Print Email Facebook Twitter Black-box combinatorial optimization using models with integer-valued minima Title Black-box combinatorial optimization using models with integer-valued minima Author Bliek, L. (TU Delft Algorithmics) Verwer, S.E. (TU Delft Cyber Security) de Weerdt, M.M. (TU Delft Algorithmics) Date 2020 Abstract When a black-box optimization objective can only be evaluated with costly or noisy measurements, most standard optimization algorithms are unsuited to find the optimal solution. Specialized algorithms that deal with exactly this situation make use of surrogate models. These models are usually continuous and smooth, which is beneficial for continuous optimization problems, but not necessarily for combinatorial problems. However, by choosing the basis functions of the surrogate model in a certain way, we show that it can be guaranteed that the optimal solution of the surrogate model is integer. This approach outperforms random search, simulated annealing and a Bayesian optimization algorithm on the problem of finding robust routes for a noise-perturbed traveling salesman benchmark problem, with similar performance as another Bayesian optimization algorithm, and outperforms all compared algorithms on a convex binary optimization problem with a large number of variables. Subject Surrogate modelsBlack-box optimizationBayesian optimization To reference this document use: http://resolver.tudelft.nl/uuid:45261c3d-7f54-4435-acab-d8bf8f84708c DOI https://doi.org/10.1007/s10472-020-09712-4 ISSN 1012-2443 Source Annals of Mathematics and Artificial Intelligence, 89 (7), 639-653 Part of collection Institutional Repository Document type journal article Rights © 2020 L. Bliek, S.E. Verwer, M.M. de Weerdt Files PDF Bliek2020_Article_Black_b ... timiza.pdf 1.3 MB Close viewer /islandora/object/uuid:45261c3d-7f54-4435-acab-d8bf8f84708c/datastream/OBJ/view