“This may not sound like a profound insight, since it is true by definition. But consider—how many comic books talk about “mutation” as if it were a source of power? Mutation is random. It’s the selection part, not the mutation part, that explains the trends of evolution.”
I think this is a specific case of people treating optimization power as if it just drops out of the sky at random. This is certainly true for some individual humans (eg., winning the lottery), but as you point out, it can’t be true for the system as a whole.
“These greedy algorithms work fine for some problems, but on other problems it has been found that greedy local algorithms get stuck in local minima.”
Er, do you mean local maxima?
“When dealing with a signal that is just below the threshold, a noiseless system won’t be able to perceive it at all. But a noisy system will pick out some of it—some of the time, the noise and the weak signal will add together in such a way that the result is strong enough for the system to react to it positively.”
In such a case, you can clearly affect the content of the signal, so why not just give it a blanket boost of ten points (or whatever), if the threshold is so high that you’re missing desirable data?
“This may not sound like a profound insight, since it is true by definition. But consider—how many comic books talk about “mutation” as if it were a source of power? Mutation is random. It’s the selection part, not the mutation part, that explains the trends of evolution.”
I think this is a specific case of people treating optimization power as if it just drops out of the sky at random. This is certainly true for some individual humans (eg., winning the lottery), but as you point out, it can’t be true for the system as a whole.
“These greedy algorithms work fine for some problems, but on other problems it has been found that greedy local algorithms get stuck in local minima.”
Er, do you mean local maxima?
“When dealing with a signal that is just below the threshold, a noiseless system won’t be able to perceive it at all. But a noisy system will pick out some of it—some of the time, the noise and the weak signal will add together in such a way that the result is strong enough for the system to react to it positively.”
In such a case, you can clearly affect the content of the signal, so why not just give it a blanket boost of ten points (or whatever), if the threshold is so high that you’re missing desirable data?