Abstract
Neural networks are a powerful computational architecture for modeling data, but optimizing the connection weights can be very difficult. Flexible neural trees (FNTs) are good at finding a globally near-optimal network to fit a dataset, using evolutionary algorithms and particle swarm optimization. We show that putting the two methods together can yield very good results. The FNT solution can be embedded into a larger neural network that is then optimized using backpropagation. The combination of the two methods outperforms either method alone.
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Acknowledgments
This research was supported by the National Key Research and Development Program of China (No. 2016YFC0106000), the Youth Science and Technology Star Program of Jinan City (201406003).
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Wu, P., Orchard, J. (2017). Using Flexible Neural Trees to Seed Backpropagation. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10634. Springer, Cham. https://doi.org/10.1007/978-3-319-70087-8_12
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DOI: https://doi.org/10.1007/978-3-319-70087-8_12
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