Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods
R.P. Prager, M. Seiler, H. Trautmann, P. Kerschke, in: G. Rudolph, A.V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, T. Tušar (Eds.), Parallel Problem Solving from Nature — PPSN XVII, Springer International Publishing, Cham, 2022, pp. 3–17.
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Conference Paper
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Author
Editor
Rudolph, Günter;
Kononova, Anna V.;
Aguirre, Hernán;
Kerschke, Pascal;
Ochoa, Gabriela;
Tušar, Tea
Abstract
In recent years, feature-based automated algorithm selection using exploratory landscape analysis has demonstrated its great potential in single-objective continuous black-box optimization. However, feature computation is problem-specific and can be costly in terms of computational resources. This paper investigates feature-free approaches that rely on state-of-the-art deep learning techniques operating on either images or point clouds. We show that point-cloud-based strategies, in particular, are highly competitive and also substantially reduce the size of the required solver portfolio. Moreover, we highlight the effect and importance of cost-sensitive learning in automated algorithm selection models.
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Proceedings Title
Parallel Problem Solving from Nature — PPSN XVII
Page
3–17
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Cite this
Prager RP, Seiler M, Trautmann H, Kerschke P. Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods. In: Rudolph G, Kononova AV, Aguirre H, Kerschke P, Ochoa G, Tušar T, eds. Parallel Problem Solving from Nature — PPSN XVII. Springer International Publishing; 2022:3–17. doi:10.1007/978-3-031-14714-2_1
Prager, R. P., Seiler, M., Trautmann, H., & Kerschke, P. (2022). Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tušar (Eds.), Parallel Problem Solving from Nature — PPSN XVII (pp. 3–17). Springer International Publishing. https://doi.org/10.1007/978-3-031-14714-2_1
@inproceedings{Prager_Seiler_Trautmann_Kerschke_2022, place={Cham}, title={Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods}, DOI={10.1007/978-3-031-14714-2_1}, booktitle={Parallel Problem Solving from Nature — PPSN XVII}, publisher={Springer International Publishing}, author={Prager, Raphael Patrick and Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}, editor={Rudolph, Günter and Kononova, Anna V. and Aguirre, Hernán and Kerschke, Pascal and Ochoa, Gabriela and Tušar, Tea}, year={2022}, pages={3–17} }
Prager, Raphael Patrick, Moritz Seiler, Heike Trautmann, and Pascal Kerschke. “Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods.” In Parallel Problem Solving from Nature — PPSN XVII, edited by Günter Rudolph, Anna V. Kononova, Hernán Aguirre, Pascal Kerschke, Gabriela Ochoa, and Tea Tušar, 3–17. Cham: Springer International Publishing, 2022. https://doi.org/10.1007/978-3-031-14714-2_1.
R. P. Prager, M. Seiler, H. Trautmann, and P. Kerschke, “Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods,” in Parallel Problem Solving from Nature — PPSN XVII, 2022, pp. 3–17, doi: 10.1007/978-3-031-14714-2_1.
Prager, Raphael Patrick, et al. “Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods.” Parallel Problem Solving from Nature — PPSN XVII, edited by Günter Rudolph et al., Springer International Publishing, 2022, pp. 3–17, doi:10.1007/978-3-031-14714-2_1.
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