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

jon trembley <>
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.

thanks,
jon


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 [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] [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] [2]http://www.springer.com/computer/swe/book/978-1-4614-4462-6
>    [3]
>    [3]http://www.amazon.com/Handbook-Neuroevolution-through-Erlang-Gene/dp/1461444624/ref=sr_1_1?ie=UTF8&qid=1338163875&sr=8-1
>    [4]
>    [4]http://www.erlang-factory.com/conference/SFBay2012/speakers/GeneSher
>    [5] [5]http://arxiv.org/abs/1111.5892
> 
> References
> 
>    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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