| Data-driven evolutionary optimization: An overview and case studies Y Jin, H Wang, T Chugh, D Guo, K Miettinen IEEE Transactions on Evolutionary Computation 23 (3), 442-458, 2018 | 500 | 2018 |
| A surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive many-objective optimization T Chugh, Y Jin, K Miettinen, J Hakanen, K Sindhya IEEE Transactions on Evolutionary Computation 22 (1), 129-142, 2016 | 496 | 2016 |
| Handling computationally expensive multiobjective optimization problems with evolutionary algorithms : A survey T Chugh, K Sindhya, J Hakanen, K Miettinen https://jyu.finna.fi/Record/jykdok.1506554, 2015 | 287* | 2015 |
| A multiple surrogate assisted decomposition-based evolutionary algorithm for expensive multi/many-objective optimization A Habib, HK Singh, T Chugh, T Ray, K Miettinen IEEE Transactions on Evolutionary Computation 23 (6), 1000-1014, 2019 | 121 | 2019 |
| A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem T Chugh, N Chakraborti, K Sindhya, Y Jin Materials and Manufacturing Processes 32 (10), 1172-1178, 2017 | 118 | 2017 |
| Surrogate-assisted evolutionary multiobjective shape optimization of an air intake ventilation system T Chugh, K Sindhya, K Miettinen, Y Jin, T Kratky, P Makkonen 2017 IEEE Congress on Evolutionary Computation (CEC), 1541-1548, 2017 | 47 | 2017 |
| On constraint handling in surrogate-assisted evolutionary many-objective optimization T Chugh, K Sindhya, K Miettinen, J Hakanen, Y Jin Parallel Problem Solving from Nature–PPSN XIV: 14th International Conference …, 2016 | 39 | 2016 |
| Scalarizing functions in Bayesian multiobjective optimization T Chugh 2020 IEEE Congress on Evolutionary Computation (CEC), 1-8, 2020 | 36 | 2020 |
| An interactive simple indicator-based evolutionary algorithm (I-SIBEA) for multiobjective optimization problems T Chugh, K Sindhya, J Hakanen, K Miettinen 8th International conference on Evolutionary Multi-Criteria Optimization, 2015 | 36 | 2015 |
| Connections of reference vectors and different types of preference information in interactive multiobjective evolutionary algorithms J Hakanen, T Chugh, K Sindhya, Y Jin, K Miettinen 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 1-8, 2016 | 34 | 2016 |
| Multi-Objective Optimization of Bulk Vinyl Acetate Polymerization with Branching KM Anitha Mogilicharla, Tinkle Chugh, Saptarshi Materials and Manufacturing Processes 29 (2), 210-217, 2014 | 34 | 2014 |
| Surrogate-assisted evolutionary optimization of large problems T Chugh, C Sun, H Wang, Y Jin High-Performance Simulation-Based Optimization, 165-187, 2020 | 30 | 2020 |
| A feature rich distance-based many-objective visualisable test problem generator JE Fieldsend, T Chugh, R Allmendinger, K Miettinen Proceedings of the Genetic and Evolutionary Computation Conference, 541-549, 2019 | 30 | 2019 |
| Surrogate-assisted evolutionary biobjective optimization for objectives with non-uniform latencies T Chugh, R Allmendinger, V Ojalehto, K Miettinen Proceedings of the genetic and evolutionary computation conference, 609-616, 2018 | 29 | 2018 |
| A visualizable test problem generator for many-objective optimization JE Fieldsend, T Chugh, R Allmendinger, K Miettinen IEEE Transactions on Evolutionary Computation 26 (1), 1-11, 2021 | 15 | 2021 |
| On dealing with uncertainties from kriging models in offline data-driven evolutionary multiobjective optimization A Mazumdar, T Chugh, K Miettinen, M López-Ibáñez Evolutionary Multi-Criterion Optimization: 10th International Conference …, 2019 | 15 | 2019 |
| Optimal management of mixed hydraulic barriers in coastal aquifers using multi-objective Bayesian optimization S Saad, AA Javadi, T Chugh, R Farmani Journal of Hydrology 612, 128021, 2022 | 12 | 2022 |
| On the impact of covariance functions in multi-objective Bayesian optimization for engineering design PS Palar, LR Zuhal, T Chugh, A Rahat AIAA Scitech 2020 Forum, 1867, 2020 | 12 | 2020 |
| Multiobjective shape design in a ventilation system with a preference-driven surrogate-assisted evolutionary algorithm T Chugh, T Kratky, K Miettinen, Y Jin, P Makonen Proceedings of the Genetic and Evolutionary Computation Conference, 1147-1155, 2019 | 11 | 2019 |
| Probabilistic selection approaches in decomposition-based evolutionary algorithms for offline data-driven multiobjective optimization A Mazumdar, T Chugh, J Hakanen, K Miettinen IEEE Transactions on Evolutionary Computation 26 (5), 1182-1191, 2022 | 10 | 2022 |
Yaochu JinChair Professor for AI, MAE FIEEE, Westlake University, China; University of Surrey, UK在 westlake.edu.cn 的电子邮件经过验证
