Abstract
In this paper we compare different evolutionary algorithm approaches and parameters used to optimize the output of neural network committee trained on regression problems. This is especially useful for large and complex datasets. We used the methodology presented in this paper to optimize the output of the committee to predict the temperature in the electric arc furnace in one of the steelworks.
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Kordos, M., Blachnik, M., Wieczorek, T. (2011). Evolutionary Optimization of Regression Model Ensembles in Steel-Making Process. In: Yin, H., Wang, W., Rayward-Smith, V. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2011. IDEAL 2011. Lecture Notes in Computer Science, vol 6936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23878-9_44
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DOI: https://doi.org/10.1007/978-3-642-23878-9_44
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