Rust NEAT

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

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

Run test

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

`cargo test --release`

Run example

cargo run --release --example simple_sample

Sample usage

Create a new cargo project:

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

Then use the library i.e. to implement the above example, use the library as follows:

```rust extern crate rustneat; use rustneat::neat::Environment; use rustneat::neat::Organism; use rustneat::neat::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 mut environment = XORClassification; let mut champion: Option = None; while champion.isnone() { population.evolve(); population.evaluatein(&mut environment); for organism in &population.getorganisms() { if organism.fitness > 15.9f64 { champion = Some(organism.clone()); } } } println!("{:?}", champion.unwrap().genome); }

```