Hybrid Genetic Programming for Optimal Approximation of High Order and Sparse Linear Systems
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.
KeywordsOptimal Approximation Gene Expression Programming Approximate Model Differential Evolution Algorithm Sparse Linear System
Unable to display preview. Download preview PDF.
- 1.Hollabd, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Application to Biology, Control, and Artificial Intelligence, 2nd edn. MIT Press, Cambridge (1992)Google Scholar
- 4.Oltean, M.: Multi-expression Programming, Technical Report, Babes-Bolyai Univ, Romania (2006)Google Scholar