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As expected, the article seems to be a typical content marketing piece. If you're looking for real insights into evolutionary algorithms, specifically "neuroevo

As expected, the article seems to be a typical content marketing piece. If you're looking for real insights into evolutionary algorithms, specifically "neuroevolution", I highly recommend to read this article: https://www.oreilly.com/ideas/neuroevolution-a-different-kind-of-deep-learning https://www.oreilly.com/ideas/neuroevolution-a-different-kin... I enjoyed it much more than - what feels like - a quickly thrown together marketing piece with no real value for the reader.

vanderZwan 9y ago
Thank you, saved it for later. Do you have any other links to offer?
- peterstoziek 9y ago
  Unfortunately not in this domain.
- ericjang 9y ago
  Here's a recent survey / observational science paper by some prominent "neuroevolution" / A-Life researchers. https://arxiv.org/abs/1803.03453 https://arxiv.org/abs/1803.03453. I found this refreshing because it's rare that science papers talk about the debugging and experimental process and debugging journeys underlying this research.
- ofrancon 9y ago
  A few links you can look at if you're interested in neuroevolution, from the same group of researchers: Ken Stanley and Risto Miikkulainen original NEAT (NeuroEvolution of Augmenting Topologies) paper: http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf Ken Stanley's novelty search page, and a link to his book, "Why Greatness Cannot Be Planned: The Myth of the Objective": http://eplex.cs.ucf.edu/noveltysearch/userspage/ http://eplex.cs.ucf.edu/noveltysearch/userspage/ Risto Miikkulainen's Evolving Deep Neural Networks paper: https://arxiv.org/abs/1703.00548 https://arxiv.org/abs/1703.00548 Ken Stanley & team's work at Uber, with links to some recent papers: https://eng.uber.com/deep-neuroevolution/ https://eng.uber.com/deep-neuroevolution/
  - yazr 9y ago
    Are these evolutionary techniques considered more or less sample-efficient (and or cpu-efficient) compared to DL with GD ??
jamesblonde 9y ago
Not quite a typical piece - it managed to make it to #2 on HN.
- John_KZ 9y ago
  I upvote links that promote discussion of interesting concepts, even if the article itself is really bad - like this one. I couldn't bother reading the original article, but designing neural networks via evolutionary algorithms is a very interesting concept I wasn't aware of.
deepnet 9y ago
Thanks for the great link surveying Evolution and NEAT ( Neuroevolution of Neural Nets ) by Ken Stanley who pioneered the method. The OP is by Risto Miikkulainen who was Stanley's collaborator on NEAT and should not be summarily dismissed.
rpm13 9y ago
Note that this blog post is not an article per se, but an overview of a research website (https://sentient.ai/sentient-labs/ea https://sentient.ai/sentient-labs/ea) built around five new research papers. The website offers demos that illustrate neuroevolution and evolutionary computation concepts at a much more concrete level than the papers can.
- rpm13 9y ago
  For a short intro to neuroevolution there is http://www.scholarpedia.org/article/Neuroevolution http://www.scholarpedia.org/article/Neuroevolution However, these overview articles do not include the newest research on evolving deep learning networks. Three such papers are introduced at https://www.sentient.ai/sentient-labs/ea-1/; https://www.sentient.ai/sentient-labs/ea-1/; there are other recent ones at https://research.googleblog.com/2018/03/using-evolutionary-automl-to-discover.html https://research.googleblog.com/2018/03/using-evolutionary-a... and https://eng.uber.com/deep-neuroevolution/ https://eng.uber.com/deep-neuroevolution/. It is a rapidly developing area.

vanderZwan 9y ago

Thank you, saved it for later. Do you have any other links to offer?

peterstoziek 9y ago
Unfortunately not in this domain.
ericjang 9y ago
Here's a recent survey / observational science paper by some prominent "neuroevolution" / A-Life researchers. https://arxiv.org/abs/1803.03453 https://arxiv.org/abs/1803.03453. I found this refreshing because it's rare that science papers talk about the debugging and experimental process and debugging journeys underlying this research.
ofrancon 9y ago
A few links you can look at if you're interested in neuroevolution, from the same group of researchers: Ken Stanley and Risto Miikkulainen original NEAT (NeuroEvolution of Augmenting Topologies) paper: http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf Ken Stanley's novelty search page, and a link to his book, "Why Greatness Cannot Be Planned: The Myth of the Objective": http://eplex.cs.ucf.edu/noveltysearch/userspage/ http://eplex.cs.ucf.edu/noveltysearch/userspage/ Risto Miikkulainen's Evolving Deep Neural Networks paper: https://arxiv.org/abs/1703.00548 https://arxiv.org/abs/1703.00548 Ken Stanley & team's work at Uber, with links to some recent papers: https://eng.uber.com/deep-neuroevolution/ https://eng.uber.com/deep-neuroevolution/
- yazr 9y ago
  Are these evolutionary techniques considered more or less sample-efficient (and or cpu-efficient) compared to DL with GD ??

jamesblonde 9y ago

Not quite a typical piece - it managed to make it to #2 on HN.

John_KZ 9y ago
I upvote links that promote discussion of interesting concepts, even if the article itself is really bad - like this one. I couldn't bother reading the original article, but designing neural networks via evolutionary algorithms is a very interesting concept I wasn't aware of.

deepnet 9y ago

Thanks for the great link surveying Evolution and NEAT ( Neuroevolution of Neural Nets ) by Ken Stanley who pioneered the method. The OP is by Risto Miikkulainen who was Stanley's collaborator on NEAT and should not be summarily dismissed.

rpm13 9y ago

Note that this blog post is not an article per se, but an overview of a research website (https://sentient.ai/sentient-labs/ea https://sentient.ai/sentient-labs/ea) built around five new research papers. The website offers demos that illustrate neuroevolution and evolutionary computation concepts at a much more concrete level than the papers can.

rpm13 9y ago
For a short intro to neuroevolution there is http://www.scholarpedia.org/article/Neuroevolution http://www.scholarpedia.org/article/Neuroevolution However, these overview articles do not include the newest research on evolving deep learning networks. Three such papers are introduced at https://www.sentient.ai/sentient-labs/ea-1/; https://www.sentient.ai/sentient-labs/ea-1/; there are other recent ones at https://research.googleblog.com/2018/03/using-evolutionary-automl-to-discover.html https://research.googleblog.com/2018/03/using-evolutionary-a... and https://eng.uber.com/deep-neuroevolution/ https://eng.uber.com/deep-neuroevolution/. It is a rapidly developing area.