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Personalizing performance regression models to black-box optimization problems.

, , , , and . GECCO, page 669-677. ACM, (2021)

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Trajectory-based Algorithm Selection with Warm-starting., , , , , and . CEC, page 1-8. IEEE, (2022)Improving Evaluation of Recombination-based Cartesian Genetic Programming., , , , and . CoRR, (May 2026)Self-Adjusting Weighted Expected Improvement for Bayesian Optimization., , , , and . AutoML, volume 224 of Proceedings of Machine Learning Research, page 6/1-50. PMLR, (2023)Future Trends in Digital Services and Products: Evidence from Serbian Manufacturing Firms., , , , , and . APMS (2), volume 664 of IFIP Advances in Information and Communication Technology, page 341-350. Springer, (2022)Adaptive landscape analysis., and . GECCO (Companion), page 2032-2035. ACM, (2019)Towards Online Landscape-Aware Algorithm Selection in Numerical Black-Box Optimization. (Vers une sélection en ligne d'algorithmes tenant compte du paysage dans l'optimisation numérique de boîte noire).. Sorbonne University, Paris, France, (2021)Towards Feature-Based Performance Regression Using Trajectory Data., , and . EvoApplications, volume 12694 of Lecture Notes in Computer Science, page 601-617. Springer, (2021)Personalizing performance regression models to black-box optimization problems., , , , and . GECCO, page 669-677. ACM, (2021)The impact of hyper-parameter tuning for landscape-aware performance regression and algorithm selection., , , and . GECCO, page 687-696. ACM, (2021)Per-Run Algorithm Selection with Warm-starting using Trajectory-based Features - Data., , , , , , and . (April 2022)