[peterstoziek]

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-kin...

I enjoyed it much more than - what feels like - a quickly thrown together marketing piece with no real value for the reader.

[deepnet]

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]

Note that this blog post is not an article per se, but an overview of a research website (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]

For a short intro to neuroevolution there is 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/; there are other recent ones at https://research.googleblog.com/2018/03/using-evolutionary-a... and https://eng.uber.com/deep-neuroevolution/. It is a rapidly developing area.

[vanderZwan]

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

[ofrancon]

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

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/

Risto Miikkulainen's Evolving Deep Neural Networks paper: 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/

[yazr]

Are these evolutionary techniques considered more or less sample-efficient (and or cpu-efficient) compared to DL with GD ??

[ericjang]

Here's a recent survey / observational science paper by some prominent "neuroevolution" / A-Life researchers. 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.

[peterstoziek]

Unfortunately not in this domain.

[jamesblonde]

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

[John_KZ]

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.