In a previous post, I said that adding energy to the system would speed it up. Between that and adjusting the mutation rate, I was right. The figures are adapting to changes within minutes, instead of taking hours.

In the most recent post, I managed to get the figures to push a button when they see a light. Take a look at a typical run, after turning off the energy requirements and mutation, running the population that was produced, guesser0.pop.
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When I posted the last post I posted, I was smugly certain that I’d be posting another post, (that very day!)all about how success had been achieved. It had been a while since I’d last looked at my figures. I had episode 200 to kick out the door, and some research to do for a new project. It wasn’t that long of a break, but just long enough to mean I had to read my journal to figure out where I was, and what I should do next. Once I knew what was what, I realized that what I had planned to do was pointless and useless, at least compared to the shiny new idea in my head.
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I’m phrasing it as though they are looking at a light, because that’s what I’m doing—experimenting with implementing vision in an artificial life system.

There’s a factoid floating around that evolution cannot explain the eye. It’s based on a phrase written by Darwin. He had said that it is difficult to believe that something as complicated and well-constructed as the human eye could have come about by evolution. It was a rhetorical device. In the very next paragraph, Darwin lays out how evolution could produce an eye, or set of eyes, or whatever the given creature might happen to need.
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So, here’s the game, implemented in game.java. A certain number of cycles is chosen, between a minimum amount, 10 at the moment, and a maximum, 50. Then a coin is flipped that determines whether the light is on or off for this round. One port will, when called, set b to 1 if the light is on, and -1 if it’s off. So far so good. This is enough for the figures to have the simplest eye imaginable. They can detect whether or not the light is on, something that even single cells are capable of. Next, we attempt to get them to react to whether or not the light is on, again, something that some of the simplest life forms can do.
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Their first task was easy. Can the digital organisms I wrote, called “figures,” learn to call a good command and avoid a bad one?

My first job was easy. It only took 6 lines of code. Each digital creature, each “figure,” has a certain amount of energy.

Figure.energy = 12,168;

Each time a figure has a turn, (when they execute one command,) the energy level drops.

Energy=energy-1;

When a figure is out of energy, it dies. It is completely deleted from the system, leaving behind it’s children, if it had any.

If (figure.energy

It’s about time I tried using my artificial life as an artificial intelligence. It’s been the goal since day one. Before I could do that, I had to come up with the algorithm in the first place, and then make sure that the figures could actually evolve. To evolve, they need to change, and some of those changes should help them survive and reproduce, passing on their successful tricks to their offspring.
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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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