@inproceedings{8a3b4b55dce2474aa0c9c103eedd20d5,
title = "Surrogate Assisted Methods for the Parameterisation of Agent-Based Models",
abstract = "Parameter calibration is a major challenge in agent-based modelling and simulation (ABMS). As the complexity of agent-based models (ABMs) increase, the number of parameters required to be calibrated grows. This leads to the ABMS equivalent of the 'curse of dimensionality'. We propose an ABMS framework which facilitates the effective integration of different sampling methods and surrogate models (SMs) in order to evaluate how these strategies affect parameter calibration and exploration. We show that surrogate assisted methods perform better than the standard sampling methods. In addition, we show that the XGBoost and Decision Tree SMs are most optimal overall with regards to our analysis.",
keywords = "Agent-based modelling and simulation, infectious disease epidemiology, machine learning, surrogate models",
author = "Rylan Perumal and {Van Zyl}, {Terence L.}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 7th International Conference on Soft Computing and Machine Intelligence, ISCMI 2020 ; Conference date: 14-11-2020 Through 15-11-2020",
year = "2020",
month = nov,
day = "14",
doi = "10.1109/ISCMI51676.2020.9311568",
language = "English",
series = "2020 7th International Conference on Soft Computing and Machine Intelligence, ISCMI 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "78--82",
booktitle = "2020 7th International Conference on Soft Computing and Machine Intelligence, ISCMI 2020",
address = "United States",
}