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Evolving Through the Looking Glass: Learning Improved Search Spaces with Variational Autoencoders.

, , , and . PPSN (1), volume 13398 of Lecture Notes in Computer Science, page 371-384. Springer, (2022)

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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., , , and . GECCO Companion, page 1870-1877. ACM, (2022)Data-efficient neuroevolution with kernel-based surrogate models., , and . GECCO, page 85-92. ACM, (2018)Discovering representations for black-box optimization., , and . GECCO, page 103-111. ACM, (2020)Editorial to the "Evolutionary Reinforcement Learning" Special Issue., , and . ACM Trans. Evol. Learn. Optim., 3 (3): 9:1-9:2 (September 2023)Are quality diversity algorithms better at generating stepping stones than objective-based search?, , and . GECCO (Companion), page 115-116. ACM, (2019)Evolution of optimal control for energy-efficient transport., and . Intelligent Vehicles Symposium, page 1121-1126. IEEE, (2014)Automating Representation Discovery with MAP-Elites., , and . CoRR, (2020)A discretization-free metric for assessing quality diversity algorithms., , , and . GECCO Companion, page 2131-2135. ACM, (2022)Generative Design through Quality-Diversity Data Synthesis and Language Models., , , and . GECCO, ACM, (2024)