Paper Info
Title
Hybrid Genetic Programming for Optimal Approximation of High Order and Sparse Linear Systems
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.
Year
DOI
Venue
2008
10.1007/978-3-540-89694-4_47
SEAL
Keywords
Field
DocType
sparse linear system,optimal approximation,hybrid genetic programming,gp operator,good performance,linear system,sparse linear systems,intrinsic property,high order,linear system approximation problem
Gene expression programming,Intrinsic and extrinsic properties (philosophy),Mathematical optimization,Linear system,Computer science,Sparse approximation,Genetic programming,Operator (computer programming),Artificial intelligence,Machine learning,Differential evolution algorithm
Conference
Volume
ISSN
Citations 
5361
0302-9743
0
PageRank 
References 
Authors
0.34
6
3
Name
Order
Citations
PageRank
Jing Liu11043115.54
Wenlong Fu211013.14
Weicai Zhong338126.14