neat

Implementation of the NEAT algorithm using genetic-rs.

Features

• serde - Implements Serialize and Deserialize on most of the types in this crate.

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How To Use

The NeuralNetwork<I, O> struct is the main type exported by this crate. The I is the number of input neurons, and O is the number of output neurons. It implements GenerateRandom, RandomlyMutable, Mitosis, and Crossover, with a lot of customizability. This means that you can use it standalone as your organism's entire genome:

use neat::*;

fn fitness(net: &NeuralNetwork<5, 6>) -> f32 {
    // ideally you'd test multiple times for consistency,
    // but this is just a simple example.
    // it's also generally good practice to normalize your inputs between -1..1,
    // but NEAT is usually flexible enough to still work anyways
    let inputs = [1.0, 2.0, 3.0, 4.0, 5.0];
    let outputs = net.predict(inputs);

    // simple fitness: sum of outputs
    // you should replace this with a real fitness test
    outputs.iter().sum()
}

fn main() {
    let mut rng = rand::rng();
    let mut sim = GeneticSim::new(
        Vec::gen_random(&mut rng, 100),
        FitnessEliminator::new_without_observer(fitness),
        CrossoverRepopulator::new(0.25, ReproductionSettings::default()),
    );

    sim.perform_generations(100);
}

Or just a part of a more complex genome:

use neat::*;

#[derive(Clone, Debug)]
struct PhysicalStats {
    strength: f32,
    speed: f32,
    // ...
}

// ... implement `RandomlyMutable`, `GenerateRandom`, `Crossover`, `Default`, etc.

#[derive(Clone, Debug, GenerateRandom, RandomlyMutable, Mitosis, Crossover)]
#[randmut(create_context(name = MyGenomeMutate, derive(Default, Clone, Debug)))]
#[mitosis(create_context(name = MyGenomeReproduce, derive(Default, Clone, Debug)))]
#[crossover(with_context = MyGenomeReproduce)]
struct MyGenome {
    brain: NeuralNetwork<4, 2>,
    stats: PhysicalStats,
}

fn fitness(genome: &MyGenome) -> f32 {
    let inputs = [1.0, 2.0, 3.0, 4.0];
    let outputs = genome.brain.predict(inputs);
    // fitness uses both brain output and stats
    outputs.iter().sum::<f32>() + genome.stats.strength + genome.stats.speed
}

// main is the exact same as before
fn main() {
    let mut rng = rand::rng();
    let mut sim = GeneticSim::new(
        Vec::gen_random(&mut rng, 100),
        FitnessEliminator::new_without_observer(fitness),
        CrossoverRepopulator::new(0.25, MyGenomeReproduce::default()),
    );

    sim.perform_generations(100);
}

If you want more in-depth examples, look at the examples. You can also check out the genetic-rs docs to see what other options you have to customize your genetic simulation.

License

This crate falls under the MIT license