Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Proceedings of the European Conference on Genetic Programming
A multiagent genetic algorithm for global numerical optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An Organizational Evolutionary Algorithm for Numerical Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Optimal approximation of linear systems by a differential evolution algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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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.