Sander van Rijn
Title
Cited by
Cited by
Year
IOHprofiler: A benchmarking and profiling tool for iterative optimization heuristics
C Doerr, H Wang, F Ye, S van Rijn, T Bäck
arXiv preprint arXiv:1810.05281, 2018
312018
Evolving the structure of Evolution Strategies
S van Rijn, H Wang, M van Leeuwen, T Bäck
Computational Intelligence (SSCI), 2016 IEEE Symposium Series on, 1-8, 2016
282016
Algorithm configuration data mining for CMA evolution strategies
S van Rijn, H Wang, B van Stein, T Bäck
Proceedings of the Genetic and Evolutionary Computation Conference, 737-744, 2017
182017
Online selection of CMA-ES variants
D Vermetten, S van Rijn, T Bäck, C Doerr
Proceedings of the Genetic and Evolutionary Computation Conference, 951-959, 2019
152019
Towards a theory-guided benchmarking suite for discrete black-box optimization heuristics: profiling (1+ λ) EA variants on onemax and leadingones
C Doerr, F Ye, S van Rijn, H Wang, T Bäck
Proceedings of the Genetic and Evolutionary Computation Conference, 951-958, 2018
142018
Optimizing highly constrained truck loadings using a self-adaptive genetic algorithm
S van Rijn, M Emmerich, E Reehuis, T Bäck
2015 IEEE Congress on Evolutionary Computation (CEC), 227-234, 2015
142015
Towards an Adaptive CMA-ES Configurator
S van Rijn, C Doerr, T Bäck
International Conference on Parallel Problem Solving from Nature, 54-65, 2018
92018
Multi-fidelity surrogate model approach to optimization
S van Rijn, S Schmitt, M Olhofer, M van Leeuwen, T Bäck
Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2018
22018
MF2: A Collection of Multi-Fidelity Benchmark Functions in Python
S van Rijn, S Schmitt
Journal of Open Source Software 5 (52), 2049, 2020
12020
Finding Efficient Trade-offs in Multi-Fidelity Response Surface Modeling
S van Rijn, S Schmitt, M van Leeuwen, T Bäck
arXiv preprint arXiv:2103.03280, 2021
2021
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Articles 1–10