The industrial challenge of the GECCO 2021 conference is an expensive optimisation problem, where the parameters of a hospital simulation model need to be tuned to optimality. We show how a surrogate-based optimisation framework, with a random ReLU expansion as the surrogate model, outperforms other methods such as Bayesian optimisation, Hyperopt, and random search on this problem.

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doi.org/10.1145/3449726.3463279
Genetic and Evolutionary Computation Conference
Centrum Wiskunde & Informatica, Amsterdam, The Netherlands

Bliek, L, Guijt, A, & Karlsson, R. (2021). Hospital simulation model optimisation with a random ReLU expansion surrogate model. In GECCO 2021 Proceedings (pp. 13–14). doi:10.1145/3449726.3463279