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Problem Features versus Algorithm Performance on Rugged Multiobjective Combinatorial Fitness Landscapes.

, , , , and . Evol. Comput., (2017)

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Decision/Objective Space Trajectory Networks for Multi-objective Combinatorial Optimisation., , , and . EvoCOP, volume 13987 of Lecture Notes in Computer Science, page 211-226. Springer, (2023)Multi-objective Local Search Based on Decomposition., , , , and . PPSN, volume 9921 of Lecture Notes in Computer Science, page 431-441. Springer, (2016)Landscape features and automated algorithm selection for multi-objective interpolated continuous optimisation problems., , , , and . GECCO, page 421-429. ACM, (2021)Shake Them All! - Rethinking Selection and Replacement in MOEA/D., , , and . PPSN, volume 8672 of Lecture Notes in Computer Science, page 641-651. Springer, (2014)A model of anytime algorithm performance for bi-objective optimization., , and . J. Glob. Optim., 79 (2): 329-350 (2021)An Ensemble Indicator-Based Density Estimator for Evolutionary Multi-objective Optimization., , and . PPSN (2), volume 12270 of Lecture Notes in Computer Science, page 201-214. Springer, (2020)On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D., , , , and . EvoCOP, volume 12102 of Lecture Notes in Computer Science, page 131-147. Springer, (2020)Designing parallelism in surrogate-assisted multiobjective optimization based on decomposition., , , , , and . GECCO, page 462-470. ACM, (2020)Designing Helper Objectives in Multi-Objectivization., , , and . CEC, page 1-8. IEEE, (2024)On the Effects of Smoothing Rugged Landscape by Different Toy Problems: A Case Study on UBQP., , , , , and . CEC, page 1-8. IEEE, (2024)