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Deep learning as a competitive feature-free approach for automated algorithm selection on the traveling salesperson problem

M. Seiler, J. Pohl, J. Bossek, P. Kerschke, and H. Trautmann. Parallel Problem Solving from Nature -- PPSN XVI, Springer International Publishing, Cham, (2020)

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On the potential of normalized TSP features for automated algorithm selectionJ. Heins, J. Bossek, J. Pohl, M. Seiler, H. Trautmann, and P. Kerschke. Proceedings of the 16th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, New York, NY, USA, ACM, (September 2021)Neural Networks as Black-Box Benchmark Functions Optimized for Exploratory Landscape Features{. Prager, K. Dietrich, L. Schneider, L. Schäpermeier, B. Bischl, P. Kerschke, H. Trautmann, and O. Mersmann. FOGA 2023, page 129--139. United States, Association for Computing Machinery, (Aug 30, 2023)Publisher Copyright: © 2023 ACM.; 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, FOGA 2023, FOGA 2023 ; Conference date: 30-08-2023 Through 01-09-2023.Multi$^3$: Optimizing multimodal single-objective continuous problems in the multi-objective space by means of multiobjectivizationP. Aspar, P. Kerschke, V. Steinhoff, H. Trautmann, and C. Grimme. Lecture Notes in Computer Science, Springer International Publishing, Cham, (2021)Deep learning as a competitive feature-free approach for automated algorithm selection on the traveling salesperson problemM. Seiler, J. Pohl, J. Bossek, P. Kerschke, and H. Trautmann. Parallel Problem Solving from Nature -- PPSN XVI, Springer International Publishing, Cham, (2020)HPO $$ ELA: Investigating hyperparameter optimization landscapes by means of exploratory landscape analysisL. Schneider, L. Schäpermeier, R. Prager, B. Bischl, H. Trautmann, and P. Kerschke. Lecture Notes in Computer Science, Springer International Publishing, Cham, (2022)BBE: Basin-based evaluation of multimodal multi-objective optimization problemsJ. Heins, J. Rook, L. Schäpermeier, P. Kerschke, J. Bossek, and H. Trautmann. Lecture Notes in Computer Science, Springer International Publishing, Cham, (2022)The objective that freed me: a multi-objective local search approach for continuous single-objective optimizationP. Aspar, V. Steinhoff, L. Schäpermeier, P. Kerschke, H. Trautmann, and C. Grimme. Nat. Comput., (September 2022)Enhancing resilience of deep learning networks by means of transferable adversariesM. Seiler, H. Trautmann, and P. Kerschke. 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, (July 2020)Multimodality in multi-objective optimization -- more boon than bane?C. Grimme, P. Kerschke, and H. Trautmann. Lecture Notes in Computer Science, Springer International Publishing, Cham, (2019)Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimizationC. Grimme, P. Kerschke, P. Aspar, H. Trautmann, M. Preuss, A. Deutz, H. Wang, and M. Emmerich. Comput. Oper. Res., 136 (105489): 105489 (December 2021)

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Deep learning as a competitive feature-free approach for automated algorithm selection on the traveling salesperson problem

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Deep learning as a competitive feature-free approach for automated algorithm selection on the traveling salesperson problem

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Deep learning as a competitive feature-free approach for automated algorithm selection on the traveling salesperson problem