[erlang-questions] DXNN: Topology and Parameter Evolving Universal Learning Network system/platform, released to GitHub.
Sat Jun 16 18:57:47 CEST 2012
This is great! I`ve been waiting for something like this in Erlang for
awhile. Can`t wait to buy the book.
On Tue, Jun 12, 2012 at 01:31:21PM -0400, G.S. 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 ), 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
> 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).
>  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.
>  http://www.springer.com/computer/swe/book/978-1-4614-4462-6
>  http://arxiv.org/abs/1111.5892
> Visible links
> 1. https://github.com/CorticalComputer/DXNN
> 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
> 4. http://www.erlang-factory.com/conference/SFBay2012/speakers/GeneSher
> 5. http://arxiv.org/abs/1111.5892
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