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Use of Piecewise Linear and Nonlinear Scalarizing Functions in MOEA/D., , and . PPSN, volume 9921 of Lecture Notes in Computer Science, page 503-513. Springer, (2016)Comparison of Hypervolume, IGD and IGD+ from the Viewpoint of Optimal Distributions of Solutions., , , and . EMO, volume 11411 of Lecture Notes in Computer Science, page 332-345. Springer, (2019)Fuzzy Ensemble Design through Multi-Objective Fuzzy Rule Selection., and . Multi-Objective Machine Learning, volume 16 of Studies in Computational Intelligence, Springer, (2006)Multiobjective genetic rule selection as a data mining postprocessing procedure., , and . GECCO, page 1591-1592. ACM, (2006)Indicator-based evolutionary algorithm with hypervolume approximation by achievement scalarizing functions., , , and . GECCO, page 527-534. ACM, (2010)Simultaneous use of different scalarizing functions in MOEA/D., , , and . GECCO, page 519-526. ACM, (2010)Topological Kernel Bayesian ARTMAP., , , , and . WAC, page 1-5. IEEE, (2018)Relation between Neighborhood Size and MOEA/D Performance on Many-Objective Problems., , and . EMO, volume 7811 of Lecture Notes in Computer Science, page 459-474. Springer, (2013)Constrained multiobjective distance minimization problems., , , , and . GECCO, page 586-594. ACM, (2019)Ensemble classifier design by parallel distributed implementation of genetic fuzzy rule selection for large data sets., , and . IEEE Congress on Evolutionary Computation, page 1-8. IEEE, (2010)