Surrogate-based optimisation for a hospital simulation scenario using pairwise classifiers

This paper presents a surrogate-based approach that uses a relatively simple population-based optimisation algorithm, a basic Differential Evolution algorithm (DE), and experiments with two complementary approaches to construct a surrogate. This surrogate-based optimisation uses a predictive model in-line and decides whether to calculate a candidate individual (using the simulation model) or discard it as part of the optimisation process. The complementary approaches for the design of the surrogate are (1) a traditional regression-based surrogate that approximates the surface of the fitness landscape using a supervised continuous machine learning algorithm (XGBoost), and (2) a pairwise approach that models the surrogate as a binary classification problem for a machine learning algorithm (in this experiment we proposed a Decision Tree binary classifier). Although there is no statistical difference in the performance of both surrogate approaches, the surface/regression one obtains a slightly better performance when the execution is limited to 200 fitness evaluations. In contrast, the pairwise/classification approach obtains the lowest value and a lower mean in executions with 750 fitness evaluations.