genetic_algorithm 0.27.2

A genetic algorithm implementation
Documentation 
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use super::Select;
use crate::chromosome::Chromosome;
use crate::fitness::{FitnessOrdering, FitnessValue};
use crate::genotype::EvolveGenotype;
use crate::population::Population;
use crate::strategy::evolve::{EvolveConfig, EvolveState};
use crate::strategy::{StrategyAction, StrategyReporter, StrategyState};
use rand::prelude::*;
use std::cmp::Reverse;
use std::marker::PhantomData;
use std::time::Instant;

/// Sort chromosomes by fitness in a multi-pass process: extract elite, partition into parents and
/// offspring, select from each group separately based on replacement_rate, then do a final
/// selection pass on the combined pool to reach target_population_size. Deterministic, but has the
/// risk of locking in to a local optimum.
#[derive(Clone, Debug)]
pub struct Elite<G: EvolveGenotype> {
    _phantom: PhantomData<G>,
    pub replacement_rate: f32,
    pub elitism_rate: f32,
}

impl<G: EvolveGenotype> Select for Elite<G> {
    type Genotype = G;

    fn call<R: Rng, SR: StrategyReporter<Genotype = G>>(
        &mut self,
        _genotype: &G,
        state: &mut EvolveState<G>,
        config: &EvolveConfig,
        _reporter: &mut SR,
        _rng: &mut R,
    ) {
        let now = Instant::now();

        let mut elite_chromosomes =
            self.extract_elite_chromosomes(state, config, self.elitism_rate);

        #[allow(clippy::type_complexity)]
        let (mut offspring, mut parents): (
            Vec<Chromosome<G::Allele>>,
            Vec<Chromosome<G::Allele>>,
        ) = state
            .population
            .chromosomes
            .drain(..)
            .partition(|c| c.is_offspring());

        let (new_parents_size, new_offspring_size) = self.parent_and_offspring_survival_sizes(
            parents.len(),
            offspring.len(),
            config.target_population_size - elite_chromosomes.len(),
            self.replacement_rate,
        );

        self.selection(
            &mut parents,
            new_parents_size,
            &mut state.population,
            config,
        );
        self.selection(
            &mut offspring,
            new_offspring_size,
            &mut state.population,
            config,
        );

        state.population.chromosomes.append(&mut elite_chromosomes);
        state.population.chromosomes.append(&mut offspring);
        state.population.chromosomes.append(&mut parents);

        // detach and attach chromosomes for general reuse of selection method
        let mut chromosomes = std::mem::take(&mut state.population.chromosomes);
        self.selection(
            &mut chromosomes,
            config.target_population_size,
            &mut state.population,
            config,
        );
        state.population.chromosomes = chromosomes;

        state.add_duration(StrategyAction::Select, now.elapsed());
    }
}

impl<G: EvolveGenotype> Elite<G> {
    /// Create a new Elite selection strategy.
    /// * `replacement_rate` - fraction of population replaced by offspring (0.3-0.7 typical)
    /// * `elitism_rate` - fraction of best chromosomes preserved across generations (0.01-0.05 typical)
    pub fn new(replacement_rate: f32, elitism_rate: f32) -> Self {
        Self {
            _phantom: PhantomData,
            replacement_rate,
            elitism_rate,
        }
    }

    pub fn selection(
        &self,
        chromosomes: &mut Vec<Chromosome<G::Allele>>,
        selection_size: usize,
        population: &mut Population<G::Allele>,
        config: &EvolveConfig,
    ) {
        let selection_size = std::cmp::min(selection_size, chromosomes.len());
        match config.fitness_ordering {
            FitnessOrdering::Maximize => {
                chromosomes.sort_unstable_by_key(|c| match c.fitness_score() {
                    Some(fitness_score) => Reverse(fitness_score),
                    None => Reverse(FitnessValue::MIN),
                });
            }
            FitnessOrdering::Minimize => {
                chromosomes.sort_unstable_by_key(|c| match c.fitness_score() {
                    Some(fitness_score) => fitness_score,
                    None => FitnessValue::MAX,
                });
            }
        }
        population.truncate_external(chromosomes, selection_size);
    }
}