Note to HyperNEAT Users: We now host a HyperNEAT Users Page that provides general information on HyperNEAT.

Software is listed in reverse-chronological order of date of first release.  Some software may have been updated more recently. 

2009

Galactic Arms Race

Galactic Arms Race (GAR) is the first video game to generate its own content.  Every particle weapon in GAR, like the one above, is evolved by the game itself.  Thus GAR is in effect a major experiment in automatic content generation in games.  Now available! (Released 6/2/09)

2008

Novelty Search C++ 1.0

By Joel Lehman ( This e-mail address is being protected from spam bots, you need JavaScript enabled to view it )

This is a public implementation of the novelty search algorithm in C++. Novelty search offers a new perspective on search, harnessing the power of open-ended evolution to solve real world problems. This package includes the maze navigation domain described in this paper.

For questions or suggestions, please email This e-mail address is being protected from spam bots, you need JavaScript enabled to view it .  

2007

Dance Evolution (links to project page)

Interactively evolve dancers to your favorite songs: Dancers in Dance Evolution are controlled by artificial neural networks that evolve interactively with NEAT.  Music from any MIDI song can be input directly into the dancers' neural networks, allowing them to "hear" the rhythm and respond by making up their own moves accordingly.  Dance Evolution is the winner of the Best Student Video Award at the AAAI-07 AI Video Competion.

Picbreeder is a massive online experiment in collaborative interactive evolution.  More than a software package, Picbreeder is actually an online service that allows you to evolve images and branch from other users' creations.  You can produce high quality, high resolution art without any artistic talent or experience.

HyperSharpNEAT 2.0

By David B. D'Ambrosio ( This e-mail address is being protected from spam bots, you need JavaScript enabled to view it )

This package is an update to HyperSharpNEAT 1.0 and is another implementation of the HyperNEAT algorithm in C#.  It is a modification of the SharpNEAT package by Colin Green which is available at http://sharpneat.sourceforge.net/.   This package includes the multiagent predator prey experiment described in this paper.  Improvements over the original version included a multithreading support, the new experiment, and a GUI.

Version 1.0 contains the food gathering experiment from this paper.

For questions, comments, or suggestions, please email This e-mail address is being protected from spam bots, you need JavaScript enabled to view it .

HyperNEAT 2.6 C++ Download (Windows & Linux)

By Jason Gauci ( This e-mail address is being protected from spam bots, you need JavaScript enabled to view it )

This HyperNEAT package contains source code and executables for the HyperNEAT method for evolving large-scale neural networks with regularities and symmetries that exploit task geometry.  This package specifically includes the simple visual discrimination experiment described in this paper, in which working neural networks with millions of connections were produced.  The package also comes with several sample networks (generated by connective Compositional Pattern Producing Networks, i.e. connective CPPNs) included from that experiment  We encourage you to build new experiments with HyperNEAT.

Please note, HyperNEAT version 2.4 and older depend on the Tinyxmldll, which is available here

Version 2.0 (April 2008) contains the checkers experiments mentioned in this paper .

Version 2.2 (July 2008) includes a way to disable the GUI interface when building.

Version 2.3 (August 2008) Has a fix to be backwards compatible with the experiments from version 1.0

Version 2.4 (September 2008) includes more bug fixes and performance enhancements.

Version 2.6 (November 2008) has a fix for crossover

Additional information will be available at Jason Gauci's HyperNEAT support site.

For questions, comments, or suggestions, please email This e-mail address is being protected from spam bots, you need JavaScript enabled to view it .    

NEAT Particles 2.0 

By Erin Hastings ( This e-mail address is being protected from spam bots, you need JavaScript enabled to view it )

NEAT Particles is an interactive content generation tool that uses interactive evolution of NEAT neural networks to evolve a wide variety of particle systems for video games or other media.  It is a demonstration of the potential for evolution to drive practical content generation for real-world applications.

A complete description of this work and its results is available in this publication. 

For questions, comments, or suggestions, please email This e-mail address is being protected from spam bots, you need JavaScript enabled to view it .