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

  • Jing Liu
  • Wenlong Fu
  • Weicai Zhong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5361)

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.

Keywords

Optimal Approximation Gene Expression Programming Approximate Model Differential Evolution Algorithm Sparse Linear System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Jing Liu
    • 1
  • Wenlong Fu
    • 1
  • Weicai Zhong
    • 1
  1. 1.Institute of Intelligent Information ProcessingXidian University Email: neouma@163.comXi’anChina

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