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Optimal Curing Strategy Enhancement of Epidemic Processes with Self-adaptive SBX Crossover

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Artificial Life and Evolutionary Computation (WIVACE 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 900))

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Abstract

Epidemic processes in networks pose sever challenges to network operators and more generally in the management of public health. One of these challenges is the research of an optimal curing policy able to suppress the epidemic. In this paper, we model the epidemic spreading in networks with a Susceptible-Infected-Susceptible (SIS) process and exploit the N-Intertwined Mean-Field Approximation (NIMFA) of the SIS model. Then, we propose a constrained genetic algorithm which assigns specific curing rates to nodes in order to minimize the total curing cost while reducing the number of infected nodes within the network. Simulating both real-world Internet backbones and Facebook networks, together with Erdős-Rényi, Watts-Strogatz and Bárabasi-Albert synthetic networks, we show that the use of a self-adaptive simulated binary crossover (SBX) improves a genetic algorithm employing a classical SBX crossover.

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Notes

  1. 1.

    The spectral radius of a square matrix \(A \in C^{N \times N}\) is the largest absolute value of its (real or complex) eigenvalues \(\lambda _1, \ldots , \lambda _N\), i.e. \(\lambda _{max}(A) = max\{\mid \lambda _1\mid , \ldots , \mid \lambda _N\mid \}\).

  2. 2.

    A semidefinite positive matrix \(A \in R^{N \times N}\) is a symmetric matrix such that \(x^TAx \ge 0 \) for all the \(x \in R^N\). Equivalently, all the eigenvalues of A are nonnegative.

  3. 3.

    http://www.topology-zoo.org/.

  4. 4.

    https://snap.stanford.edu/data/egonets-Facebook.html.

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Authors and Affiliations

  1. National Research Council of Italy (CNR), Institute for High Performance Computing and Networking (ICAR), Via Pietro Bucci, 8-9C, 87036, Rende (CS), Italy

    Clara Pizzuti & Annalisa Socievole

Authors
  1. Clara Pizzuti
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  2. Annalisa Socievole
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Corresponding author

Correspondence to Clara Pizzuti .

Editor information

Editors and Affiliations

  1. University of Parma, Parma, Italy

    Stefano Cagnoni

  2. University of Parma, Parma, Italy

    Monica Mordonini

  3. Università degli Studi eCampus, Novedrate, Italy

    Riccardo Pecori

  4. University of Bologna, Cesena, Italy

    Andrea Roli

  5. University of Modena and Reggio Emilia, Modena, Italy

    Marco Villani

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Pizzuti, C., Socievole, A. (2019). Optimal Curing Strategy Enhancement of Epidemic Processes with Self-adaptive SBX Crossover. In: Cagnoni, S., Mordonini, M., Pecori, R., Roli, A., Villani, M. (eds) Artificial Life and Evolutionary Computation. WIVACE 2018. Communications in Computer and Information Science, vol 900. Springer, Cham. https://doi.org/10.1007/978-3-030-21733-4_12

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  • DOI: https://doi.org/10.1007/978-3-030-21733-4_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-21732-7

  • Online ISBN: 978-3-030-21733-4

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