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Multi-objective aerodynamic design with user preference using truncated expected hypervolume improvement.

, , , , and . GECCO, page 1333-1340. ACM, (2018)

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Comparison of the criteria for updating Kriging response surface models in multi-objective optimization., , , and . IEEE Congress on Evolutionary Computation, page 1-8. IEEE, (2012)Global Sensitivity Analysis in Aerodynamic Design Using Shapley Effects and Polynomial Chaos Regression., , and . IEEE Access, (2023)A new efficient and useful robust optimization approach - design for multi-objective six sigma., , and . Congress on Evolutionary Computation, page 950-957. IEEE, (2005)Effects of the number of design variables on performances in Kriging-model-based many-objective optimization., , and . CEC, page 1901-1908. IEEE, (2015)On multi-objective efficient global optimization via universal Kriging surrogate model., and . CEC, page 621-628. IEEE, (2017)Uncertainty quantification methods for evolutionary optimization under uncertainty., , and . GECCO Companion, page 1614-1622. ACM, (2020)Polynomial-chaos-kriging-assisted efficient global optimization., and . SSCI, page 1-8. IEEE, (2017)Multi-objective aerodynamic design with user preference using truncated expected hypervolume improvement., , , , and . GECCO, page 1333-1340. ACM, (2018)Aerodynamic optimization using building cube method and data mining with proper orthogonal decomposition., , , and . SCIS&ISIS, page 1882-1887. IEEE, (2012)Evolutionary algorithm with parallel evaluation strategy using constrained penalty-based boundary intersection., and . CEC, page 3702-3709. IEEE, (2016)