Abstract
In this paper, we propose a neuroevolution technique specifically designed for evolving LSTM networks. The proposed technique uses a grammar-based approach to evolve LSTM neural networks for time series prediction tasks, and is based on a previous technique which was designed in order to evolve CNN networks.
We use transfer learning in order to reduce the computational time of our approach. We have compared results obtained with other state of the art time series forecasting techniques on twenty time series, which contains data generated by sensors placed on a number of Iberian pigs. Results obtained confirm the effectiveness of the strategy proposed in this work.
Overall, we showcase the potential of our proposal in producing precise and efficient deep learning models for time series prediction, as well as the adaptability of transfer learning to new datasets.
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Acknowledgments
The authors would like to thank the Spanish Ministry of Science and Innovation for the support under the project PID2020-117954RB-C21 and the European Regional Development Fund and Junta de Andalucía for projects PY20-00870 and UPO-138516.
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Vellinger, A., Torres, J.F., Divina, F., Vanhoof, W. (2023). Neuroevolutionary Transfer Learning for Time Series Forecasting. In: García Bringas, P., et al. 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023). SOCO 2023. Lecture Notes in Networks and Systems, vol 749. Springer, Cham. https://doi.org/10.1007/978-3-031-42529-5_21
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DOI: https://doi.org/10.1007/978-3-031-42529-5_21
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