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

[G.S.]

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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