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It would be interesting to see how good the combination of EA an NN would be... This is basically us, humans: evolution + learning. Have there been any attempts

It would be interesting to see how good the combination of EA an NN would be... This is basically us, humans: evolution + learning. Have there been any attempts to combine the two?

nothis 8y ago
For what it’s worth, the final paragraph says so, I believe. No links, though.
wmwragg 8y ago
There have indeed, see https://en.m.wikipedia.org/wiki/Neuroevolution https://en.m.wikipedia.org/wiki/Neuroevolution
awb 8y ago
Was just thinking the same thing: Deep Evolution
chenglou 8y ago
Relevant: https://medium.com/beyond-intelligence/reinforcement-learning-or-evolutionary-strategies-nature-has-a-solution-both-8bc80db539b3 https://medium.com/beyond-intelligence/reinforcement-learnin...
EGreg 8y ago
Isn’t that what AlphaZero did with Monte Carlo Tree Search? It basically looked only at outcomes and backtracked in a Monte Carlo tree and assigned weights without preconceived training sets. And it outperformed the bespoke programs with existing training data.
flopska 8y ago
There are some papers from Prof. Braun describing experiments where he uses mutations from genetic / evolutionary algorithms to find the optimal structure of a neural net.
ufo 8y ago
Despite the catchy names, Genetic Algorithms and Neural Networks don't work quite the same way as their biological counterparts.
- sprt 8y ago
  I thought GAs did, but I do lack knowledge in the area. Could you highlight some differences?
  - ufo 8y ago
    Genetic Algorithms is a description for a wide range of techniques for adding some extra variety to search algorimhs. You maintain a population of candidate solutions and at each step you improve them by a combination of local search (like gradient descent or greedy neighbor search) and solution mixing (to help escape local minima). When doing this there are lots of ways to go about it. How to decide what population to keep, what local optimization to use, and how to mix the candidate optimizations. If you look closely, all of these are not quite how biology works. In biology the population is decided by natural or artificial selection while in a GA there may be more factors at play (such as favoring a more diverse pool). The way solutions are represented is also different. In biology you have genes which behave according to the laws of genetics. In a GA, on the other hand, the solution representation and the operations to mutate and mix solutions are carefully planned by an intelligent designer.
- benp84 8y ago
  "All models are wrong. Some are useful."
deleted 8y ago
[deleted]
mrfusion 8y ago
In my nn class Thomas tried using an ea to evolve the weights for a nn instead of using backpropagation. Surprisingly it performed way worse.

It would be interesting to see how good the combination of EA an NN would be... This is basically us, humans: evolution + learning. Have there been any attempts to combine the two?

nothis 8y ago

For what it’s worth, the final paragraph says so, I believe. No links, though.

wmwragg 8y ago

There have indeed, see https://en.m.wikipedia.org/wiki/Neuroevolution https://en.m.wikipedia.org/wiki/Neuroevolution

awb 8y ago

Was just thinking the same thing: Deep Evolution

chenglou 8y ago

Relevant: https://medium.com/beyond-intelligence/reinforcement-learning-or-evolutionary-strategies-nature-has-a-solution-both-8bc80db539b3 https://medium.com/beyond-intelligence/reinforcement-learnin...

EGreg 8y ago

Isn’t that what AlphaZero did with Monte Carlo Tree Search? It basically looked only at outcomes and backtracked in a Monte Carlo tree and assigned weights without preconceived training sets. And it outperformed the bespoke programs with existing training data.

flopska 8y ago

There are some papers from Prof. Braun describing experiments where he uses mutations from genetic / evolutionary algorithms to find the optimal structure of a neural net.

ufo 8y ago

Despite the catchy names, Genetic Algorithms and Neural Networks don't work quite the same way as their biological counterparts.

sprt 8y ago
I thought GAs did, but I do lack knowledge in the area. Could you highlight some differences?
- ufo 8y ago
  Genetic Algorithms is a description for a wide range of techniques for adding some extra variety to search algorimhs. You maintain a population of candidate solutions and at each step you improve them by a combination of local search (like gradient descent or greedy neighbor search) and solution mixing (to help escape local minima). When doing this there are lots of ways to go about it. How to decide what population to keep, what local optimization to use, and how to mix the candidate optimizations. If you look closely, all of these are not quite how biology works. In biology the population is decided by natural or artificial selection while in a GA there may be more factors at play (such as favoring a more diverse pool). The way solutions are represented is also different. In biology you have genes which behave according to the laws of genetics. In a GA, on the other hand, the solution representation and the operations to mutate and mix solutions are carefully planned by an intelligent designer.
benp84 8y ago
"All models are wrong. Some are useful."

deleted 8y ago

[deleted]

mrfusion 8y ago

In my nn class Thomas tried using an ea to evolve the weights for a nn instead of using backpropagation. Surprisingly it performed way worse.