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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)Evolutionary algorithm with parallel evaluation strategy using constrained penalty-based boundary intersection., and . CEC, page 3702-3709. IEEE, (2016)Exploiting gradient for kriging-based multi-objective aerodynamic optimization., and . SSCI, page 1-8. IEEE, (2017)Exploiting active subspaces in global optimization: how complex is your problem?, and . GECCO (Companion), page 1487-1494. ACM, (2017)On the use of metaheuristics in hyperparameters optimization of gaussian processes., , and . GECCO (Companion), page 263-264. ACM, (2019)Combined kriging surrogate model for efficient global optimization using the optimal weighting method., and . GECCO Companion, page 29-30. ACM, (2020)Kriging-surrogate-based optimization considering expected hypervolume improvement in non-constrained many-objective test problems., , and . IEEE Congress on Evolutionary Computation, page 658-665. IEEE, (2013)Multiple Metamodels for Robustness Estimation in Multi-objective Robust Optimization., and . EMO, volume 10173 of Lecture Notes in Computer Science, page 469-483. Springer, (2017)A new efficient and useful robust optimization approach - design for multi-objective six sigma., , and . Congress on Evolutionary Computation, page 950-957. IEEE, (2005)Global Sensitivity Analysis in Aerodynamic Design Using Shapley Effects and Polynomial Chaos Regression., , and . IEEE Access, (2023)