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Alarie, S., Audet, C., Jacquot, P., & Le Digabel, S. (2022). Hierarchically constrained blackbox optimization. Operations Research Letters, 50(4), 446-451. Lien externe
Audet, C., Le Digabel, S., & Saltet, R. (2022). Quantifying uncertainty with ensembles of surrogates for blackbox optimization. Computational Optimization and Applications, 83(1), 29-66. Lien externe
Audet, C., Le Digabel, S., Rochon Montplaisir, V., & Tribes, C. (2022). Algorithm 1027: NOMAD Version 4: Nonlinear Optimization with the MADS Algorithm. ACM Transactions on Mathematical Software, 48(3), 1-22. Lien externe
Audet, C., Le Digabel, S., Salomon, L., & Tribes, C. (juin 2022). Constrained blackbox optimization with the NOMAD solver on the COCO constrained test suite [Communication écrite]. Genetic and Evolutionary Computation Conference Companion (GECCO 2022), Boston, Massachusetts, USA. Lien externe
Dzahini, K. J., Kokkolaras, M., & Le Digabel, S. (2022). Constrained stochastic blackbox optimization using a progressive barrier and probabilistic estimates. Mathematical Programming, 198(1), 675-732. Lien externe
Lakhmiri, D., & Le Digabel, S. (2022). Use of Static Surrogates in Hyperparameter Optimization. Operations Research Forum, 3(1). Lien externe
Lakhmiri, D., Alimo, R., & Le Digabel, S. (2022). Anomaly detection for data accountability of Mars telemetry data. Expert Systems With Applications, 189, 116060. Lien externe
