[buffalobuffalo]

There are several evolutionary algorithms that are used to design network topography as well as weights. NEAT is a popular one: https://en.wikipedia.org/wiki/Neuroevolution_of_augmenting_t...

There are a few problems with this approach though. For one, there are often many local maxima that a genetic algorithm can easily converge on.

Secondly, the state space is often enormous, and testing/discarding millions of generations of poorly constructed networks is a very inefficient way of sampling it.

These problems are often enough to prohibit GA use.

[falcor84]

I suppose that eventually, we'll have the metric being actual "money" that the neural nets try to earn (by mining crpyto, or solving captchas, etc.), such they'd have to pay for their own infrastructure costs. It would then be up to each of them to decide if they'd like to spawn "children", and then decide on an appropriate revenue-share model with them (maybe even pyramid schemes).

[signa11]

i went over these resources, and what strikes me as kind of odd, is the fact that the folks involved in this research / exploration tried to evolve both the architecture and the weights.

i was naively thinking that architecture should be evolved, while weights should be computed just as it is being done now i.e. via back-propogation etc.

[mnky9800n]

> There are a few problems with this approach though. For one, there are often many local maxima that a genetic algorithm can easily converge on.

> Secondly, the state space is often enormous, and testing/discarding millions of generations of poorly constructed networks is a very inefficient way of sampling it.

This is true for the living as well.