neatJax: Fast NeuroEvolution of Augmenting Topologies 🪸

JAX implementation of the Neat (Evolving Neural Networks through Augmenting Topologies) algorithm.

🚀 TO DO

Forward Pass:

• Add connection weights
• Add individual activation functions
• Add conditional activation of output nodes, return output values
• Test forward when a single neuron is linked to multiple receivers
• Test forward pass on larger architectures

Mutations:

• Determine mutation frequency and common practices
• Implement the following mutations:
  • Weight shift
  • Weight reset
  • Add node
  • Add connection
  • Mutate activation
• Wrap all mutations in a single function

Misc:

• Add Hydra config for constant attributes
• Separate max_nodes and max_connections
• Add bias
• Set the minimum sender index to 1 instead of 0

Crossing:

• Add novelty fields to Network dataclass
• Implement crossing for two simple networks
• Create a Species dataclass
• Define a distance metrics between networks to cluster species

📝 References

• Efficient Evolution of Neural Network Topologies, Kenneth O. Stanley and Risto Miikkulainen, 2001