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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)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)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)Automated algorithm selection: Survey and perspectivesP. Kerschke, H. Hoos, F. Neumann, and H. Trautmann. Evol. Comput., 27 (1): 3--45 (2019)Automated algorithm selection on continuous black-box problems by combining Exploratory Landscape Analysis and machine learningP. Kerschke, and H. Trautmann. Evol. Comput., 27 (1): 99--127 (2019)Per-instance configuration of the modularized CMA-ES by means of classifier chains and exploratory landscape analysisR. Prager, H. Trautmann, H. Wang, T. Back, and P. Kerschke. 2020 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, (December 2020)Evolving diverse TSP instances by means of novel and creative mutation operatorsJ. Bossek, P. Kerschke, A. Neumann, M. Wagner, F. Neumann, and H. Trautmann. Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, New York, NY, USA, ACM, (August 2019)

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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