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::*;fnfitness(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 anywayslet 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()}fnmain(){letmut rng = rand::rng();letmut 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)]structPhysicalStats{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)]structMyGenome{brain:NeuralNetwork<4,2>,stats:PhysicalStats,}fnfitness(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 beforefnmain(){letmut rng = rand::rng();letmut 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