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ben_wtoday at 6:20 PM2 repliesview on HN

Was going to say much the same. I recall one story about a genetic algorithm to make an oscillator with the fewest possible components, and it successfully did so by surprising the humans with a single wire, i.e. an antenna picking up nearby stray RF.


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robvirentoday at 6:22 PM

That is my favorite part of GA. Gradient free optimization but it turns out making a good fitness function is hard and like 70% of the time it just exploits some assumptions or gap you have in your theories. Really reveals the problem in different ways that traditional ML.

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formerly_proventoday at 9:26 PM

That sounds apocryphal but there was a noted paper describing a frequency discriminator implemented using a genetic algorithm and it ended up tied to the exact piece of silicon used to evolve it, with logic cells not connected to anything still changing the output.

https://osmarks.net/assets/misc/evolved-circuit.pdf