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MOEA/D-HH: A Hyper-Heuristic for Multi-objective Problems., , , and . EMO (1), volume 9018 of Lecture Notes in Computer Science, page 94-108. Springer, (2015)Decomposition Based Multiobjective Hyper Heuristic with Differential Evolution., , , and . ICCCI (2), volume 9330 of Lecture Notes in Computer Science, page 129-138. Springer, (2015)Improved Cultural Immune Systems to solve the economic load dispatch problems., , , , and . IEEE Congress on Evolutionary Computation, page 621-628. IEEE, (2013)Multi-armed Bandit Based Hyper-Heuristics for the Permutation Flow Shop Problem., , , , and . BRACIS, page 139-144. IEEE Computer Society, (2018)ELMOEA/D-DE: Extreme Learning Surrogate Models in Multi-objective Optimization Based on Decomposition and Differential Evolution., , , , and . BRACIS, page 318-323. IEEE Computer Society, (2014)Warping search: a new metaheuristic applied to the protein structure prediction., , , and . GECCO, page 349-350. ACM, (2008)ADEMO/D: An adaptive differential evolution for protein structure prediction problem., , , and . Expert Syst. Appl., (2016)Extreme Learning Surrogate Models in Multi-objective Optimization based on Decomposition., , , , and . Neurocomputing, (2016)Adaptive Operator Selection for Many-Objective Optimization with NSGA-III., , , , , and . EMO, volume 10173 of Lecture Notes in Computer Science, page 267-281. Springer, (2017)Upper Confidence Bound (UCB) Algorithms for Adaptive Operator Selection in MOEA/D., , and . EMO (1), volume 9018 of Lecture Notes in Computer Science, page 411-425. Springer, (2015)