Crate oxineat_nn

OxiNEAT-NN

An neural network-based implementation of the OxiNEAT crate’s Genome trait.

Provides a NNGenome type usable in OxiNEAT Populations, as well as two neural network implementations which can be generated from an NNGenome:

• RealTimeNetwork: best suited for real-time control tasks, with new inputs set for each activation, and multiple time-steps involved.
• FunctionApproximatorNetwork: best suited for more instantaneous single-output-per-input function approximation tasks.

Example usage: Evolution of XOR function approximator

use oxineat::{Population, PopulationConfig};
use oxineat_nn::{
    genomics::{ActivationType, GeneticConfig, NNGenome},
    networks::FunctionApproximatorNetwork,
};
use serde_json;
use std::num::NonZeroUsize;
 
// Allowed error margin for neural net answers.
const ERROR_MARGIN: f32 = 0.3;
 
fn evaluate_xor(genome: &NNGenome) -> f32 {
    let mut network = FunctionApproximatorNetwork::from::<1>(genome);

    let values = [
        ([1.0, 0.0, 0.0], 0.0),
        ([1.0, 0.0, 1.0], 1.0),
        ([1.0, 1.0, 0.0], 1.0),
        ([1.0, 1.0, 1.0], 0.0),
    ];

    let mut errors = [0.0, 0.0, 0.0, 0.0];
    for (i, (input, output)) in values.iter().enumerate() {
        errors[i] = (network.evaluate_at(input)[0] - output).abs();
        if errors[i] < ERROR_MARGIN {
            errors[i] = 0.0;
        }
    }

    (4.0 - errors.iter().copied().sum::<f32>()).powf(2.0)
}

fn main() {
    let genetic_config = GeneticConfig {
        input_count: NonZeroUsize::new(3).unwrap(),
        output_count: NonZeroUsize::new(1).unwrap(),
        activation_types: vec![ActivationType::Sigmoid],
        output_activation_types: vec![ActivationType::Sigmoid],
        child_mutation_chance: 0.65,
        mate_by_averaging_chance: 0.4,
        suppression_reset_chance: 1.0,
        initial_expression_chance: 1.0,
        weight_bound: 5.0,
        weight_reset_chance: 0.2,
        weight_nudge_chance: 0.9,
        weight_mutation_power: 2.5,
        node_addition_mutation_chance: 0.03,
        gene_addition_mutation_chance: 0.05,
        max_gene_addition_mutation_attempts: 20,
        recursion_chance: 0.0,
        excess_gene_factor: 1.0,
        disjoint_gene_factor: 1.0,
        common_weight_factor: 0.4,
        ..GeneticConfig::zero()
    };

    let population_config = PopulationConfig {
        size: NonZeroUsize::new(150).unwrap(),
        distance_threshold: 3.0,
        elitism: 1,
        survival_threshold: 0.2,
        sexual_reproduction_chance: 0.6,
        adoption_rate: 1.0,
        interspecies_mating_chance: 0.001,
        stagnation_threshold: NonZeroUsize::new(15).unwrap(),
        stagnation_penalty: 1.0,
    };
 
    let mut population = Population::new(population_config, genetic_config);
    for _ in 0..100 {
        population.evaluate_fitness(evaluate_xor);
        if (population.champion().fitness() - 16.0).abs() < f32::EPSILON {
            println!("Solution found!: {}", serde_json::to_string(&population.champion()).unwrap());
            break;
        }
        if let Err(e) = population.evolve() {
            eprintln!("{}", e);
            break;
        }
    }
}

Modules

genomics

This implementation of the Genome trait is a collection of genes and nodes that can be instantiated as a phenotype (a neural network). NNGenomes can be progressively mutated, thus adding complexity and functionality.

networks

A network is a simple near-isomorphism of a NNGenome, with suppressed genes being ignored. Genes are converted into connections, and Nodes into network nodes.

Type Definitions

Innovation

Identifier type used to designate historically identical mutations for the purposes of genome comparison and genetic tracking.