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Automated algorithm selection on continuous black-box problems by combining Exploratory Landscape Analysis and machine learning

P. Kerschke, and H. Trautmann. Evol. Comput., 27 (1): 99--127 (2019)

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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)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)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)To boldly show what no one has seen before: A dashboard for visualizing multi-objective landscapesL. Schäpermeier, C. Grimme, and P. Kerschke. Lecture Notes in Computer Science, Springer International Publishing, Cham, (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)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.Tools for Landscape Analysis of Optimisation Problems in Procedural Content Generation for GamesV. Volz, B. Naujoks, P. Kerschke, and T. Tušar. Applied Soft Computing, (2023)

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Automated algorithm selection on continuous black-box problems by combining Exploratory Landscape Analysis and machine learning

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Automated algorithm selection on continuous black-box problems by combining Exploratory Landscape Analysis and machine learning

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Automated algorithm selection on continuous black-box problems by combining Exploratory Landscape Analysis and machine learning