Hospital simulation model optimisation with a random ReLU expansion surrogate model
Paper in proceeding, 2021

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.

simulation optimisation

expensive optimisation

surrogate models

Author

Laurens Bliek

Eindhoven University of Technology

Arthur Guijt

Stichting Centrum voor Wiskunde & Informatica (CWI)

Rikard Karlsson

Chalmers, Electrical Engineering, Systems and control, Automation

GECCO 2021 Companion - Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion

13-14

2021 Genetic and Evolutionary Computation Conference, GECCO 2021
Virtual, Online, France,

Subject Categories

Computational Mathematics

Probability Theory and Statistics

Control Engineering

DOI

10.1145/3449726.3463279

More information

Latest update

8/9/2021 7