NeuroEvolution of Augmented Topologies - Definition and Overview

Neuro-Evolution of Augmenting Topologies (NEAT) is a genetic algorithm for evolving neural networks written by Ken Stanley at University of Texas at Austin and published under the GPL; it integrates with Guile, a GNU common lisp interpreter.

External links

• Ken Stanley's website (http://www.cs.utexas.edu/users/kstanley/)
• NEAT Homepage (http://nn.cs.utexas.edu/project-view.php?RECORD_KEY(Projects)=ProjID&ProjID(Projects)=14)
• Evolving Adaptive Neural Networks with and without Adaptive Synapses (http://nn.cs.utexas.edu/downloads/papers/stanley.geccolbp03.pdf)
• download NEAT 1.1 (http://nn.cs.utexas.edu/downloads/software/neat.1.1.tar.gz)