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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
People
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
Projects
Learning Strategic Behavior in Sequential Decision Tasks 2009 - 2014
NEAT: Evolving Increasingly Complex Neural Network Topologies 2000 - 2011
Publications
     [Expand to show all 22][Minimize]
Neuroevolution: Harnessing Creativity in AI Model Design Sebastian Risi, David Ha, Yujin Tang, Risto Miikkulainen To Appear In , Cambridge, MA, 2025. MIT Press. 2025
Neuroevolution Insights Into Biological Neural Computation Risto Miikkulainen To Appear In Science, 2024. 2024
Neuroevolution Risto Miikkulainen In Dinh Phung, Claude Sammut and Geoffrey I. Webb, editors, Encyclopedia of Machine Learning and ... 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
Demos
Evolving Vehicle Warning SystemsNate Kohl2006
Neuroevolution of Augmenting Topologies DemosKenneth Stanley2003
Areas of Interest
Neuroevolution Robotics Artificial Life
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