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Evolutionary Algorithms and Analog Electronic Circuits

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Re: Evolutionary Algorithms and Analog Electronic Circuits

#11

Post by mindcrime »

Houshalter wrote: post #10
jerf wrote: post #8

"Not very good as a reference but considering that this was generated automatically I think it's a success" Behold 80% of the appeal of evolutionary algorithms... people get so excited that they do anything that they are willing to overlook that you burned CPU-hours of time to produce something that doesn't do it very well. There's this weird disconnect between how people speak of them and the actual standards applie…

There is an annual award given to the creators of genetic algorithms that produce results competitive with humans: http://www.genetic-programming.org/combined.php Some of the results are really impressive. There are domains where stochastic optimization works really well. And even when it doesn't generate human competitive results, it's still cool. It's a computer program doing something previously only humans could…

Is this happening in 2015?

Re: Evolutionary Algorithms and Analog Electronic Circuits

#12

Post by sevensor »

api wrote: post #5

I studied EC a lot, and this is actually one of the strongest success areas for evolutionary algorithms. In general evolutionary computing does far better with real-world, analog, and physical systems than it does with brittle software systems. Other areas of success include mechanical component design, antennas, and materials. The results must be post-validated of course, since in some cases it will over-optimize to…

I agree whole-heartedly with the point about post-validation. I work in the field (EC) and the first dozen results you get are usually instances of the algorithm finding where your model or your assumptions are broken. For that reason the "On the Origins of Circuits" link posted in another comment is a really bad example. The results are total garbage because there was no model at all, just an actual physical FPGA. Models are great because they abstract away the effects that you really shouldn't rely on in your design. With out a model, the FPGA optimization gives us an answer that's only valid for one particular FPGA, at a particular point in its lifecycle. In fact, this might be why software optimization breaks --- nobody's figured out how to abstract away the irrelevant effects.

Re: Evolutionary Algorithms and Analog Electronic Circuits

#13

Post by nicklaf »

joshvm wrote: post #7

A related article with some more complicated circuits http://www.damninteresting.com/on-the-origin-of-circuits/ This was evolving an FPGA to do things like detect tones or voice commands. Weirdly reminiscent of DNA, logic cells which were apparently useless would cause the circuit to break when they were removed. Neither did the layout work when transferred to an identical FPGA.

Neither did the layout work when transferred to an identical FPGA.

Now that is quite fascinating. (The article goes into possible explanations, for those who care.)

Re: Evolutionary Algorithms and Analog Electronic Circuits

#14

Post by Houshalter »

mindcrime wrote: post #11
Houshalter wrote: post #10

Earlier quoted context omitted.

There is an annual award given to the creators of genetic algorithms that produce results competitive with humans: http://www.genetic-programming.org/combined.php Some of the results are really impressive. There are domains where stochastic optimization works really well. And even when it doesn't generate human competitive results, it's still cool. It's a computer program doing something previously only humans could…

Is this happening in 2015?

Yes: http://www.genetic-programming.org/hc2015/cfe2015.php

Re: Evolutionary Algorithms and Analog Electronic Circuits

#15

Post by jhallenworld »

He needs to put resistors in series with both the signal source and the power supply. Also there should always be some kind of load.

With 0-ohm signal source impedance very strange things happen. For example, a common emitter amplifier will seem to have much better bandwidth than it actually does (hides miller and base capacitance).

Re: Evolutionary Algorithms and Analog Electronic Circuits

#17

Post by etrautmann »

api wrote: post #5

I studied EC a lot, and this is actually one of the strongest success areas for evolutionary algorithms. In general evolutionary computing does far better with real-world, analog, and physical systems than it does with brittle software systems. Other areas of success include mechanical component design, antennas, and materials. The results must be post-validated of course, since in some cases it will over-optimize to…

Great point - they work well for anything where the optimization surface is relatively smooth, where small changes don't lead to highly suboptimal solutions. In programming, a single character can make a program go from working to not, so the likelihood of "discovering" a valid program is low.

Re: Evolutionary Algorithms and Analog Electronic Circuits

#18

Post by jerf »

Houshalter wrote: post #10
jerf wrote: post #8

"Not very good as a reference but considering that this was generated automatically I think it's a success" Behold 80% of the appeal of evolutionary algorithms... people get so excited that they do anything that they are willing to overlook that you burned CPU-hours of time to produce something that doesn't do it very well. There's this weird disconnect between how people speak of them and the actual standards applie…

There is an annual award given to the creators of genetic algorithms that produce results competitive with humans: http://www.genetic-programming.org/combined.php Some of the results are really impressive. There are domains where stochastic optimization works really well. And even when it doesn't generate human competitive results, it's still cool. It's a computer program doing something previously only humans could…

My criticism is more targeted at credulous blog posts, to do things like paint pictures with polygons more slowly than a human could do it in a paint program, with worse results. Professionals know what's what, and when to use it. I know it's not useless, it just doesn't particularly resemble the what blog posts like this present it as.

Re: Evolutionary Algorithms and Analog Electronic Circuits

#19

Post by stellioskes »

When I worked as a Research Associate I developed a genetic algorithm for the constrained via minimisation which was to help design circuit boards with the minimum number of vias/holes between the layers of the boards. Indeed it worked well . Unfortunately the professor took all the credit it was to me a good paying job. Search for constrained via minimisation using ga for more on the topic.

Re: Evolutionary Algorithms and Analog Electronic Circuits

#20

Post by api »

etrautmann wrote: post #17
api wrote: post #5

I studied EC a lot, and this is actually one of the strongest success areas for evolutionary algorithms. In general evolutionary computing does far better with real-world, analog, and physical systems than it does with brittle software systems. Other areas of success include mechanical component design, antennas, and materials. The results must be post-validated of course, since in some cases it will over-optimize to…

Great point - they work well for anything where the optimization surface is relatively smooth, where small changes don't lead to highly suboptimal solutions. In programming, a single character can make a program go from working to not, so the likelihood of "discovering" a valid program is low.

There's a whole subdomain of genetic programming research that centers around the design of evolvable computer languages and instruction encodings. There are some immensely interesting programming languages and instruction sets out there for which this is less true than for ordinary programming. But we still have yet to devise an encoding that is truly analog and smooth... except maybe for really exotic encodings that are too slow to be useful in any practical system. These are sort of like homomorphic crypto-- interesting but too compute intensive to run anything real.
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