evolve 0.4.0

A generic, composable genetic algorithm framework for Rust
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94

use crate::{
    core::{
        context::Context, individual::Individual, offspring::Offspring, population::Population,
        state::State,
    },
    fitness::FitnessEvaluator,
    operators::GeneticOperator,
};
use rand::{Rng, RngExt};
use std::marker::PhantomData;

/// Mutation operator that duplicates a random segment within a genome.
///
/// The duplicated segment is inserted immediately after its original position.
/// If the resulting genome would exceed `max_genome_len`, the individual is left unchanged.
#[derive(Debug, Clone, Copy)]
pub struct SegmentDuplication<T> {
    max_segment_fraction: f64,
    max_genome_len: usize,
    _marker: PhantomData<T>,
}

impl<T> SegmentDuplication<T> {
    /// Creates a new `SegmentDuplication` operator.
    ///
    /// - `max_segment_fraction` — maximum fraction of the genome to duplicate (0.0..1.0)
    /// - `max_genome_len` — duplication is skipped if it would exceed this length
    pub fn new(max_segment_fraction: f64, max_genome_len: usize) -> Self {
        Self {
            max_segment_fraction,
            max_genome_len,
            _marker: PhantomData,
        }
    }
}

impl<T, F, Fe, R, C> GeneticOperator<Vec<T>, F, Fe, R, C> for SegmentDuplication<T>
where
    T: Clone,
    R: Rng,
    Fe: FitnessEvaluator<Vec<T>, F>,
{
    fn apply(&self, state: &State<Vec<T>, F>, ctx: &mut Context<Fe, R, C>) -> Offspring<Vec<T>, F> {
        let mut population = Population::with_capacity(state.population().len());

        for individual in state.population() {
            let genome = individual.genome();
            let len = genome.len();

            if len == 0 {
                population.add(Individual::new(genome.clone()));
                continue;
            }

            let max_seg = ((len as f64 * self.max_segment_fraction).floor() as usize).max(1);
            let seg_len = ctx.rng().random_range(1..=max_seg);

            if len + seg_len > self.max_genome_len {
                population.add(Individual::new(genome.clone()));
            } else {
                let start = ctx.rng().random_range(0..=(len - seg_len));
                let mut new_genome: vecpool::PoolVec<T> = vecpool::with_capacity(len + seg_len);
                new_genome.extend_from_slice(&genome[..start + seg_len]);
                new_genome.extend_from_slice(&genome[start..start + seg_len]);
                new_genome.extend_from_slice(&genome[start + seg_len..]);
                population.add(Individual::new(new_genome.into_vec()));
            }
        }

        Offspring::Multiple(population)
    }

    fn transform(&self, state: State<Vec<T>, F>, ctx: &mut Context<Fe, R, C>) -> Offspring<Vec<T>, F> {
        let population: Population<Vec<T>, F> = state.into_population().into_iter()
            .map(|ind| ind.mutate_genome(|genome| {
                let len = genome.len();
                if len == 0 {
                    return;
                }
                let max_seg = ((len as f64 * self.max_segment_fraction).floor() as usize).max(1);
                let seg_len = ctx.rng().random_range(1..=max_seg);
                if len + seg_len > self.max_genome_len {
                    return;
                }
                let start = ctx.rng().random_range(0..=(len - seg_len));
                let mut segment: vecpool::PoolVec<T> = vecpool::with_capacity(seg_len);
                segment.extend_from_slice(&genome[start..start + seg_len]);
                genome.splice(start + seg_len..start + seg_len, segment.into_vec());
            }))
            .collect();

        Offspring::Multiple(population)
    }
}