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Authors: Zong-De Jian ; Tsan-Sheng Hsu and Da-Wei Wang

Affiliation: Institute of Information Science, Taiwan

Keyword(s): Agent-based Simulation, Simulation for Disease Control, Surrogate-based Genetic Algorithm.

Related Ontology Subjects/Areas/Topics: Agent Based Modeling and Simulation ; Artificial Intelligence ; Business Analytics ; Cardiovascular Technologies ; Complex Systems Modeling and Simulation ; Computing and Telecommunications in Cardiology ; Data Engineering ; Decision Support Systems ; Decision Support Systems, Remote Data Analysis ; Health Engineering and Technology Applications ; Knowledge-Based Systems ; Sensor Networks ; Simulation and Modeling ; Simulation Tools and Platforms ; Software and Architectures ; Symbolic Systems

Abstract: Agent-based stochastic simulation is an established approach to study infectious diseases. Its advantage is the flexibility to incorporate important concepts. The effect of various mitigation strategies has been demonstrated using simulation models. Most of the previous studies compared a few options with a few selected scenarios. We propose to use genetic algorithms to search for the best vaccination strategy for a given scenario with the simulation program as fitness scorer. Vaccination efficacy varies significantly. Therefore, the real challenge is to find a good strategy without the knowledge of it. The simulation software is efficient, yet still takes three minutes to complete a simulation run with Taiwan population. We use surrogate to speed up the search about 1000 times. The surrogate has the average of the absolute value of error around 0.284 percent and the rank correlation coefficient is greater than 0.98 for all the scenarios except one. The optimal solution with surrogat e has fitness value very close to use simulations. The difference is generally less than one percent. We envision that an autonomous software searches through the huge scenario space with the help of surrogate function and adaptively executes simulation program to revise the surrogate function to produce higher fidelity surrogate and better search results. (More)

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Paper citation in several formats:
Jian, Z.; Hsu, T. and Wang, D. (2016). Searching Vaccination Strategy with Surrogate-assisted Evolutionary Computing. In Proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH; ISBN 978-989-758-199-1; ISSN 2184-2841, SciTePress, pages 56-63. DOI: 10.5220/0005958600560063

@conference{simultech16,
author={Zong{-}De Jian. and Tsan{-}Sheng Hsu. and Da{-}Wei Wang.},
title={Searching Vaccination Strategy with Surrogate-assisted Evolutionary Computing},
booktitle={Proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH},
year={2016},
pages={56-63},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005958600560063},
isbn={978-989-758-199-1},
issn={2184-2841},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH
TI - Searching Vaccination Strategy with Surrogate-assisted Evolutionary Computing
SN - 978-989-758-199-1
IS - 2184-2841
AU - Jian, Z.
AU - Hsu, T.
AU - Wang, D.
PY - 2016
SP - 56
EP - 63
DO - 10.5220/0005958600560063
PB - SciTePress

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