5 ms·

It's also using a simple neural network, which is the target of the genetic algorithm, if I understood it right. I haven't seen this combination often - does th

by Fannon 10y ago

It's also using a simple neural network, which is the target of the genetic algorithm, if I understood it right. I haven't seen this combination often - does that make sense in general, or is this just interesting as in playing around with those concepts?

deepnet 10y ago
Otoro has an excellent page graphically explaining the neuroevolution of an opponent for Slime Volleyball. http://blog.otoro.net/2015/03/28/neural-slime-volleyball/ http://blog.otoro.net/2015/03/28/neural-slime-volleyball/
cedricblack 10y ago
In a way that's what nature does :) I think this combination is quite common. While visiting a lab in Paris, I saw some researchers using neuroevolution to teach a bird-like robot how to fly. some links related to neuroevolution: https://en.wikipedia.org/wiki/Neuroevolution_of_augmenting_topologies https://en.wikipedia.org/wiki/Neuroevolution_of_augmenting_t... http://eplex.cs.ucf.edu/hyperNEATpage http://eplex.cs.ucf.edu/hyperNEATpage https://github.com/colgreen/sharpneat https://github.com/colgreen/sharpneat
maaaats 10y ago
It's an easy way to train a neural network when you cannot give it a long list of input/output pairs. Are there other good ways of training a neural network in this kind of scenario?
stefs 10y ago
it's a pretty standard procedure to train NNs through GAs but usually not very efficient (e.g. compared to backtracking). in some cases you might lack an easy way to calculate a fitness score out of the NN performance, which is needed to run the GA. i tried training a simple NN with a stupid hill climber some time ago but quickly hit a roadblock even with very few neurons because of local minima ... or maybe bugs. i guess for more complicated problems the pure GA training method might just not be "cost effective" (i.e. time/quality tradeoff).
- maaaats 10y ago
  > it's a pretty standard procedure to train NNs through GAs but usually not very efficient (e.g. compared to backtracking). Different applications, though. Backtracking in the normal sense needs input and expected output (e.g. lots of training data), while GA/EA learns to solve it without explicit wrong/correct actions, just the score at the end.

by Fannon 10y ago

deepnet 10y ago

Otoro has an excellent page graphically explaining the neuroevolution of an opponent for Slime Volleyball. http://blog.otoro.net/2015/03/28/neural-slime-volleyball/ http://blog.otoro.net/2015/03/28/neural-slime-volleyball/

cedricblack 10y ago

In a way that's what nature does :) I think this combination is quite common. While visiting a lab in Paris, I saw some researchers using neuroevolution to teach a bird-like robot how to fly. some links related to neuroevolution: https://en.wikipedia.org/wiki/Neuroevolution_of_augmenting_topologies https://en.wikipedia.org/wiki/Neuroevolution_of_augmenting_t... http://eplex.cs.ucf.edu/hyperNEATpage http://eplex.cs.ucf.edu/hyperNEATpage https://github.com/colgreen/sharpneat https://github.com/colgreen/sharpneat

maaaats 10y ago

It's an easy way to train a neural network when you cannot give it a long list of input/output pairs. Are there other good ways of training a neural network in this kind of scenario?

stefs 10y ago

it's a pretty standard procedure to train NNs through GAs but usually not very efficient (e.g. compared to backtracking). in some cases you might lack an easy way to calculate a fitness score out of the NN performance, which is needed to run the GA. i tried training a simple NN with a stupid hill climber some time ago but quickly hit a roadblock even with very few neurons because of local minima ... or maybe bugs. i guess for more complicated problems the pure GA training method might just not be "cost effective" (i.e. time/quality tradeoff).

maaaats 10y ago
> it's a pretty standard procedure to train NNs through GAs but usually not very efficient (e.g. compared to backtracking). Different applications, though. Backtracking in the normal sense needs input and expected output (e.g. lots of training data), while GA/EA learns to solve it without explicit wrong/correct actions, just the score at the end.