one of my favorite natural processes to simulate has always been genetic algorithms it's a very simple way to make a computer program learn to solve problems in an inefficient but impressive way inspired by how life evolved on our planet if you don't already know the concept behind genetic evolution i'm going to attempt to explain it in an oversimplified way every living being on earth has a unique set of genes which define every aspect of the organism ranging from physical appearance to psychological behaviors in the case of a human one gene could for example be responsible for the person's eye or hair color but others will also be responsible for much more important things like the number of limbs and their locations or the way the brain should be laid out when a living being reproduces it will pass part of its genes to its offspring with a slight chance that one or more genes mutate meaning that they change slightly if such a mutation is beneficial for your organism it will have a higher chance of passing these genes onto the next generation and so with every generation living beings adapt to their environment this concept can be applied to neural networks to train machine learning models which is where the neet algorithm comes from but it can also be used for much simpler simulations today i wanted to try something out that i haven't seen much on youtube and that's to simulate the evolution of a virus on a very basic level
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