Analysis of Search Landscape Samplers for Solver Performance Prediction on a University Timetabling Problem

Author:

Feutrier ThomasORCID,Kessaci Marie-ÉléonoreORCID,Veerapen NadarajenORCID

Publisher

Springer International Publishing

Reference22 articles.

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2. Daolio, F., Liefooghe, A., Verel, S., Aguirre, H., Tanaka, K.: Problem features vs. algorithm performance on rugged multiobjective combinatorial fitness landscapes. Evol. Comput. 25(4), 555–585 (2017). https://doi.org/10.1162/EVCO_a_00193

3. Feutrier, T., Kessaci, M.E., Veerapen, N.: Exploiting landscape features for fitness prediction in university timetabling. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2022. Association for Computing Machinery, New York (2022, [accepted as poster paper])

4. Jankovic, A., Doerr, C.: Landscape-aware fixed-budget performance regression and algorithm selection for modular cma-es variants. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference, GECCO 2020, pp. 841–849. Association for Computing Machinery, New York (2020). https://doi.org/10.1145/3377930.3390183

5. Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, IJCAI 1995, vol. 2, pp. 1137–1143. Morgan Kaufmann Publishers Inc., San Francisco (1995)

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1. Improving the Relevance of Artificial Instances for Curriculum-Based Course Timetabling through Feasibility Prediction;Proceedings of the Companion Conference on Genetic and Evolutionary Computation;2023-07-15

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