NEAT C++
Released 2010
The NEAT package contains source code implementing the NeuroEvolution of Augmenting Topologies method. The source code is written in C++. NEAT is a method for evolving speciated neural networks of arbitrary structures and sizes. NEAT leverages the evolution of structure to make neuroevolution more efficient. For more information on NEAT, see the original publication or our Neuroevolution page.

The package includes implementations of experiments for XOR, single pole balancing, and both Markovian and non-Markovian double pole balancing.

For answers to common questions, refer to our FAQ .

Please contact kstanley@cs.utexas.edu for comments, including ideas or plans for expanding the open source software.

Versions:

  • v1.0 8/16/01 kstanley
  • v1.1 7/14/02 kstanley
    • removed extraneous files from package
    • fixed array bound error
    • made text output default on instead of off
  • v1.2 7/19/10 kstanley & ikarpov
    • Re-release package under Apache 2.0 license per authors' request
    • Fix compilation errors with GCC 4.x.x and above
    • Make sure neat requires the paramfile command line argument.
    • Update README file
  • v1.2.1 8/20/11 erkin
    • Fix typo in NNode::depth() causing max_depth to be calculated incorrectly.
    • Fix Makefile optimization flag and a few minor memory issues.
    • Fix a performance regression with the Markovian double pole balancing experiment, mainly by increasing weight caps.
    • Consider failed runs when printing the average number of evaluations.
Download:
ZIP, TAR
Erkin Bahceci Ph.D. Alumni erkin [at] cs utexas edu
Thomas D'Silva Masters Alumni twdsilva [at] gmail com
Igor V. Karpov Masters Alumni ikarpov [at] gmail com
Kenneth Stanley Postdoctoral Alumni kstanley [at] cs ucf edu
     [Expand to show all 20][Minimize]
Neuroevolution Risto Miikkulainen To Appear In Dinh Phung, Claude Sammut and Geoffrey I. Webb, editors, Encyclopedia of Machine Lea... 2022

Tradeoffs in Neuroevolutionary Learning-Based Real-Time Robotic Task Design in the Imprecise Computation Framework Pei-Chi Huang, Luis Sentis, Joel Lehman, Chien-Liang Fok, Aloysius K. Mok, Risto Miikkulainen ACM Transactions on Cyber-Physical Systems, 3, 2019. DOI 0.1145/3178903. 2019

Neuroevolution Risto Miikkulainen In Sammut, C. and Webb, G. I., editors, Encyclopedia of Machine Learning, 2nd Edition, Berlin... 2015

IJCNN-2013 Tutorial on Evolution of Neural Networks Risto Miikkulainen To Appear In 2013. Tutorial slides.. 2013

Multiagent Learning through Neuroevolution Risto Miikkulainen, Eliana Feasley, Leif Johnson, Igor Karpov, Padmini Rajagopalan, Aditya Rawal, an... In J. Liu et al., editors, Advances in Computational Intelligence, LNCS 7311, 24-46, Berlin, ... 2012

Evolving Explicit Opponent Models for Game Play Alan Lockett, Charles Chen, and Risto Miikkulainen In Genetic and Evolutionary Computation Conference (GECCO-2007), 2007. 2007

Computational Intelligence in Games Risto Miikkulainen, Bobby D. Bryant, Ryan Cornelius, Igor V. Karpov, Kenneth O. Stanley, and Chern H... In Gary Y. Yen and David B. Fogel, editors, Computational Intelligence: Principles and Practice<... 2006

Creating Intelligent Agents in Games Risto Miikkulainen The Bridge:5-13, 2006. 2006

Evolving a Real-World Vehicle Warning System Nate Kohl, Kenneth Stanley, Risto Miikkulainen, Michael Samples, and Rini Sherony In Proceedings of the Genetic and Evolutionary Computation Conference, 2006. 2006

Evolving Robot Arm Controllers Using the NEAT Neuroevolution Method Thomas W. D'Silva Masters Thesis, Department of Electrical and Computer Engineering, The University of Texas at Austin... 2006

Coevolution of Neural Networks Using a Layered Pareto Archive German A. Monroy Masters Thesis, Department of Computer Sciences, The University of Texas at Austin, Austin, TX, 2005... 2005

Neuroevolution of an Automobile Crash Warning System Kenneth Stanley, Nate Kohl, Rini Sherony, and Risto Miikkulainen In Proceedings of the Genetic and Evolutionary Computation Conference, 2005. 2005

Competitive Coevolution through Evolutionary Complexification Kenneth O. Stanley and Risto Miikkulainen Journal of Artificial Intelligence Research, 21:63-100, 2004. 2004

Evolving a Roving Eye for Go Kenneth O. Stanley and Risto Miikkulainen In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2004), Berlin, 2... 2004

Continual Coevolution Through Complexification Kenneth O. Stanley and Risto Miikkulainen In William B. Langdon and Erick Cantu-Paz and Keith E. Mathias and Rajkumar Roy and David Davis and ... 2002

Efficient Evolution Of Neural Network Topologies Kenneth O. Stanley and Risto Miikkulainen In William B. Langdon and Erick Cantu-Paz and Keith E. Mathias and Rajkumar Roy and David Davis and ... 2002

Efficient Reinforcement Learning Through Evolving Neural Network Topologies Kenneth O. Stanley and Risto Miikkulainen In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), 9, San Fr... 2002

Evolving Neural Networks Through Augmenting Topologies Kenneth O. Stanley and Risto Miikkulainen Evolutionary Computation, 10(2):99-127, 2002. 2002

Neuroevolution through Augmenting Topologies Applied to Evolving Neural Networks to Play Othello Timothy Andersen Technical Report HR-02-01, Department of Computer Sciences, The University of Texas at Austin, 2002. 2002

Cooperative Coevolution of Multi-Agent Systems Chern Han Yong Technical Report HR-00-01, Department of Computer Sciences, The University of Texas at Austin, 2000. 2000