Implementation of the NEAT algorithm using genetic-rs
.
- rayon - Uses parallelization on the
NeuralNetwork
struct and adds therayon
feature to thegenetic-rs
re-export. - serde - Adds the NNTSerde struct and allows for serialization of
NeuralNetworkTopology
- crossover - Implements the
CrossoverReproduction
trait onNeuralNetworkTopology
and adds thecrossover
feature to thegenetic-rs
re-export.
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When working with this crate, you'll want to use the NeuralNetworkTopology
struct in your agent's DNA and
the use NeuralNetwork::from
when you finally want to test its performance. The genetic-rs
crate is also re-exported with the rest of this crate.
Here's an example of how one might use this crate:
use neat::*;
#[derive(Clone, RandomlyMutable, DivisionReproduction)]
struct MyAgentDNA {
network: NeuralNetworkTopology<1, 2>,
}
impl GenerateRandom for MyAgentDNA {
fn gen_random(rng: &mut impl rand::Rng) -> Self {
Self {
network: NeuralNetworkTopology::new(0.01, 3, rng),
}
}
}
struct MyAgent {
network: NeuralNetwork<1, 2>,
// ... other state
}
impl From<&MyAgentDNA> for MyAgent {
fn from(value: &MyAgentDNA) -> Self {
Self {
network: NeuralNetwork::from(&value.network),
}
}
}
fn fitness(dna: &MyAgentDNA) -> f32 {
// agent will simply try to predict whether a number is greater than 0.5
let mut agent = MyAgent::from(dna);
let mut rng = rand::thread_rng();
let mut fitness = 0;
// use repeated tests to avoid situational bias and some local maximums, overall providing more accurate score
for _ in 0..10 {
let n = rng.gen::<f32>();
let above = n > 0.5;
let res = agent.network.predict([n]);
let resi = res.iter().max_index();
if resi == 0 ^ above {
// agent did not guess correctly, punish slightly (too much will hinder exploration)
fitness -= 0.5;
continue;
}
// agent guessed correctly, they become more fit.
fitness += 3.;
}
fitness
}
fn main() {
let mut rng = rand::thread_rng();
let mut sim = GeneticSim::new(
Vec::gen_random(&mut rng, 100),
fitness,
division_pruning_nextgen,
);
// simulate 100 generations
for _ in 0..100 {
sim.next_generation();
}
// display fitness results
let fits: Vec<_> = sim.entities
.iter()
.map(fitness)
.collect();
dbg!(&fits, fits.iter().max());
}
This crate falls under the MIT
license