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Content
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Breck Yunits pfp
Breck Yunits
@breck
ETA!: A Measure of Evolution https://breckyunits.com/eta.html
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Connor McCormick ☀️ pfp
Connor McCormick ☀️
@nor
Why would reducing the size of the assembly pool increase the lifespan of an idea?
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Breck Yunits pfp
Breck Yunits
@breck
Increase the lifespan of a *bad* idea. A larger assembly pool means a bad idea will evolve into a good idea faster.
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Connor McCormick ☀️ pfp
Connor McCormick ☀️
@nor
why would that be the case? A larger assembly pool just means the search space is larger but it says nothing about the average fitness or distribution of possible assemblies
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Breck Yunits pfp
Breck Yunits
@breck
If A' is an alteration of A with a quantum jump in fitness, and A' is composed of (A + Z), then we can't get to A' until Z is in the assembly pool.
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Connor McCormick ☀️ pfp
Connor McCormick ☀️
@nor
Ok but if I add X & Y then the assembly pool is larger but won’t reach A' because A' requires Z I can do this infinitely, adding elements to A that don’t improve fitness Larger A increases the search space with no guarantee of improved fitness, in fact increasing the lifespan of “bad” ideas, not decreasing
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Breck Yunits pfp
Breck Yunits
@breck
There is a guarantee of improved fitness by increasing A. If you only added bad ideas to A, then that implies you have a filter F that can detect good ideas, so just add !F. If you add all ideas at random, then you will add ideas that improve fitness.
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Connor McCormick ☀️ pfp
Connor McCormick ☀️
@nor
Haha. All you're saying is that there's a guarantee of improved fitness by increasing A as long as subassemblies on average improve fitness. That's a tautology. What this formalism is missing is that the context / environment informs the time-to-fitness. Here, you can test it easily: implement a genetic algorithm searching for the string "abababababababa". Now, randomly select a subset of letters from the English alphabet as the assembly elements A (e.g. A = {c, g, e, z}). On average, time-to-fitness will tend to decrease with each marginal element added to the assembly. I'd guess the graph looks something like this:
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Breck Yunits pfp
Breck Yunits
@breck
oh you just gave me a really big idea. brb.
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Connor McCormick ☀️ pfp
Connor McCormick ☀️
@nor
P.S. said this backward "On average, time-to-fitness will tend to decrease with each marginal element added to the assembly." but the graph is correct. I.e. there comes a point where starting a genetic algorithm with a larger number of elements on average slows down its "fitness rate" / learning rate.
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