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Comparing Random Forest and Gaussian Process Modeling in the Gp-demo Algorithm
Miha Mlakar, Tea Tušar, Bogdan Filipic
Department of Intelligent Systems, Jozef Stefan Institute and Jozef Stefan International Postgraduate School & Jamova cesta 39, SI-1000 Ljubljana, Slovenia
Abstract: In surrogate-model-based optimization, the selection of an appropriate surrogate model is very important. If solution approximations returned by a surrogate model are accurate and with narrow confidence intervals, an algorithm using this surrogate model needs less exact solution evaluations to obtain results comparable to an algorithm without surrogate models. In this paper we compare two well known modeling techniques, random forest (RF) and Gaussian process (GP) modeling. The comparison includes the approximation accuracy and confidence in the approximations (expressed as the confidence interval width). The results show that GP outperforms RF and that it is more suitable for use in a surrogatemodel- based multiobjective evolutionary algorithm.
Keywords: Random Forest Modeling, Gaussian Process Modeling Comparing Random Forest and Gaussian Process Modeling in the Gp-demo Algorithm
DOI:https://doi.org/10.6025/isej/2019/6/1/9-14
Full_Text   PDF 166 KB   Download:   30  times
References:
[1] Breiman, L. (2001). Random forests. Machine Learning, 45 (1) 5–32.
[2] Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, New York.
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[5] Mlakar, M., Tušar, T., Filipic, B. (2014). Comparing solutions under uncertainty in multiobjective optimization. Mathematical Problems in Engineering, 2014, doi: 10.1155/2014/817964.
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[7] Rasmussen, C. E., Williams, C. (2006). Gaussian Processes for Machine Learning. MIT Press, Cambridge, MA.


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