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Quentin RENAU
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Exploratory landscape analysis is strongly sensitive to the sampling strategy
Q Renau, C Doerr, J Dreo, B Doerr
Parallel Problem Solving from Nature–PPSN XVI: 16th International Conference …, 2020
282020
Expressiveness and robustness of landscape features
Q Renau, J Dreo, C Doerr, B Doerr
Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2019
162019
Towards explainable exploratory landscape analysis: extreme feature selection for classifying BBOB functions
Q Renau, J Dréo, C Doerr, B Doerr
Applications of Evolutionary Computation: 24th International Conference …, 2021
122021
Linear matrix factorization embeddings for single-objective optimization landscapes
T Eftimov, G Popovski, Q Renau, P Korošec, C Doerr
2020 IEEE Symposium Series on Computational Intelligence (SSCI), 775-782, 2020
92020
Applications of Evolutionary Computation: 24th International Conference, EvoApplications 2021, Held as Part of EvoStar 2021, Virtual Event, April 7–9, 2021, Proceedings
PA Castillo, JLJ Laredo
Springer Nature, 2021
22021
Landscape-Aware Selection of Metaheuristics for the Optimization of Radar Networks
Q Renau
Institut polytechnique de Paris, 2022
12022
Automated algorithm selection for radar network configuration
Q Renau, J Dreo, A Peres, Y Semet, C Doerr, B Doerr
Proceedings of the Genetic and Evolutionary Computation Conference, 1263-1271, 2022
2022
Exploratory Landscape Analysis Feature Values for the 24 Noiseless BBOB Functions
Q Renau, J Dreo, C Doerr, B Doerr
10.5281/zenodo.4449934, 2021
2021
Experimental Data Set for the study "Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy"
Q Renau, C Doerr, J Dreo, B Doerr
10.5281/zenodo.3886816, 2020
2020
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