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> Genetic Algorithms do exactly the same thing that Deep Learning methods do: optimize a function for a particular criterion. The difference is that for a GA,
by jmmcd 9y ago
> Genetic Algorithms do exactly the same thing that Deep Learning methods do: optimize a function for a particular criterion.
The difference is that for a GA, that can be any function, whereas for NNs it must be decomposable into a sum of partial objectives, and differentiable. Moreover, the result of training an NN is a function mapping a vector to a vector, whereas the result of training a GA could be an object drawn from any search space.
- randomsearch 9y agoI think there's a further difference - with some GAs (including GP, widely used by Sentient) the structure of the solution is "designed" alongside the parameterisation of that structure. Most DL work parameter tunes a structure that is hacked by human guesswork. The two approaches have strengths and weaknesses, and in no way does this make EC in general superior to DL, but perhaps different.
- jmmcd 9y agoI agree, but I intended that point as part of my final point -- an EA can draw a solution from any search space, including a space of trees, for example, as in GP.