I’m sitting on the edge of a binary ocean, casting hyperdimensional nets into the infinite waters of possible programs. My digital creatures, which I call “figures,” will run on my computer for hours, until I finally catch something.
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Written on Wednesday October3, 2018

Small sample sizes and all those caveats.

I’m tossing out the number of figures being born as a threshold. That will pop out of any of them at any time, and it’s just luck. One of them starts reproducing very quickly, and happens to find a window of relatively few mutation’s, and then skate on through to the finish line.

I retested the first five populations, snagged with a threshold of 100 and 100,000, if memory servs me. Looking at the most mutations given to a population, side by side results, first five and latest five.
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Written on Tuesday October 2, 2018

It seemed like a good idea. Let evolution solve the mutation problem for me. This is procrastination, as what I really need to do next is update the documentation and archive this version. It’s time to clean up the code and concentrate on making the system run faster. Still, I had a few days, and I’d notice that some populations were much more resistant to mutation than others.
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Written on Thursday September 27, 2018

mutation each extinction
figures1 skipcom
5.pop average 73
figures just fat
6.pop average 48
x.pop average 460 just fat longest with mu 131616 without only 38453
61.pop keeps doing too well to tell

I was trying to make s.pop into a six figures steady pop size stable population. I wanted to recreate 6.pop whose magic children have done so well that they climb off the measurement scale. I set things up so that s.pop was read from disc and stored in memory. Then, when s.pop died out, she’d be restored from memory rather than from disc. Mutations would happen unless the population was within a certain size range. Once it was all ready and as tested as I was willing to bother with, I let er rip.
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