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Arbitrarily Close Alignments in the Error Space: A Geometric Semantic Genetic Programming Approach.

, , , and . GECCO (Companion), page 99-100. ACM, (2016)

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Hypervolume Subset Selection in Two Dimensions: Formulations and Algorithms., , , , , and . Evol. Comput., 24 (3): 411-425 (2016)Greedy Hypervolume Subset Selection in Low Dimensions., , and . Evol. Comput., 24 (3): 521-544 (2016)Theory of Randomized Optimization Heuristics (Dagstuhl Reports 19431)., , , and . Dagstuhl Reports, 9 (10): 61-94 (2019)Multiobjective Optimization on a Budget (Dagstuhl Seminar 23361)., , , , and . Dagstuhl Reports, 13 (9): 1-68 (2023)Preference Articulation in Evolutionary Multiobjective Optimisation.. HIS, page 4. IEEE Computer Society, (2007)Methodology to select solutions from the pareto-optimal set: a comparative study., , and . GECCO, page 789-796. ACM, (2007)Graph partitioning through a multi-objective evolutionary algorithm: a preliminary study., , , and . GECCO, page 625-632. ACM, (2008)Identifying the structure of nonlinear dynamic systems using multiobjective genetic programming, , and . IEEE Transactions on Systems, Man and Cybernetics, Part A, 34 (4): 531--545 (July 2004)An Improved Dimension-Sweep Algorithm for the Hypervolume Indicator., , and . IEEE Congress on Evolutionary Computation, page 1157-1163. IEEE, (2006)Hypervolume Sharpe-Ratio Indicator: Formalization and First Theoretical Results., and . PPSN, volume 9921 of Lecture Notes in Computer Science, page 814-823. Springer, (2016)