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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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Initial design strategies and their effects on sequential model-based optimizationJ. Bossek, C. Doerr, and P. Kerschke. Proceedings of the 2020 Genetic and Evolutionary Computation Conference, New York, NY, USA, ACM, (June 2020)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)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)Anytime behavior of inexact TSP solvers and perspectives for automated algorithm selectionJ. Bossek, P. Kerschke, and H. Trautmann. 2020 IEEE Congress on Evolutionary Computation (CEC), IEEE, (July 2020)A multi-objective perspective on performance assessment and automated selection of single-objective optimization algorithmsJ. Bossek, P. Kerschke, and H. Trautmann. Appl. Soft Comput., 88 (105901): 105901 (March 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)Evolving sampling strategies for one-shot optimization tasksJ. Bossek, C. Doerr, P. Kerschke, A. Neumann, and F. Neumann. Parallel Problem Solving from Nature -- PPSN XVI, Springer International Publishing, Cham, (2020)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)OpenML: An R package to connect to the machine learning platform OpenMLG. Casalicchio, J. Bossek, M. Lang, D. Kirchhoff, P. Kerschke, B. Hofner, H. Seibold, J. Vanschoren, and B. Bischl. Comput. Stat., 34 (3): 977--991 (September 2019)The node weight dependent traveling salesperson problemJ. Bossek, K. Casel, P. Kerschke, and F. Neumann. Proceedings of the 2020 Genetic and Evolutionary Computation Conference, New York, NY, USA, ACM, (June 2020)

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Disambiguation of "Bossek, Jakob"

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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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Disambiguation of "Bossek, Jakob"

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