Author: thelabwithbradbarton

Perceptions: part II.

Perceptions: part II.

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It was raining. Safe from the downpour, I stood on the porch and enjoyed the weather. Fall is one of my top five favorite seasons, especially if I don’t have to be out in the rain. It was really coming down too, the air filled with the hissing roar of all that water smashing against all that ground. Against the roof, a more percussive pounding, while the bushes and trees in my yard played host to myriad tiny streams and rivulets cascading down their leaves and branches.
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Solving the mutation problem

Solving the mutation problem

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This might solve everything. I’ll need to do some testing to make sure I’m right, but there’s a beautiful way around the mutation problem.

It’s not the type of mutation; it’s the type of population. We’ve got two main types: the quick and the slow. The quick ones, like m1.pop, do their best to fill up all available space and to make new figures as quickly as possible. The slow ones generally find a particular population size, and stay there, no matter how much more room is available; and they only make new figures slowly. I’ve said several times, and it turns out to be true—speed isn’t everything.
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Ep 191: A few fragments

Ep 191: A few fragments

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A few fragments

Though Phil is gone, his voice is not. Not quite yet. We spoke much more last time than made it in the episode. Some of what we talked about was interesting and/or amusing, so I stuck together another episode with it.
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Zombie code

Zombie code

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I’ve come up with a mixed bag of modifications to my artificial life system. I’m trying to get my digital creatures, which I call “figures,” more stable, quicker to emerge from randomness, and better able to tolerate mutations. As far as quicker emergence and greater stability goes, the tricks are working out. The system as gone from requiring multiple runs of 8 to 12 hours each, just to generate one stable population; to a system that can make a stable population consistently in less than 15 minutes, usually less than ten, and often less than 5. As far as tolerating mutation goes, it still isn’t any better. One single mutation is still enough to kill off the entire population, no matter which trick or combination of tricks I use.
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Two entries from my project journal

Two entries from my project journal

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22 :: Sunday September 2, 2018

I need to jot this down before I forget.

I only just yesterday got the fat ports to work. I’d been calling inner write on the baby figures before they were added to the realm. That meant there were no trackers connected to the baby figures when their safe random method got called. All that time, I was just running the system more or less like the fat ports weren’t even there. Once I fix that, it ran like a dream.
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Ep 189: The Miocene, the right teeth for the right job

Ep 189: The Miocene, the right teeth for the right job

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The Miocene, the right teeth for the right job

Today we chat about the Miocene. During this epoch, kelp forest spread along the shoreline, creating habitat for otters and pinnipeds. The climate cooled and forest gave way to open grassland. Those grazing animals without the right kind of teeth died out, and the early apes diversified. Toward the end of the epoch, the branch of apes that would give rise to humans appeared.
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ep 188: The Oligocene, getting cold and growing huge

ep 188: The Oligocene, getting cold and growing huge

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Podcast: Play in new window | Download (Duration: 14:37 — 10.4MB) | Embed

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The Oligocene, getting cold and growing huge

Today we talk about the Oligocene epoch, when the climate cooled and forest gave way to areas of open land. Grass spread beyond the lake shores and river sides where it had been living, and began to spread across the landscape. Many animal types changed their bodies to become better runners, and we got the largest mammal ever to walk the earth.
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Searching for a kinder gentler mutation

Searching for a kinder gentler mutation

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I spent a couple of weeks testing a notion I had. I’m not going to bother explaining—it would take too long for something that I’m not going to use. So far as I can tell, what I did to try and increase the system’s stability made it even more fragile. For example, with the usual approach, as few as 5 mutations have wiped out an entire population. With the other method, what I called snapcom, the one I’m tossing out the window, one mutation was enough. I gathered some statistics, but the results were inconclusive. Still, even if it is somehow performing better, it’s not enough better.
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