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Exploiting Promising Sub-Sequences of Jobs to solve the No-Wait Flowshop Scheduling Problem., , and . CoRR, (2019)Multi-objective Automatic Algorithm Configuration for the Classification Problem of Imbalanced Data., , , , , and . CEC, page 1-8. IEEE, (2020)Improving MOEA/D with Knowledge Discovery. Application to a Bi-objective Routing Problem., , , and . EMO, volume 13970 of Lecture Notes in Computer Science, page 462-475. Springer, (2023)Local Optima Network Sampling for Permutation Flowshop., , and . CEC, page 1131-1138. IEEE, (2021)A New Constructive Heuristic for the No-Wait Flowshop Scheduling Problem., , and . LION, volume 10556 of Lecture Notes in Computer Science, page 196-209. Springer, (2017)Improving the Relevance of Artificial Instances for Curriculum-Based Course Timetabling through Feasibility Prediction., , and . GECCO Companion, page 203-206. ACM, (2023)Configuration of a Dynamic MOLS Algorithm for Bi-objective Flowshop Scheduling., , , , and . EMO, volume 11411 of Lecture Notes in Computer Science, page 565-577. Springer, (2019)Automatic Configuration of a Multi-objective Local Search for Imbalanced Classification., , , , and . PPSN (1), volume 12269 of Lecture Notes in Computer Science, page 65-77. Springer, (2020)Meta-learning on flowshop using fitness landscape analysis., , and . GECCO, page 925-933. ACM, (2019)Dynamic Learning in Hyper-Heuristics to Solve Flowshop Problems., , and . BRACIS (1), volume 13073 of Lecture Notes in Computer Science, page 155-169. Springer, (2021)