Test problems and representations for graph evolution

2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

2018

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The accurate modeling of epidemics on social contact networks is difficult due to the variation between different epidemics and the large number of parameters inherent to the problem. To reduce complexity, evolutionary computation is used to create a generative representation of the epidemic model. Previous gains from the use of local, verses global, operators are further explored to better balance exploration and exploitation of the genetic algorithm. A typical parameter study is conducted to test this new local operator and the new method of point packing is utilized as a proof of concept to perform a better search of the parameter space. All experiments from both approaches are tested against nine epidemic profiles. The point-packing driven parameter search demonstrates both that the algorithm parameters interact substantially and in a non-linear fashion and also shows that the good parameter settings are problem specific.

Section: Other Graph-evolution Problemsmentioning

confidence: 99%

Parameter selection for modeling of epidemic networks

2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

2018

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“…This graph was chosen because earlier research [3], [5] has demonstrated that graphs having vertices with degree 4-5 are desirable for the test problems. Other work on network evolution appears in [1]- [4], [6].…”

Section: Profile Matching (Pm) Problemmentioning

confidence: 99%

Evolving the Curve

2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

et al. 2020

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Evolutionary algorithms are used to generate personal contact networks, modelling human populations, that are most likely to match a given epidemic profile. The Susceptible-Infected-Removed (SIR) model is used and also expanded upon to allow for an extended period of infection, termed the SIIR model. The networks generated for each of these models are thoroughly evaluated for their ability to match nine different epidemic profiles. The addition of the SIIR model showed that the model of infection has an impact on the networks generated. For the SIR and SIIR models, these differences were relatively minor in most cases.

“…This graph was chosen because previous research [5], [7] has demonstrated that graphs having vertices with four or five edges are desirable for the test problems. Other graph topologies appear in previous work [1], [3], [5], [6], [8].…”

Section: The Local Thads-n Representationmentioning

confidence: 99%

Representation for Evolution of Epidemic Models

2019 IEEE Congress on Evolutionary Computation (CEC)

2019

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Creating a representation capable of generating personal contact networks that are most likely to exhibit specific epidemic behavior is difficult due to the inherit volatility of an epidemic and the numerous parameters accompanying the problem. To surpass these hurdles, evolutionary algorithms are used to create a generative solution which generates personal contact networks, modeling human populations, to satisfy the epidemic duration and epidemic profile matching problems. This representation is entitled the Local THADS-N representation. Two new operators are added to the original THADS-N system, and tested with a traditional parameter sweep and a parameter selection method known as point packing on nine epidemic profiles. Additionally, a new epidemic model is implemented in order to allow for lost immunity within a population thus increasing the length of an epidemic.