🔬 Genetic Algorithm Walkers
Evolution is a powerful algorithm for solving problems and is being using more and more in engineering to search for solutions to problems involving many interacting variables. Here is one simple example to illustrate how this works.
I found this interactive model very interesting because it gave me a better understanding of how the right genetic algorithms can cause creatures to walk upright.
I had fun with this simulation. I think it was interesting that sometimes it went so long without a new record and it gave me the impression that the evolution had met its highest point, but eventually another generation would come along. Unfortunately I gave it up at generation 143 when there hadn’t been a new record in 100 generations.
This interactive model was fun to play with not only because of how interactive it is but its cool so see that the correct algorithms are almost so close to what some of the incorrect were. After a while there were gaps in how long these “people” could stand which made me wonder what happened to the champions who didn’t make top two. Also, the way they walk reminds me of the way I run in nightmares so I got a little bit of a kick out of that.
I liked this a lot. It really showed how genetic traits pass from one generation to another. Theres only a mutation every once and a while, which makes the overall progress in learning to walk take a long time.
I played around with this algorithm and I did not know before those genetics play a huge role in everyday tasks animals do like walking, running, even sleeping.
This simulation was very fun to play around with. It really goes to show how perfectly aligned everything has to be for a trait to develop/ for evolution to occur.
I found this model to be very helpful because it showed me what genetic markers help creatures walk upright and I never really knew what genetics make us walk upright, so I found this very interesting.