Hospital simulation model optimisation with a random ReLU expansion surrogate model
Paper i 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

Författare

Laurens Bliek

Technische Universiteit Eindhoven

Arthur Guijt

Stichting Centrum voor Wiskunde & Informatica (CWI)

Rikard Karlsson

Chalmers, Elektroteknik, System- och reglerteknik, 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,

Ämneskategorier

Beräkningsmatematik

Sannolikhetsteori och statistik

Reglerteknik

DOI

10.1145/3449726.3463279

Mer information

Senast uppdaterat

2021-08-09