Covid-19 Variants Survivability Simulation With Genetic Algorithm

Genetic algorithm is well known for its ability to solve search and optimization problems with or without mathematical representation. The use of probabilistic transition rules makes it simpler than other algorithms that exploit complex mathematics and numerical methods. Running its iterative proces...

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Bibliographic Details
Main Authors: Sarungu, Cornelius M., Sarungu, Julianus J.
Format: Conference Proceeding
Language:English
Subjects:
COVID-19
genetic algorithm
Iterative methods
Linear programming
machine learning
Machine learning algorithms
Probabilistic logic
Search problems
simulation
Sociology
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Summary:Genetic algorithm is well known for its ability to solve search and optimization problems with or without mathematical representation. The use of probabilistic transition rules makes it simpler than other algorithms that exploit complex mathematics and numerical methods. Running its iterative process will create new population of solutions that has its average fitness value increased over generations. Meanwhile the domination of one variant over another in Covid-19 spread can be seen as the implementation of elitism principle of genetic algorithm in real life. The questions are what aspects contribute to this process, what kind of relationship are there between those aspects, and what aspect(s) that contribute more to the domination of one variant over another? We choose the genetic algorithm to do simulations on searching the answer for those questions because of its nature-like behavior that explore the probabilistic transition rules.
ISSN:2770-4661
DOI:10.1109/ICOIACT55506.2022.9972005