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I lost it at "emerging revolution". My PhD thesis in 2000 already used genetic algorithms for seeds and it was hardly new then.

deleted 8y ago
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LoSboccacc 8y ago
I had a genetically trained neural network driving a car simulation* in qbasick in 1997, and it wasn't "novel" it was quite common back then to combine them. *a very limited one, imagine 2d, no inertia, 5 sensors at 15° each other each reporting distance from the nearest "grass", with the road 20px wide, drawn by hand gray on green and fitting the screen 13 space. network was 5x5 neuron, with weights all part of the gneetic code. car never made past the fourth corner, let alone did a lap.
ur-whale 8y ago
Genetic algorithms are (and were already back in 2000) a pretty decent and -more importantly- generic solution to the problem of global optimization (as opposed to local optimization) when the problem to optimize has some sort of (maybe not-so-smooth) structure. Many of the recent "AI" development very often boil down to finding a local extremum using some sort of ski down the slope optimization program (aka "training"). These techniques very rarely tackles global optimization, or when they do, bundle it up under the moniker "hyper-parameter tuning". A good example of something that falls under "global optimization" and isn't often tackled in deep learning would be finding the correct deep net architecture for a given problem. The problem doesn't lend itself very well to local optimization, but might yield to GA-type optimization.
- foxes 8y ago
  How are genetic algorithms guaranteed in any way to give you a global optimum? I thought it might just work better when your search space is non smooth.
  - siekmanj 8y ago
    They're not, as far as I know. In fact, that was one of the big selling points of reinforcement learning - that it tends to reach a better minimum than GA.
- AareyBaba 8y ago
  Agreed. However, there is no reason to believe that a GA can give a better result than just randomly picking points in search space and doing local optimization around those points. GA's are evo-babble with much hand waving about 'correlation' during mutation or crossover rubbish. The 'curse of dimensionality' in global optimization doesn't go away by using GA's nor can you talk about their effectiveness without characterizing the landscape you are searching (the no free lunch theorem).
- machinelearning 8y ago
  https://arxiv.org/pdf/1709.06030.pdf https://arxiv.org/pdf/1709.06030.pdf
hacker_9 8y ago
Your missing the point I think. GAs aren't new, same as neural networks which were thought up half a century ago. It's the finding that GAs work at such a scale, with 100 of layers and millions of nodes and even outperform SGD, that is of interest here.

deleted 8y ago

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LoSboccacc 8y ago

I had a genetically trained neural network driving a car simulation* in qbasick in 1997, and it wasn't "novel" it was quite common back then to combine them. *a very limited one, imagine 2d, no inertia, 5 sensors at 15° each other each reporting distance from the nearest "grass", with the road 20px wide, drawn by hand gray on green and fitting the screen 13 space. network was 5x5 neuron, with weights all part of the gneetic code. car never made past the fourth corner, let alone did a lap.

ur-whale 8y ago

Genetic algorithms are (and were already back in 2000) a pretty decent and -more importantly- generic solution to the problem of global optimization (as opposed to local optimization) when the problem to optimize has some sort of (maybe not-so-smooth) structure. Many of the recent "AI" development very often boil down to finding a local extremum using some sort of ski down the slope optimization program (aka "training"). These techniques very rarely tackles global optimization, or when they do, bundle it up under the moniker "hyper-parameter tuning". A good example of something that falls under "global optimization" and isn't often tackled in deep learning would be finding the correct deep net architecture for a given problem. The problem doesn't lend itself very well to local optimization, but might yield to GA-type optimization.

foxes 8y ago
How are genetic algorithms guaranteed in any way to give you a global optimum? I thought it might just work better when your search space is non smooth.
- siekmanj 8y ago
  They're not, as far as I know. In fact, that was one of the big selling points of reinforcement learning - that it tends to reach a better minimum than GA.
AareyBaba 8y ago
Agreed. However, there is no reason to believe that a GA can give a better result than just randomly picking points in search space and doing local optimization around those points. GA's are evo-babble with much hand waving about 'correlation' during mutation or crossover rubbish. The 'curse of dimensionality' in global optimization doesn't go away by using GA's nor can you talk about their effectiveness without characterizing the landscape you are searching (the no free lunch theorem).
machinelearning 8y ago
https://arxiv.org/pdf/1709.06030.pdf https://arxiv.org/pdf/1709.06030.pdf

hacker_9 8y ago

Your missing the point I think. GAs aren't new, same as neural networks which were thought up half a century ago. It's the finding that GAs work at such a scale, with 100 of layers and millions of nodes and even outperform SGD, that is of interest here.