetic programming, Gene Expression Programming, GEP, high-quality computer programs, coevolutionary gene expression, coevolutionary framework, evolutionary optimization technique, Cooperative Co-evolution",
• DOI = "doi:10.1109/SmartWorld.2018.00246",
• month = oct,
• notes = "Also known as \cite{8560224}",

}

Genetic Programming entries for Tiantian Cheng Jinghui Zhong

Citations