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.

, ,
doi.org/10.1145/3449726.3463279
Genetic and Evolutionary Computation Conference
Centrum Wiskunde & Informatica, Amsterdam (CWI), 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