evolve 0.4.0

A generic, composable genetic algorithm framework for Rust
Documentation 
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use crate::{
    core::{
        context::Context, individual::Individual, offspring::Offspring, population::Population,
        state::State,
    },
    fitness::FitnessEvaluator,
    operators::GeneticOperator,
};
use rand::{Rng, RngExt};

/// Gaussian mutation operator for continuous-valued genomes.
///
/// Selects a random gene and adds Gaussian noise with the configured
/// standard deviation. Optionally clamps the result to specified bounds.
///
/// # Examples
///
/// ```
/// use evolve::operators::sequential::mutation::Gaussian;
///
/// // Unbounded
/// let mutation = Gaussian::new(0.1);
///
/// // Lower bound only
/// let mutation = Gaussian::new(0.1).min(0.0);
///
/// // Upper bound only
/// let mutation = Gaussian::new(0.1).max(1.0);
///
/// // Both bounds
/// let mutation = Gaussian::new(0.1).min(-1.0).max(1.0);
/// ```
#[derive(Debug, Clone, Copy)]
pub struct Gaussian {
    std_dev: f64,
    min: Option<f64>,
    max: Option<f64>,
}

impl Gaussian {
    /// Creates a new `Gaussian` with the given standard deviation.
    pub fn new(std_dev: f64) -> Self {
        Self { std_dev, min: None, max: None }
    }

    /// Sets the lower bound. Genes are clamped to this minimum after mutation.
    pub fn min(mut self, min: f64) -> Self {
        self.min = Some(min);
        self
    }

    /// Sets the upper bound. Genes are clamped to this maximum after mutation.
    pub fn max(mut self, max: f64) -> Self {
        self.max = Some(max);
        self
    }
}

/// Generates a standard normal random value using the Box-Muller transform.
fn box_muller(rng: &mut impl Rng) -> f64 {
    let u1: f64 = rng.random();
    let u2: f64 = rng.random();
    (-2.0 * u1.ln()).sqrt() * (std::f64::consts::TAU * u2).cos()
}

impl<F, Fe, R, C, const N: usize> GeneticOperator<[f64; N], F, Fe, R, C> for Gaussian
where
    R: Rng,
    Fe: FitnessEvaluator<[f64; N], F>,
{
    fn apply(
        &self,
        state: &State<[f64; N], F>,
        ctx: &mut Context<Fe, R, C>,
    ) -> Offspring<[f64; N], F> {
        let mut population = Population::with_capacity(state.population().len());
        for individual in state.population() {
            let mut genome = *individual.genome();
            let idx = ctx.rng().random_range(0..N);
            genome[idx] += box_muller(ctx.rng()) * self.std_dev;
            if let Some(min) = self.min { genome[idx] = genome[idx].max(min); }
            if let Some(max) = self.max { genome[idx] = genome[idx].min(max); }
            population.add(Individual::new(genome));
        }
        Offspring::Multiple(population)
    }

    fn transform(
        &self,
        state: State<[f64; N], F>,
        ctx: &mut Context<Fe, R, C>,
    ) -> Offspring<[f64; N], F> {
        let std_dev = self.std_dev;
        let min = self.min;
        let max = self.max;
        let population = state
            .into_population()
            .into_iter()
            .map(|ind| {
                ind.mutate_genome(|genome| {
                    let idx = ctx.rng().random_range(0..N);
                    genome[idx] += box_muller(ctx.rng()) * std_dev;
                    if let Some(lo) = min { genome[idx] = genome[idx].max(lo); }
                    if let Some(hi) = max { genome[idx] = genome[idx].min(hi); }
                })
            })
            .collect();
        Offspring::Multiple(population)
    }
}

impl<F, Fe, R, C> GeneticOperator<Vec<f64>, F, Fe, R, C> for Gaussian
where
    R: Rng,
    Fe: FitnessEvaluator<Vec<f64>, F>,
{
    fn apply(
        &self,
        state: &State<Vec<f64>, F>,
        ctx: &mut Context<Fe, R, C>,
    ) -> Offspring<Vec<f64>, F> {
        let mut population = Population::with_capacity(state.population().len());
        for individual in state.population() {
            let mut genome = individual.genome().clone();
            let idx = ctx.rng().random_range(0..genome.len());
            genome[idx] += box_muller(ctx.rng()) * self.std_dev;
            if let Some(min) = self.min { genome[idx] = genome[idx].max(min); }
            if let Some(max) = self.max { genome[idx] = genome[idx].min(max); }
            population.add(Individual::new(genome));
        }
        Offspring::Multiple(population)
    }

