Abstract
Neural tree model has been successfully applied to solving a variety of interesting problems. In most previous studies, optimization of the neural tree model was divided into two steps: first structure optimization, then parameter optimization. One major problem in the evolution of structure without parameter information was noisy fitness evaluation. In this paper, an improved breeder genetic programming algorithm is proposed to the synthesis of neural tree model. The effectiveness and performance of the method are evaluated on time series prediction problems and compared with those of related methods. Simulation results show that the proposed algorithm is a potential method with better performance and effectiveness.
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Acknowledgments
This project is carried out under the Taishan Scholar project of Shandong China, and this research is also supported by the Natural Science Foundation of China (No. 60873058), the Natural Science Foundation of Shandong Province (No. Z2007G03, No. Z2008G04), and the Science and Technology Project of Shandong Education Bureau, Shangdong Province Young Scientists Research Awards Fund (BS2009DX005).
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Qi, F., Liu, X. & Ma, Y. Synthesis of neural tree models by improved breeder genetic programming. Neural Comput & Applic 21, 515–521 (2012). https://doi.org/10.1007/s00521-010-0451-z
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DOI: https://doi.org/10.1007/s00521-010-0451-z