Abstract
In the last years, Bayesian optimization (BO) has emerged as a practical tool for high-quality parameter selection in prediction systems. BO methods are useful for optimizing black-box objective functions that either lack an analytical expression, or are very expensive to evaluate. In this paper we show how BO can be used to obtain optimal parameters of a prediction system for a problem of wave energy flux prediction. Specifically, we propose the Bayesian optimization of a hybrid Grouping Genetic Algorithm with an Extreme Learning Machine (GGA-ELM) approach. The system uses data from neighbor stations (usually buoys) in order to predict the wave energy at a goal marine energy facility. The proposed BO methodology has been tested in a real problem involving buoys data in the Western coast of the USA, improving the performance of the GGA-ELM without a BO approach.
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Acknowledgements
This work has been partially supported by Comunidad de Madrid, under projects number S2013/ICE-2933 and S2013/ICE-2845, and by National projects TIN2014-54583-C2-2-R, TIN2013-42351-P and TIN2016-76406-P of the Spanish Ministerial Commission of Science and Technology (MICYT). We acknowledge support by DAMA network TIN2015-70308-REDT. We acknowledge the use of the facilities of Centro de Computación Científica de la UAM.
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Cornejo-Bueno, L., Garrido-Merchán, E.C., Hernández-Lobato, D., Salcedo-Sanz, S. (2017). Bayesian Optimization of a Hybrid Prediction System for Optimal Wave Energy Estimation Problems. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10305. Springer, Cham. https://doi.org/10.1007/978-3-319-59153-7_56
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