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Evolution Is the New Deep Learning

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Re: Evolution Is the New Deep Learning

#51

Post by turingcompeteme »

xants wrote: post #31
yorwba wrote: post #29

Earlier quoted context omitted.

If you mutate genomes by small additive modifications to a vector of continuous parameters, then taking lots of samples and keeping the best is essentially a stochastic approximation to gradient descent. However, unlike the SGD used in deep learning, it doesn't make use of calculus and therefore requires many more samples (exponentially more, in the worst case) to get a gradient of equivalent accuracy. I.e. it's slow…

Is it true that genetic algorithms have the benefit of being able to find the global optima more consistently due to the incorporation of randomness in subsequent generations? Whereas DL models often get stuck at local optima?

It depends, but I don't think general statements like that are necessarily true. Most methods allow trade-offs between converging quickly on local optima vs finding global optima. The mutation aspect of genetic algorithms is just one way to do this. With well tuned mutation parameters, genetic algorithms can definitely be successful at finding global optima, but similar measures exist for most methods.

Re: Evolution Is the New Deep Learning

#52

Post by make3 »

it's just not. Evolution learns more sloly than reinforcement learning, which itself learns much more slowly than supervised deep learning. Evolution doesn't have gradiants, and needs a huge amount of samples to learn anything.

Re: Evolution Is the New Deep Learning

#53

Post by [deleted] »

simion314 wrote: post #47
IAmEveryone wrote: post #40

If only wishing made it so... Evolutionary approaches have always had one big feature in their favour: they are far more fun to work with. They produce all these fascinating oddities, like the one that learnt to outsmart it’s opponents at infinite tic-tac-toe by playing coordinates 10 2312 and 4 7875 and watching them run out of memory trying to build a data structure for the board. They are also far easier to combin…

As others mentioned the hard part is modeling your problem, how to encode the "individuals", defining a good fitness function etc. I think he neural networks are more poplar now because are easy to use, you throw data at it and you get some results(though IMO I am scared that we are using things that we do not understand exactly how they work)

[deleted]

Re: Evolution Is the New Deep Learning

#54

Post by PeterisP »

xants wrote: post #31
yorwba wrote: post #29

Earlier quoted context omitted.

If you mutate genomes by small additive modifications to a vector of continuous parameters, then taking lots of samples and keeping the best is essentially a stochastic approximation to gradient descent. However, unlike the SGD used in deep learning, it doesn't make use of calculus and therefore requires many more samples (exponentially more, in the worst case) to get a gradient of equivalent accuracy. I.e. it's slow…

Is it true that genetic algorithms have the benefit of being able to find the global optima more consistently due to the incorporation of randomness in subsequent generations? Whereas DL models often get stuck at local optima?

DL models don't often get stuck at local optima. In theory, they could be vulnerable to that, but in practice they are not, it simply doesn't happen in most practical supervised learning applications.

I'm not up to date on theoretical research about this topic, but as far as I recall there are some interesting demonstrations on realistic problems showing that all the different "local" optima resulting from different random initializations are actually all "connected", i.e. there exists a nondecreasing route how you can get from a worse "local optimum" to the better one, so in reality it's not a local optimum, it's just that it's nontrivial to find the path to a better optimum if the space is very highdimentional.

Re: Evolution Is the New Deep Learning

#55

Post by Nomentatus »

mav3r1ck wrote: post #15

Having studied this extensively back when they were called Genetic Algorithms, I would like to offer a few insights. 1) One of the biggest reasons they fell out of favor for more "mathematical" approaches was that no one could really explain why exactly they worked. It makes sense on the surface that "survival of the fittest" and doing something akin to multiple stochastic gradient descents would work, but no one has…

Re 3, last resort: isn't it also true that any other method can likely be improved upon by taking whatever network results and using it as a starting point for genetic improvements? Sometimes the extra expense won't be worthwhile, of course, but if you want to reduce the number of edge cases for autonomous driving, say, it might well be worthwhile IMHO.

(There's an earlier discussion about the myth of local optima here that might or might not answer my question.)

Re: Evolution Is the New Deep Learning

#57

Post by guy98238710 »

ewjordan wrote: post #19
mav3r1ck wrote: post #15

Having studied this extensively back when they were called Genetic Algorithms, I would like to offer a few insights. 1) One of the biggest reasons they fell out of favor for more "mathematical" approaches was that no one could really explain why exactly they worked. It makes sense on the surface that "survival of the fittest" and doing something akin to multiple stochastic gradient descents would work, but no one has…

Wait, I'm confused...you're saying genetic approaches fell out of favor because they're basically just stochastic gradient descent? Most of modern DL relies heavily on SGD at various points during training. My impression is that they fell out of favor precisely because don't actually use any gradients, and end up converging on good maxima slower than you could if you used the gradients from the net. Am I off base her…

Genetic algorithms essentially run coordinate descent with some tweaks. On the positive side, coordinate descent doesn't need gradients and thus happily optimizes complex models. On the negative side, coordinate descent is roughly N times slower than gradient descent where N is the number of parameters being optimized.

Re: Evolution Is the New Deep Learning

#58

Post by hcrisp »

PeterisP wrote: post #54
xants wrote: post #31

Earlier quoted context omitted.

Is it true that genetic algorithms have the benefit of being able to find the global optima more consistently due to the incorporation of randomness in subsequent generations? Whereas DL models often get stuck at local optima?

DL models don't often get stuck at local optima. In theory, they could be vulnerable to that, but in practice they are not, it simply doesn't happen in most practical supervised learning applications. I'm not up to date on theoretical research about this topic, but as far as I recall there are some interesting demonstrations on realistic problems showing that all the different "local" optima resulting from different…

The theories I've heard for why you don't get stuck in local optima when using deep learning include:

  1. It's hard to get stuck in multidimensional space
  2. There are more saddles than convex local optima
  3. There are many local optima, but they are all useful
  4. Something related to spin glass theory (which I don't understand)
  5. There is no theory, or we haven't found it yet; all we 
  know is that it works in practice and the theory will have to catch up later

Re: Evolution Is the New Deep Learning

#59

Post by yters »

The only thing EAs have going for them is a biological metaphor, the magic of Darwinian evolution, fountain of endless novelty. But, modern science shows evolution does not really work in a Darwinian manner, so thus the metaphor ends.

Re: Evolution Is the New Deep Learning

#60

Post by bckygldstn »

klmr wrote: post #41
blacksmith_tb wrote: post #12

Earlier quoted context omitted.

A can think of a few objections to that argument. First, evolution isn't 'over' for any of the species on that list, and if any of those seemingly maladaptive traits really do cause serious problems for those species, we would expect them to be selected against (there are some cases where they are associated with other traits that are important and helpful, and so won't be tossed overboard even then). Evolution also…

I agree, the examples aren’t the best chosen. But there are cases which are pretty well established in biology as being maladaptations. — The laryngeal nerve is the most famous of those [1] because there’s really no advantage to the way it develops, and its characteristics poses significant health problems that do affect evolutionary fitness negatively. Yet there’s no easy fix to solve its positioning through gradual…

That nerve seems like an example of a local optimum. The way to avoid this is either to increase the mutation rate, or modify the fitness function so the intermediate states aren't so heavily penalised.

We can't easily do that with real evolution but it's configurable with evolutionary algorithms, as with most machine learning algorithms. The hard part is finding those parameters.

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