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.
This work was supported by the National Natural Science Foundations of China under Grant 60502043, 60872135, and 60602064, the Program for New Century Excellent Talents in University of China under Grant NCET-06-0857, the National High Technology Research and Development Program (“863” program) of China under Grant 2006AA01Z107, and the Natural Science Research Project of Shaanxi, China.
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Liu, J., Fu, W., Zhong, W. (2008). Hybrid Genetic Programming for Optimal Approximation of High Order and Sparse Linear Systems. In: Li, X., et al. Simulated Evolution and Learning. SEAL 2008. Lecture Notes in Computer Science, vol 5361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89694-4_47
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DOI: https://doi.org/10.1007/978-3-540-89694-4_47
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