Networking with Evolution

Genetic Algorithms in Elixir — by Sean Moriarity (97 / 101)

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The world today is undoubtedly more connected than it has ever been. In one way or another, the majority of people are connected all around the world virtually and physically through social networks and trade. In an increasingly connected world, network science has become more and more relevant.

Initially, genetic algorithms might not seem like a relevant tool for network science, but if you dig deeper, you’ll find plenty of practical applications of basic genetic algorithms being used in network science. One relevant example is in epidemic mitigation.

Epidemic mitigation is the study of how to effectively prevent the spread of disease in a social network. Ideas surrounding epidemic mitigation are increasingly relevant in today’s world with the proliferation of COVID-19. As early as 2017, researchers[33] showed that genetic algorithms could be used to mitigate epidemics by removing, especially, the most impactful relationships in a social network. Disease spreads through contact between individuals. The paper shows how you could use genetic algorithms to remove key relationships to prevent the spread of a disease. This repository[34] shows an Elixir example of this application using libgraph[35] and genex.[36]

Another relevant application of network science is mapping and understanding social networks. In the same respect as the process of identifying relevant nodes in a network to mitigate the spread of disease, you can use genetic algorithms to identify the most influential nodes in a social network. The process of identifying and understanding influential nodes helps advertisers identify the most influential people on a platform. Identifying influential nodes can also help you understand the spread of information in a social network.

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