Sander van Rijn
Beheerder ict
- Naam
- S.J. van Rijn MSc
- Telefoon
- +31 71 527 4799
- s.j.van.rijn@liacs.leidenuniv.nl
- ORCID iD
- 0000-0001-6159-041X
Beheerder ict
- Wiskunde en Natuurwetenschappen
- Leiden Inst. Advanced Computer Sciences
- Vermetten D.L., Rijn S.J. van, Bäck T.H.W. & Doerr C. (2019), Online selection of CMA-ES variants. In: Lopez-Ibanez M. (red.) Gecco '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion. New York, NY, U.S.A.: ACM. 951-959.
- Doerr C., Ye F., Rijn S.J. van, Wang H. & Bäck T. H. W. (2018), Towards a Theory-Guided Benchmarking Suite for Discrete Black-Box Optimization Heuristics: Profiling (1+λ) EA Variants on OneMax and LeadingOnes. In: Aguirre H. (red.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '18). Kyoto, Japan: ACM. 951-958.
- Doerr C., Wang H., Ye F., Rijn S.J. van & Bäck T.H.W. (2018), IOHprofiler: A Benchmarking and Profiling Tool for Iterative Optimization Heuristics.
- Rijn S.J. van, Schmitt S., Olhofer M., Leeuwen M. van & Bäck T. (2018), Multi-Fidelity Surrogate Model Approach to Optimization. In: GECCO'18 Proceedings of the Genetic and Evolutionary Computation Conference Companion.: ACM. 225-226.
- Rijn S.J. van, Doerr C. & Bäck T. (2018), Towards an Adaptive CMA-ES Configurator. In: Auger A., Fonseca C., Lourenco N., Machado P., Paquete L., Whitley D. (red.) Parallel Problem Solving from Nature - PPSN XV. PPSN 2018. nr. LNCS11101 Cham: Springer. 54-65.
- Rijn S.J. van, Wang H., Stein B. van & Bäck T.H.W. (2017), Algorithm Configuration Data Mining for CMA Evolution Strategies. In: GECCO '17 Proceedings of the Genetic and Evolutionary Computation Conference. New York: ACM. 737-744.
- Sander van Rijn, Hao Wang, Leeuwen M. van & Bäck T.H.W. (2016), Evolving the Structure of Evolution Strategies. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI).: IEEE Publishing. 1-8.
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