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Selecting a small number of representative non-dominated solutions by a hypervolume-based solution selection approach., , , and . FUZZ-IEEE, page 1609-1614. IEEE, (2009)Effects of using two neighborhood structures on the performance of cellular evolutionary algorithms for many-objective optimization., , , and . IEEE Congress on Evolutionary Computation, page 2508-2515. IEEE, (2009)Adaptation of Scalarizing Functions in MOEA/D: An Adaptive Scalarizing Function-Based Multiobjective Evolutionary Algorithm., , , and . EMO, volume 5467 of Lecture Notes in Computer Science, page 438-452. Springer, (2009)Single-objective and multi-objective formulations of solution selection for hypervolume maximization., , , and . GECCO, page 1831-1832. ACM, (2009)Hypervolume approximation using achievement scalarizing functions for evolutionary many-objective optimization., , , and . IEEE Congress on Evolutionary Computation, page 530-537. IEEE, (2009)Implementation of cellular genetic algorithms with two neighborhood structures for single-objective and multi-objective optimization., , , and . Soft Comput., 15 (9): 1749-1767 (2011)Evolutionary Many-Objective Optimization by NSGA-II and MOEA/D with Large Populations., , , and . SMC, page 1758-1763. IEEE, (2009)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)