Hybrid Genetic Programming for Optimal Approximation of High Order and Sparse Linear Systems

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{DBLP:conf/seal/LiuFZ08,

• author = "Jing Liu and Wenlong Fu and Weicai Zhong",
• title = "Hybrid Genetic Programming for Optimal Approximation of High Order and Sparse Linear Systems",
• booktitle = "Proceedings of the 7th International Conference on Simulated Evolution And Learning (SEAL '08)",
• year = "2008",
• editor = "Xiaodong Li and Michael Kirley and Mengjie Zhang and David G. Green and Victor Ciesielski and Hussein A. Abbass and Zbigniew Michalewicz and Tim Hendtlass and Kalyanmoy Deb and Kay Chen Tan and J{\"u}rgen Branke and Yuhui Shi",
• volume = "5361",
• series = "Lecture Notes in Computer Science",
• pages = "462--472",
• address = "Melbourne, Australia",
• month = dec # " 7-10",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-540-89693-7",
• DOI = "doi:10.1007/978-3-540-89694-4_47",
• abstract = "A Hybrid Genetic Programming (HGP) algorithm is proposed for optimal approximation of high order and sparse linear systems. With the intrinsic property of linear systems in mind, an individual in HGP is designed as an organization that consists of two cells. The nodes of the cells include a function and a terminal. All GP operators are designed based on organizations. In the experiments, three kinds of linear system approximation problems, namely stable, unstable, and high order and sparse linear systems, are used to test the performance of HGP. The experimental results show that HGP obtained a good performance in solving high order and sparse linear systems.",
• bibsource = "DBLP, http://dblp.uni-trier.de",
• notes = "Institute of Intelligent Information Processing, Xidian University Xi'an, China",

}

Genetic Programming entries for Jing Liu Wenlong Fu Weicai Zhong

Citations