NEAT Newer Packages: More Recent Releases
Python NEAT Gym by Simon Levy and Coletta Fuller. Makes it possible to train, test, and visualize
OpenAI Gym
environments with the NEAT algorithm and variants including HyperNEAT, ES-HyperNEAT, and novelty search. Requires installation of neat-python and PURPLES (links and instructions are provided).
PyTorch PyTorch NEAT by Alex Gajewski. PyTorch NEAT builds upon NEAT-Python by providing functions that can turn a NEAT-Python genome into either a recurrent PyTorch network or a PyTorch CPPN for use in HyperNEAT or Adaptive HyperNEAT. It also provides environments in which to test NEAT and Adaptive HyperNEAT, and a more involved example using the CPPN infrastructure with Adaptive HyperNEAT on a T-maze.
Go
GoNEAT
by Iaroslav Omelianenko.
This NEAT implementation in Go includes XOR
(classic and with disconnected inputs), single and
double pole-balancing experiments, and both Markovian and
non-Markovian variants of the double pole-balancing version.
Lisp
NEAT-Lisp by Dmitrii Korobeinikov. The first NEAT implementaton in Common Lisp. An XOR experiment and a novel experiment on pushing a ball in a physical simulator are
included.
Elixir
NEAT-Ex by Stuart Hunt. The first NEAT implementaton in the
Elixir language. An XOR experiment and an experiment in
evolving fish called FishSim is included.
Go
NEAT Go
by Scott Hummer.
The first NEAT implementation in the Go language includes
an XOR experiment and network visualizations.
Go
Another NEAT Go
by Jin Yeom.
A newer implementation in the Go language also includes
an XOR experiment.
Lua
NEATEvolve.lua
by SethBling.
This source code implements the "MarI/O" demo of NEAT
evolving a solution to a level of Super Mario World that
received over 1,000,000 views on YouTube in two days in June 2015.
See the video
here.
C++
NEAT Visualizer SFML C++
by Eric Laukien.
Variant of NEAT with implicit speciation instead of the usual
explicit speciation, which means that organisms are not
explcitly grouped into species, but they can only reproduce with other
genes that are similar enough and fitness is modified according to
compatibility. Includes a special visualization system
and XOR.
Ruby
RubyNEAT
by Fred Mitchell. The only NEAT implementation available in
Ruby includes XOR and parity examples.
C# for Unity
UnityNEAT
by Daniel Jallov. For those wanting to integrate NEAT
into a gaming platform: SharpNEAT is ported in this package from
pure C# 4.0 to Unity 4.x and 5 (using Mono 2.6).
A
car
racing demo is included to make getting
started easy.