[erlang-questions] DXNN: Topology and Parameter Evolving Universal Learning Network system/platform, released to GitHub.

Shahrdad Shadab shahrdad1@REDACTED
Fri Jun 15 15:52:53 CEST 2012

Grate work! I cannot wait to read your book.
 I am also working on using Erlang in statistical machine learning, this
requires of mathematical/statistical
library functions (like linear algebra / statistical libraries and so on)
to be implemented in Erlang which is taking a lot of time from me.
I wonder if you ever looked at statistical approach to AI and why you
didn't follow that path as opposed to neuron-genetic approach.

Thanks a lot
Best regards

On Tue, Jun 12, 2012 at 1:31 PM, G.S. <corticalcomputer@REDACTED> wrote:

> Hello all,
> DXNN [1,4]  is Topology and Parameter Evolving Universal Learning Network
> (TPEULN) system, similar to topology and weight evolving artificial neural
> network, but more general, and not constrained to the use of only sigmoid
> based activation function neurons. Erlang was chosen because of its perfect
> and complete mapping to the neural network architecture.
> DXNN is a TPEULN platform that uses direct and indirect encoding (neural
> and substrate respectively [5]), has a cross-validation system for
> experimentation, decoupled sensor/actuator systems, decoupled
> learning/selection/... algorithms (in MK2), a built in 2d world simulator
> called flatland for ALife experiments (all in gs()).
> The second generation (mk2) DXNN is available as a branch of the original
> project, and is a clean implementation of this computational intelligence
> evolving system. It is also the system explained and created in my Springer
> book: Handbook of Neuroevolution Through Erlang [2,3], with a foreword
> written by Joe Armstrong. The book will go into print this September.
> There are not a lot of comments within the source code on github, but I
> will continue to add more comments as time permits.
> Upcoming features:
> 1. Visualisation system.
> 2. New selection algorithm modules.
> 3. New speciation and diversification functions.
> 4. An improved cross-validation system for the experiment database.
> 5. Full population backup, so that all agents are saved, and only manually
> deleted at the researcher's request (they don't take much space, and it
> would make for an interesting visualisation, and ability to traverse from
> the seed agent to the current agent).
> -Gene
> [1] https://github.com/CorticalComputer/DXNN First generation DXNN has a
> convoluted implementation. DXNN mk2 is a very clean implementation and is
> currently on the non master branch, it will eventually overwrite the master
> branch but both have the same features (almost) at this time.
> [2] http://www.springer.com/computer/swe/book/978-1-4614-4462-6
> [3]
> http://www.amazon.com/Handbook-Neuroevolution-through-Erlang-Gene/dp/1461444624/ref=sr_1_1?ie=UTF8&qid=1338163875&sr=8-1<https://github.com/CorticalComputer/DXNN>
> [4] http://www.erlang-factory.com/conference/SFBay2012/speakers/GeneSher
> [5] http://arxiv.org/abs/1111.5892
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Software Architect & Computer Scientist
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