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Meta-learning on flowshop using fitness landscape analysis.

, , и . GECCO, стр. 925-933. ACM, (2019)

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Marie-Eléonore Marmion

Marie Eleonore Wied

Yacine Kessaci

Eléonore Möller

Eléonore Junker

 

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Exploiting Promising Sub-Sequences of Jobs to solve the No-Wait Flowshop Scheduling Problem., , и . CoRR, (2019)Multi-objective Automatic Algorithm Configuration for the Classification Problem of Imbalanced Data., , , , , и . CEC, стр. 1-8. IEEE, (2020)Improving MOEA/D with Knowledge Discovery. Application to a Bi-objective Routing Problem., , , и . EMO, том 13970 из Lecture Notes in Computer Science, стр. 462-475. Springer, (2023)Local Optima Network Sampling for Permutation Flowshop., , и . CEC, стр. 1131-1138. IEEE, (2021)A New Constructive Heuristic for the No-Wait Flowshop Scheduling Problem., , и . LION, том 10556 из Lecture Notes in Computer Science, стр. 196-209. Springer, (2017)Configuration of a Dynamic MOLS Algorithm for Bi-objective Flowshop Scheduling., , , , и . EMO, том 11411 из Lecture Notes in Computer Science, стр. 565-577. Springer, (2019)Automatic Configuration of a Multi-objective Local Search for Imbalanced Classification., , , , и . PPSN (1), том 12269 из Lecture Notes in Computer Science, стр. 65-77. Springer, (2020)Meta-learning on flowshop using fitness landscape analysis., , и . GECCO, стр. 925-933. ACM, (2019)Improving the Relevance of Artificial Instances for Curriculum-Based Course Timetabling through Feasibility Prediction., , и . GECCO Companion, стр. 203-206. ACM, (2023)Analysis of Search Landscape Samplers for Solver Performance Prediction on a University Timetabling Problem., , и . PPSN (1), том 13398 из Lecture Notes in Computer Science, стр. 548-561. Springer, (2022)