
Evolving Simple Organisms - ColinWright
https://nathanrooy.github.io/posts/2017-11-30/evolving-simple-organisms-using-a-genetic-algorithm-and-deep-learning/
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mexus
Reminds me of an old Javascript experiment called "Creatures Avoiding Planks"
also based on neuroevolution:

Link: [http://otoro.net/planks/](http://otoro.net/planks/) Past Discussion:
[https://news.ycombinator.com/item?id=10711951](https://news.ycombinator.com/item?id=10711951)

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faeyanpiraat
This tutorial is almost the same as this ~10 years old tutorial, only a little
bit modernised: [http://www.ai-
junkie.com/ann/evolved/nnt1.html](http://www.ai-
junkie.com/ann/evolved/nnt1.html)

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nurettin
The article shows a useful neural network model for changing direction
depending on an input. However, I'm not sure what this has to do with deep
learning as there is only one hidden layer and even that one is only fully
connected to output. Edit: also, why evolve weights when you can
backpropagate?

~~~
Matumio
Even random search (in the weight space) can outperform gradient-based deep
reinforcement learning algorithms on some Atari games. Genetic algorithms are
quite competitive. Paper:
[https://arxiv.org/abs/1712.06567v1](https://arxiv.org/abs/1712.06567v1)

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jawarner
This is neat, if you want to extend it there's a version of genetic algorithm
for neural networks that tries out different connection settings and hidden
layer sizes -- it's called NEAT.

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vslira
Correct me if I’m wrong, but doesn’t inheriting by averaging the parents’
matrices only work in single layer perceptrons?

Anyway, nice exercise! :)

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35bge57dtjku
Cool, I think it's Matt Stonie on the right in the competitive eating GIF.

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__s
> ef evolve

Missing 'd'

