A New Model and Heuristic for Infection Minimization by Cutting Relationships

Santiago, Rafael de; Zunino, Wellington; Concatto, Fernando; Lamb, Luís C.

doi:10.1007/978-3-319-46672-9_56

Cited by 1 publication

(7 citation statements)

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“…The standard error is in the right-side of each average. Classic is the heuristic of [3] (section 2.1), and GA-Int and GA-Bin are the genetic algorithms proposed in this paper with integer and binary chromosomes respectively (Section 3). The number on the right-side of the label of genetic algorithm is the number of individuals in the population.…”

Section: Analysis Of Resultsmentioning

confidence: 99%

“…This paper investigated the genetic algorithm metaheuristic, analyzing its effectiveness in solving the Min-SEIS-Cluster problem. All versions of the genetic algorithm proposed in this paper surpassed the heuristic of [3]. The best results are achieved when using the size of the population equal to 100 and binary chromosomes.…”

Section: Discussionmentioning

confidence: 97%

“…In network contexts, the optimization is commonly applied in the number of connections between individuals to be removed [14]. One of these models is the Min-SEIS-Cluster, introduced in [3].…”

Section: Introductionmentioning

confidence: 99%

“…For example, nodes from the same family can affect each other with more intensity. In [3], a heuristic based on successive probabilistic simulations of infection dynamics over the instance is presented.…”

Section: Introductionmentioning

confidence: 99%

“…In [3], the authors have proposed a computational problem called Min-SEIS-Cluster that attempts to minimize the number of infected nodes over time by removing a specific number of edges. This problem also considers that the nodes can be infected with different dynamics in their social groups, so the nodes inside of the same community might have more probability of infecting each other.…”

Section: Introductionmentioning

confidence: 99%

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Genetic algorithm for epidemic mitigation by removing relationships

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et al. 2017

Proceedings of the Genetic and Evolutionary Computation Conference

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Min-SEIS-Cluster is an optimization problem which aims at minimizing the infection spreading in networks. In this problem, nodes can be susceptible to an infection, exposed to an infection, or infectious. One of the main features of this problem is the fact that nodes have different dynamics when interacting with other nodes from the same community. Thus, the problem is characterized by distinct probabilities of infecting nodes from both the same and from different communities. This paper presents a new genetic algorithm that solves the Min-SEIS-Cluster problem. This genetic algorithm surpassed the current heuristic of this problem significantly, reducing the number of infected nodes during the simulation of the epidemics. The results therefore suggest that our new genetic algorithm is the state-of-the-art heuristic to solve this problem.

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Section: Analysis Of Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 97%