Rust NEAT

Implementation of NeuroEvolution of Augmenting Topologies NEAT http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf

This implementations uses a Continous-Time Recurrent Neural Network (CTRNN) (Yamauchi and Beer, 1994).

Run test

To speed up tests, run they with --release (XOR classification should take less than a minute)

cargo test --release

Sample usage

Create a new cargo project:

cargo init rustneatsample cd rustneatsample

Add rustneat to Cargo.toml [dependencies] rustneat = "0.1.5"

Remove src/lib.rs and create a src/main.rs with: ``` extern crate rustneat; use rustneat::neat::Environment as Environment; use rustneat::neat::Organism as Organism; use rustneat::neat::Population as Population;

struct XORClassification;

impl Environment for XORClassification{ fn test(&self, organism: &mut Organism) -> f64 { let mut output = vec![0f64]; let mut distance: f64; organism.activate(&vec![0f64,0f64], &mut output); distance = (0f64 - output[0]).abs(); organism.activate(&vec![0f64,1f64], &mut output); distance += (1f64 - output[0]).abs(); organism.activate(&vec![1f64,0f64], &mut output); distance += (1f64 - output[0]).abs(); organism.activate(&vec![1f64,1f64], &mut output); distance += (0f64 - output[0]).abs(); (4f64 - distance).powi(2) } }

fn main(){ let mut population = Population::createpopulation(150); let environment = XORClassification; let mut champion: Option = None; while champion.isnone() { population.evolve(); population.evaluatein(&environment); for organism in &population.getorganisms() { if organism.fitness > 15.9f64 { champion = Some(organism.clone()); } } } println!("{:?}", champion.unwrap().genome); } ```

run the app and wait a minute for the result: cargo run