"It seems that some circuits had amplified radio signals present in the air that were stable enough over the 2 ms sampling period to give good fitness scores. These signals were generated by nearby PCs in the laboratory where the experiments took place."
The way to solve it is something engineers hate for some reason. You explicitly design (and simulate) VERY bad hardware. What's bad hardware ? A camera that has a noise floor of 30% it's measurements. Yes, even in low light conditions (also: noise floor must vary a lot between runs of the algorithm). An actuator that goes the right way 90% of the time, and the result of a particular voltage on the motor varies by 20-30%. And in 10% of cases, it's just entirely stuck, without giving any feedback abou tthat.
Or for the more visually inclined: https://www.youtube.com/watch?v=lUZUr7jxoqM
The lesson is that what engineers always do, open-loop designs (I send voltage, motor moves), can be incredibly outperformed in control, accuracy, resiliency, and more by much worse hardware closed-loop designs (I send voltage, motor moves, I check how it moves, I change voltage).
And yet somehow people seem to have incredible issues trusting such systems. For instance, autopilots are mostly open-loop designs. That's like a pilot flying a plane with his eyes glued shut, and no sense of balance (ie. he HAS to trust one instrument, and have no way to verify that, say, the plane actually goes up when they pull the stick. So if it's keeling over backwards or something, they'd just keep pulling the stick right up to the point they hit the decor). They implicitly trust the plane does what the autopilot orders it to do. If for some reason it doesn't ... it's not going to end well.
The issue is that closed-loop designs are much harder to write. The solution to that of course is to not write them, learn them. An autopilot that flies a plane, and when the plane breaks, rapidly learns how to fly a broken plane rather than just trusting it's (now inaccurate) model and killing everyone doing that.
> Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them. However, because evolution is an algorithmic process that transcends the substrate in which it occurs, evolution's creativity is not limited to nature. Indeed, many researchers in the field of digital evolution have observed their evolving algorithms and organisms subverting their intentions, exposing unrecognized bugs in their code, producing unexpected adaptations, or exhibiting outcomes uncannily convergent with ones in nature. Such stories routinely reveal creativity by evolution in these digital worlds, but they rarely fit into the standard scientific narrative. Instead they are often treated as mere obstacles to be overcome, rather than results that warrant study in their own right. The stories themselves are traded among researchers through oral tradition, but that mode of information transmission is inefficient and prone to error and outright loss. Moreover, the fact that these stories tend to be shared only among practitioners means that many natural scientists do not realize how interesting and lifelike digital organisms are and how natural their evolution can be. To our knowledge, no collection of such anecdotes has been published before. This paper is the crowd-sourced product of researchers in the fields of artificial life and evolutionary computation who have provided first-hand accounts of such cases. It thus serves as a written, fact-checked collection of scientifically important and even entertaining stories. In doing so we also present here substantial evidence that the existence and importance of evolutionary surprises extends beyond the natural world, and may indeed be a universal property of all complex evolving systems.
They used a FPGA for voice detection. It was fascinating they didn't understand how it worked, and it wasn't a universal design because it depended on manufacturing variation.
> A further five cells appeared to serve no logical purpose at all--there was no route of connections by which they could influence the output. And yet if he disconnected them, the circuit stopped working.
> It appears that evolution made use of some physical property of these cells--possibly a capacitive effect or electromagnetic inductance--to influence a signal passing nearby. Somehow, it seized on this subtle effect and incorporated it into the solution.
ive re-read that piece dozens of times, the implications are fascinating.
I'd imagine with even a moderate pool (~10?) the variation falls away and you're left with a robust design, although the process will take significantly longer.
Things like this:
which could be implemented in a more general manner and don't rely on the peculiarities of a single board. It may not prove to be robust, but in a pure research sense I'd love to see it.
