| Multi-objective Bayesian global optimization using expected hypervolume improvement gradient K Yang, M Emmerich, A Deutz, T Bäck Swarm and evolutionary computation 44, 945-956, 2019 | 107 | 2019 |
| A multicriteria generalization of Bayesian global optimization M Emmerich, K Yang, A Deutz, H Wang, CM Fonseca Advances in stochastic and deterministic global optimization, 229-242, 2016 | 74 | 2016 |
| Efficient computation of expected hypervolume improvement using box decomposition algorithms K Yang, M Emmerich, A Deutz, T Bäck Journal of Global Optimization 75, 3-34, 2019 | 68 | 2019 |
| Faster exact algorithms for computing expected hypervolume improvement I Hupkens, A Deutz, K Yang, M Emmerich international conference on evolutionary multi-criterion optimization, 65-79, 2015 | 62 | 2015 |
| A review: Prognostics and health management in automotive and aerospace DV Nguyen, M Kefalas, K Yang, A Apostolidis, M Olhofer, S Limmer, ... International Journal of Prognostics and Health Management 10 (2), 35, 2019 | 47 | 2019 |
| Computing 3-D expected hypervolume improvement and related integrals in asymptotically optimal time K Yang, M Emmerich, A Deutz, CM Fonseca International Conference on Evolutionary Multi-Criterion Optimization, 685-700, 2017 | 43 | 2017 |
| Preference-based multiobjective optimization using truncated expected hypervolume improvement K Yang, L Li, A Deutz, T Back, M Emmerich 2016 12th International Conference on Natural Computation, Fuzzy Systems and …, 2016 | 35 | 2016 |
| Truncated expected hypervolume improvement: Exact computation and application K Yang, A Deutz, Z Yang, T Back, M Emmerich 2016 IEEE Congress on Evolutionary Computation (CEC), 4350-4357, 2016 | 31 | 2016 |
| A multi-point mechanism of expected hypervolume improvement for parallel multi-objective bayesian global optimization K Yang, PS Palar, M Emmerich, K Shimoyama, T Bäck Proceedings of the Genetic and Evolutionary Computation Conference, 656-663, 2019 | 25 | 2019 |
| Expected Hypervolume Improvement Algorithm for PID Controller Tuning and the Multiobjective Dynamical Control of a Biogas Plant K Yang, D Gaida, T Bäck, M Emmerich Evolutionary Computation (CEC), 2015 IEEE Congress on, 1934 - 1942, 2015 | 24 | 2015 |
| Infill criteria for multiobjective Bayesian optimization MTM Emmerich, K Yang, AH Deutz High-performance simulation-based optimization, 3-16, 2020 | 17 | 2020 |
| A new approach to target region based multiobjective evolutionary algorithms Y Wang, L Li, K Yang, MTM Emmerich 2017 IEEE Congress on Evolutionary Computation (CEC), 1757-1764, 2017 | 16 | 2017 |
| Deep non-crossing probabilistic wind speed forecasting with multi-scale features R Zou, M Song, Y Wang, J Wang, K Yang, M Affenzeller Energy Conversion and Management 257, 115433, 2022 | 11 | 2022 |
| SMS-EMOA with multiple dynamic reference points Z Yang, H Wang, K Yang, T Back, M Emmerich Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2016 …, 2016 | 11 | 2016 |
| Towards single-and multiobjective Bayesian global optimization for mixed integer problems K Yang, K van der Blom, T Bäck, M Emmerich AIP Conference Proceedings 2070 (1), 2019 | 10 | 2019 |
| Multi-objective aerodynamic design with user preference using truncated expected hypervolume improvement PS Palar, K Yang, K Shimoyama, M Emmerich, T Bäck Proceedings of the Genetic and Evolutionary Computation Conference, 1333-1340, 2018 | 9 | 2018 |
| Power distribution network reconfiguration by evolutionary integer programming K Yang, MTM Emmerich, R Li, J Wang, T Bäck Parallel Problem Solving from Nature–PPSN XIII: 13th International …, 2014 | 9 | 2014 |
| Modeling and prediction of remaining useful lifetime for maintenance scheduling optimization of a car fleet D Van Nguyen, S Limmer, K Yang, M Olhofer, T Bäck International Journal of Performability Engineering 15 (9), 2318, 2019 | 8 | 2019 |
| Automating penetration testing using reinforcement learning S Niculae, D Dichiu, K Yang, T Bäck Experimental Research Unit Bitdefender, 2020 | 7 | 2020 |
| Multi-Objective Bayesian Global Optimization using expected hypervolume improvement gradient. Swarm and Evolutionary Computation 44 (feb 2019), 945–956 K Yang, M Emmerich, A Deutz, T Bäck | 7 | 2019 |
Michael T.M. Emmerich (Dr. rer. nat.)Lead AI Scientist Silo.ai & Jyväskylä University, Finland & LIACS Leiden Univ. NLVerified email at liacs.nl
