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Evolutionary Algorithms with Machine Learning Models for Multiobjective Optimization in Epidemics Control

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Evolutionary Multi-Criterion Optimization (EMO 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13970))

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Abstract

This paper studies the use of machine learning models for multiobjective optimization of vaccinations used to control an epidemic spreading in a graph representing contacts between individuals. Graph nodes are parameterized by attributes which are known to affect the susceptibility of people to influenza and the disease transmission probability depends on the attributes of the node which can get infected. Instead of directly optimizing the assignment of vaccine doses to graph nodes, in the proposed approach an evolutionary algorithm is used to train a neural network, which is subsequently used to make decisions about vaccinating the nodes of the graph. In the paper, both a classifier and a regression model are used to select graph nodes for vaccination. The results obtained using the machine learning models improve over the results obtained by optimizing the assignment of vaccine doses to graph nodes. Importantly, the models trained on a certain problem instance can be used for selecting graph nodes for vaccination when other problem instances are solved.

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Acknowledgment

This work was supported by the Polish National Science Centre under grant no. 2015/19/D/HS4/02574. Calculations have been carried out using resources provided by Wroclaw Centre for Networking and Supercomputing (http://wcss.pl), grant No. 407.

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Authors and Affiliations

  1. Department of Information Technologies, Wrocław University of Economics and Business, Wrocław, Poland

    Krzysztof Michalak

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  1. Krzysztof Michalak
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Correspondence to Krzysztof Michalak .

Editor information

Editors and Affiliations

  1. Leiden University, Leiden, The Netherlands

    Michael Emmerich

  2. Leiden University, Leiden, The Netherlands

    André Deutz

  3. Leiden University, Leiden, The Netherlands

    Hao Wang

  4. Leiden University, Leiden, The Netherlands

    Anna V. Kononova

  5. TH Köln, Gummersbach, Germany

    Boris Naujoks

  6. University of Exeter, Exeter, UK

    Ke Li

  7. University of Jyvaskyla, Jyvaskyla, Finland

    Kaisa Miettinen

  8. De Montfort University, Leicester, UK

    Iryna Yevseyeva

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Michalak, K. (2023). Evolutionary Algorithms with Machine Learning Models for Multiobjective Optimization in Epidemics Control. In: Emmerich, M., et al. Evolutionary Multi-Criterion Optimization. EMO 2023. Lecture Notes in Computer Science, vol 13970. Springer, Cham. https://doi.org/10.1007/978-3-031-27250-9_31

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  • DOI: https://doi.org/10.1007/978-3-031-27250-9_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-27249-3

  • Online ISBN: 978-3-031-27250-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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