| Self-adjusting parameter control for surrogate-assisted constrained optimization under limited budgets S Bagheri, W Konen, M Emmerich, T Bäck Applied Soft Computing 61, 377-393, 2017 | 56 | 2017 |
| Constraint handling in efficient global optimization S Bagheri, W Konen, R Allmendinger, J Branke, K Deb, J Fieldsend, ... Proceedings of the genetic and evolutionary computation conference, 673-680, 2017 | 36 | 2017 |
| A new repair method for constrained optimization P Koch, S Bagheri, W Konen, C Foussette, P Krause, T Bäck Proceedings of the 2015 Annual Conference on Genetic and Evolutionary …, 2015 | 25 | 2015 |
| Online adaptable learning rates for the game Connect-4 S Bagheri, M Thill, P Koch, W Konen IEEE Transactions on Computational Intelligence and AI in Games 8 (1), 33-42, 2014 | 25 | 2014 |
| Temporal difference learning with eligibility traces for the game connect four M Thill, S Bagheri, P Koch, W Konen 2014 IEEE Conference on Computational Intelligence and Games, 1-8, 2014 | 24 | 2014 |
| Online selection of surrogate models for constrained black-box optimization S Bagheri, W Konen, T Bäck 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 1-8, 2016 | 17 | 2016 |
| Equality constraint handling for surrogate-assisted constrained optimization S Bagheri, W Konen, T Back 2016 IEEE Congress on Evolutionary Computation (CEC), 1924-1931, 2016 | 13 | 2016 |
| Constrained optimization with a limited number of function evaluations P Koch, S Bagheri, C Foussette, P Krause, T Bäck, W Konen ProcEEDings 24. Workshop comPutational intElligEncE, 237, 2014 | 11 | 2014 |
| Comparing Kriging and Radial Basis Function Surrogates S Bagheri, T Konen, Wolfgang, Bäck Proceedings 27. Workshop Computational Intelligence, 243-259, 2017 | 10 | 2017 |
| Solving the G-problems in less than 500 iterations: Improved efficient constrained optimization by surrogate modeling and adaptive parameter control S Bagheri, W Konen, M Emmerich, T Bäck arXiv preprint arXiv:1512.09251, 2015 | 10 | 2015 |
| SACOBRA: Self-adjusting constrained black-box optimization with RBF S Bagheri, W Konen, C Foussette, P Krause, T Bäck, P Koch Proc. 25. Workshop Computational Intelligence, 87-96, 2015 | 7 | 2015 |
| How to solve the dilemma of margin-based equality handling methods S Bagheri, W Konen, T Bäck Proceedings of the Workshop Computational Intelligence, 257-270, 2018 | 4 | 2018 |
| Final adaptation reinforcement learning for N-player games W Konen, S Bagheri arXiv preprint arXiv:2111.14375, 2021 | 3 | 2021 |
| Reinforcement learning for n-player games: The importance of final adaptation W Konen, S Bagheri International Conference on Bioinspired Methods and Their Applications, 84-96, 2020 | 2 | 2020 |
| SACOBRA with Online Whitening for Solving Optimization Problems with High Conditioning S Bagheri, W Konen, T Bäck arXiv preprint arXiv:1904.08397, 2019 | 2 | 2019 |
| Self-adjusting surrogate-assisted optimization techniques for expensive constrained black box problems S Bagheri Leiden University, 2020 | 1 | 2020 |
| Solving optimization problems with high conditioning by means of online whitening S Bagheri, W Konen, T Bäck Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2019 | 1 | 2019 |
| Efficient Surrogate Assisted Optimization for Constrained Black-Box Problems S Bagheri Master thesis, Leiden University, 2015 | 1 | 2015 |
| Surrogate-assisted optimization for augmentation of finite element techniques S Bagheri, U Reinicke, D Anders, W Konen Journal of Computational Science 54, 101427, 2021 | | 2021 |
| Sol ing optimization problems with high conditioning b means of online whitening. 243-244. doi: 10.1145/3319619.3322008 Version: Publisher's Version License: Licensed under … S Bagheri, W Konen, T Bäck Law (mendment Ta erne) Downloaded from: https://hdl. handle. net/1887/85096, 2019 | | 2019 |
Wolfgang KonenProfessor for Computer Science & Mathematics, Cologne University of Applied SciencesE-mail megerősítve itt: th-koeln.de
