Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning
B. Bischl, O. Mersmann, H. Trautmann, M. Preuß, in: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, Association for Computing Machinery, New York, NY, USA, 2012, pp. 313–320.
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Author
Bischl, Bernd;
Mersmann, Olaf;
Trautmann, HeikeLibreCat 
;
Preuß, Mike
;
Preuß, MikeAbstract
The steady supply of new optimization methods makes the algorithm selection problem (ASP) an increasingly pressing and challenging task, specially for real-world black-box optimization problems. The introduced approach considers the ASP as a cost-sensitive classification task which is based on Exploratory Landscape Analysis. Low-level features gathered by systematic sampling of the function on the feasible set are used to predict a well-performing algorithm out of a given portfolio. Example-specific label costs are defined by the expected runtime of each candidate algorithm. We use one-sided support vector regression to solve this learning problem. The approach is illustrated by means of the optimization problems and algorithms of the BBOB’09/10 workshop.
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Proceedings Title
Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation
forms.conference.field.series_title_volume.label
GECCO ’12
Page
313–320
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Bischl B, Mersmann O, Trautmann H, Preuß M. Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning. In: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation. GECCO ’12. Association for Computing Machinery; 2012:313–320. doi:10.1145/2330163.2330209
Bischl, B., Mersmann, O., Trautmann, H., & Preuß, M. (2012). Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning. Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, 313–320. https://doi.org/10.1145/2330163.2330209
@inproceedings{Bischl_Mersmann_Trautmann_Preuß_2012, place={New York, NY, USA}, series={GECCO ’12}, title={Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning}, DOI={10.1145/2330163.2330209}, booktitle={Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation}, publisher={Association for Computing Machinery}, author={Bischl, Bernd and Mersmann, Olaf and Trautmann, Heike and Preuß, Mike}, year={2012}, pages={313–320}, collection={GECCO ’12} }
Bischl, Bernd, Olaf Mersmann, Heike Trautmann, and Mike Preuß. “Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning.” In Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, 313–320. GECCO ’12. New York, NY, USA: Association for Computing Machinery, 2012. https://doi.org/10.1145/2330163.2330209.
B. Bischl, O. Mersmann, H. Trautmann, and M. Preuß, “Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning,” in Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, 2012, pp. 313–320, doi: 10.1145/2330163.2330209.
Bischl, Bernd, et al. “Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning.” Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, Association for Computing Machinery, 2012, pp. 313–320, doi:10.1145/2330163.2330209.
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