> Genetic algorithm is supposed to configure a circuit into an oscillator, but instead makes a radio to pick up signals from neighboring computers
I suspect you're remembering some entertaining "how AI could hypothetically escape" scenario instead. Sure, maybe a "real AI" is going to escape but an evolutionary algorithm, which is just several loops around fitness and mutation functions, probably isn't going be coming up with such novel approaches.
Antenna optimization software has changed antenna design completely. Before wide-spread computer simulation, there was an awful lot of antenna range cut-and-try. Antennas are much better now. Learning to drive the modeling software is still a huge amount of work, but the results are well worth it.
An example from the world of amateur radio: the only popular HF tri-band beam to survive from the pre-simulation days is the KT-34 and big brother KT-34XA, originally from KLM. I was talking to Mike Stahl about it (The M in KLM, also one of the M's in M-squared) and he said he spent months going up and down towers at an antenna range. Pretty much all other hand-tuned competitors from that era have fallen by the wayside, the new simulation-verified designs being much better.
Mike is a great hands-on antenna designer -- when the Stanford dish was new he designed several feed horns for it.
My mother's beach house still has a shed full of various aluminium pieces, many of them part of a range of semi- or fully-built customised Yagi designs, the legacy of years of my father's cut-and-try attempts to optimise very marginal UHF television reception.
For large antennas with thin structures, the typical solver is "Method of Moments". NEC2 was developed by the government and is public domain. It is quite popular, and does some things well but it is also easy to stumble into modeling bugs/deficiencies, and isn't much use above high UHF. But is very useful if you know how not to step in the bugs, and is free. NEC4 falls under ITAR, the last I heard. So it isn't particularly hard to get a license, but you have to clear ITAR.
Microwave structures are more often done with a finite-element model, as I understand it.
Both rely on numerical approximations to Maxwell's equations. At least for MoM, each element cheats the boundary conditions a bit in order to make the problem tractable. With a fine enough grid, you get a good enough answer.
Another friend that has started two antenna companies is an NEC4 guru. I asked him: "How can I tell if my model has a small enough grid?" Him: "Keep reducing the mesh until the answer stops changing. When it stops, you had enough elements in the previous try." Antenna modelling is a bit of an art, I'm not expert, just hack a few as a hobby.
For different applications the various scoring measurements would be adjusted differently. (Sometimes you care greatly about minimizing back and side lobes, other times not so much, for instance.)
So here it is:
Professor: "Hmmm, it looks like somebody straightened out the antenna. It had an unusual shape for a reason."
Skipper: "Giillligan?! Do you know anything about this antenna?"
Gilligan: "Uh, I'm sorry guys, the antenna looked all bent up, so I straightened it. See how nice it looks now?"
(They both bop Gilligan on the noggin.)
I put some hematite sand on the top of a glass plate that was suspended about 5 inches from the bottom of the microwave oven. I wanted to see what kind of pattern the standing waves would heat the hematite to (this microwave didn't have an RF stirring fan).
Instead of some standing wave pattern, I ended up with a fractal like antenna that grew from a molten blob in the middle. The initial molten blob extended into arms of molten material, with each arm necessarily extending in the direction that maximized RF absorption, causing additional material to melt. It grew brighter and brighter as it extended, reached some peak, then some of the arms shorted and it dimmed. Then the plate shattered from the heat.
I wrote some software that converted the picture into antenna elements for some free antenna simulation package I found at the time, but it couldn't support enough elements to get close to the shape of the antenna and would just crash beyond 10 or so.
I'm not sure how useful of an antenna it would be, since it was maximized to generate heat, but it was a neat kitchen experiment.
Pure math (matrix of delays per element). Go crazy :)
Take a look at the dishes used for 71-86 GHz band mmwave links for instance.
For instance, putting your entire FCC city-of-license within your grade A signal contour from X miles away at a bearing of B. Or, in one case I am aware of, covering your puny city of license in one direction, and incidentally getting good coverage into the much bigger city off the back of the beam so that your sales department can get some traction selling advertising.
evolutionary algorithms are anything but sophisticated. i love them, but sophisticated they are not.