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Accelerating Evolutionary Design Exploration with Predictive and Generative Models. (Accélérer l'exploration de la conception par algorithme évolutionniste grâce à des modèles prédictifs et génératifs).. University of Lorraine, Nancy, France, (2020)COIL: Constrained optimization in learned latent space: learning representations for valid solutions., , , и . GECCO Companion, стр. 1870-1877. ACM, (2022)Data-efficient neuroevolution with kernel-based surrogate models., , и . GECCO, стр. 85-92. ACM, (2018)Multi-stage evolution of single- and multi-objective MCLP - Successive placement of charging stations., , , , и . Soft Comput., 21 (17): 4859-4872 (2017)Discovering representations for black-box optimization., , и . GECCO, стр. 103-111. ACM, (2020)Editorial to the "Evolutionary Reinforcement Learning" Special Issue., , и . ACM Trans. Evol. Learn. Optim., 3 (3): 9:1-9:2 (сентября 2023)Evolution of optimal control for energy-efficient transport., и . Intelligent Vehicles Symposium, стр. 1121-1126. IEEE, (2014)Are quality diversity algorithms better at generating stepping stones than objective-based search?, , и . GECCO (Companion), стр. 115-116. ACM, (2019)Data-efficient exploration, optimization, and modeling of diverse designs through surrogate-assisted illumination., , и . GECCO, стр. 99-106. ACM, (2017)Evolutionary Design via Indirect Encoding of Non-Uniform Rational Basis Splines.. GECCO (Companion), стр. 1197-1200. ACM, (2015)