Követés
samineh bagheri
Cím
Hivatkozott rá
Hivatkozott rá
Év
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
562017
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
362017
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
252015
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
252014
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
242014
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
172016
Equality constraint handling for surrogate-assisted constrained optimization
S Bagheri, W Konen, T Back
2016 IEEE Congress on Evolutionary Computation (CEC), 1924-1931, 2016
132016
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
112014
Comparing Kriging and Radial Basis Function Surrogates
S Bagheri, T Konen, Wolfgang, Bäck
Proceedings 27. Workshop Computational Intelligence, 243-259, 2017
102017
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
102015
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
72015
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
42018
Final adaptation reinforcement learning for N-player games
W Konen, S Bagheri
arXiv preprint arXiv:2111.14375, 2021
32021
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
22020
SACOBRA with Online Whitening for Solving Optimization Problems with High Conditioning
S Bagheri, W Konen, T Bäck
arXiv preprint arXiv:1904.08397, 2019
22019
Self-adjusting surrogate-assisted optimization techniques for expensive constrained black box problems
S Bagheri
Leiden University, 2020
12020
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
12019
Efficient Surrogate Assisted Optimization for Constrained Black-Box Problems
S Bagheri
Master thesis, Leiden University, 2015
12015
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
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Cikkek 1–20