    fn transform(
        &self,
        state: State<Vec<f64>, F>,
        ctx: &mut Context<Fe, R, C>,
    ) -> Offspring<Vec<f64>, F> {
        let std_dev = self.std_dev;
        let min = self.min;
        let max = self.max;
        let population = state
            .into_population()
            .into_iter()
            .map(|ind| {
                ind.mutate_genome(|genome| {
                    let idx = ctx.rng().random_range(0..genome.len());
                    genome[idx] += box_muller(ctx.rng()) * std_dev;
                    if let Some(lo) = min { genome[idx] = genome[idx].max(lo); }
                    if let Some(hi) = max { genome[idx] = genome[idx].min(hi); }
                })
            })
            .collect();
        Offspring::Multiple(population)
    }
}

impl<F, Fe, R, C, const N: usize> GeneticOperator<[f32; N], F, Fe, R, C> for Gaussian
where
    R: Rng,
    Fe: FitnessEvaluator<[f32; N], F>,
{
    fn apply(
        &self,
        state: &State<[f32; N], F>,
        ctx: &mut Context<Fe, R, C>,
    ) -> Offspring<[f32; N], F> {
        let mut population = Population::with_capacity(state.population().len());
        for individual in state.population() {
            let mut genome = *individual.genome();
            let idx = ctx.rng().random_range(0..N);
            genome[idx] += (box_muller(ctx.rng()) * self.std_dev) as f32;
            if let Some(min) = self.min { genome[idx] = genome[idx].max(min as f32); }
            if let Some(max) = self.max { genome[idx] = genome[idx].min(max as f32); }
            population.add(Individual::new(genome));
        }
        Offspring::Multiple(population)
    }

    fn transform(
        &self,
        state: State<[f32; N], F>,
        ctx: &mut Context<Fe, R, C>,
    ) -> Offspring<[f32; N], F> {
        let std_dev = self.std_dev;
        let min = self.min;
        let max = self.max;
        let population = state
            .into_population()
            .into_iter()
            .map(|ind| {
                ind.mutate_genome(|genome| {
                    let idx = ctx.rng().random_range(0..N);
                    genome[idx] += (box_muller(ctx.rng()) * std_dev) as f32;
                    if let Some(lo) = min { genome[idx] = genome[idx].max(lo as f32); }
                    if let Some(hi) = max { genome[idx] = genome[idx].min(hi as f32); }
                })
            })
            .collect();
        Offspring::Multiple(population)
    }
}

impl<F, Fe, R, C> GeneticOperator<Vec<f32>, F, Fe, R, C> for Gaussian
where
    R: Rng,
    Fe: FitnessEvaluator<Vec<f32>, F>,
{
    fn apply(
        &self,
        state: &State<Vec<f32>, F>,
        ctx: &mut Context<Fe, R, C>,
    ) -> Offspring<Vec<f32>, F> {
        let mut population = Population::with_capacity(state.population().len());
        for individual in state.population() {
            let mut genome = individual.genome().clone();
            let idx = ctx.rng().random_range(0..genome.len());
            genome[idx] += (box_muller(ctx.rng()) * self.std_dev) as f32;
            if let Some(min) = self.min { genome[idx] = genome[idx].max(min as f32); }
            if let Some(max) = self.max { genome[idx] = genome[idx].min(max as f32); }
            population.add(Individual::new(genome));
        }
        Offspring::Multiple(population)
    }

    fn transform(
        &self,
        state: State<Vec<f32>, F>,
        ctx: &mut Context<Fe, R, C>,
    ) -> Offspring<Vec<f32>, F> {
        let std_dev = self.std_dev;
        let min = self.min;
        let max = self.max;
        let population = state
            .into_population()
            .into_iter()
            .map(|ind| {
                ind.mutate_genome(|genome| {
                    let idx = ctx.rng().random_range(0..genome.len());
                    genome[idx] += (box_muller(ctx.rng()) * std_dev) as f32;
                    if let Some(lo) = min { genome[idx] = genome[idx].max(lo as f32); }
                    if let Some(hi) = max { genome[idx] = genome[idx].min(hi as f32); }
                })
            })
            .collect();
        Offspring::Multiple(population)
    }
}