moo-rs

Evolution is a mystery

Overview

moo-rs is a project for solving multi-objective optimization problems with evolutionary algorithms, combining:

• moors: a pure-Rust crate for high-performance implementations of genetic algorithms
• pymoors: a Python extension crate (via pyo3) exposing moors algorithms with a Pythonic API

Inspired by the amazing Python project pymoo, moo-rs delivers both the speed of Rust and the ease-of-use of Python.

Quickstart

The knapsack problem is a classic combinatorial optimization challenge where you must select a subset of items, each with a given weight and value, to include in a fixed‐capacity knapsack so as to maximize total value. It requires balancing the trade‐off between item weights and values under a strict weight constraint.

In this small example, the algorithm finds a single solution on the Pareto front: selecting the items (A, D, E), with a profit of 7 and a quality of 15. This means there is no other combination that can match or exceed both objectives without exceeding the knapsack capacity (7).

RustPython

use ndarray::{Array1, Array2, Axis, stack};

use moors::{
    algorithms::{AlgorithmError, Nsga2Builder},
    duplicates::ExactDuplicatesCleaner,
    operators::{
        crossover::SinglePointBinaryCrossover, mutation::BitFlipMutation,
        sampling::RandomSamplingBinary,
    },
};

// problem data
const WEIGHTS: [f64; 5] = [12.0, 2.0, 1.0, 4.0, 10.0];
const VALUES: [f64; 5] = [4.0, 2.0, 1.0, 5.0, 3.0];
const CAPACITY: f64 = 15.0;

/// Compute multi-objective fitness [–total_value, total_weight]
/// Returns an Array2<f64> of shape (population_size, 2)
fn fitness_knapsack(population_genes: &Array2<f64>) -> Array2<f64> {
    let weights_arr = Array1::from_vec(WEIGHTS.to_vec());
    let values_arr = Array1::from_vec(VALUES.to_vec());

    let total_values = population_genes.dot(&values_arr);
    let total_weights = population_genes.dot(&weights_arr);

    // stack two columns: [-total_values, total_weights]
    stack(Axis(1), &[(-&total_values).view(), total_weights.view()]).expect("stack failed")
}

fn constraints_knapsack(population_genes: &Array2<f64>) -> Array1<f64> {
    let weights_arr = Array1::from_vec(WEIGHTS.to_vec());
    population_genes.dot(&weights_arr) - CAPACITY
}

fn main() -> Result<(), AlgorithmError> {
    // build the NSGA-II algorithm
    let mut algorithm = Nsga2Builder::default()
        .fitness_fn(fitness_knapsack)
        .constraints_fn(constraints_knapsack)
        .sampler(RandomSamplingBinary::new())
        .crossover(SinglePointBinaryCrossover::new())
        .mutation(BitFlipMutation::new(0.5))
        .duplicates_cleaner(ExactDuplicatesCleaner::new())
        .num_vars(5)
        .population_size(100)
        .crossover_rate(0.9)
        .mutation_rate(0.1)
        .num_offsprings(32)
        .num_iterations(2)
        .build()?;

    algorithm.run()?;
    let population = algorithm.population()?;
    println!("Done! Population size: {}", population.len());

    Ok(())
}

import numpy as np

from pymoors import (
    Nsga2,
    RandomSamplingBinary,
    BitFlipMutation,
    SinglePointBinaryCrossover,
    ExactDuplicatesCleaner,
)
from pymoors.typing import TwoDArray


PROFITS = np.array([2, 3, 6, 1, 4])
QUALITIES = np.array([5, 2, 1, 6, 4])
WEIGHTS = np.array([2, 3, 6, 2, 3])
CAPACITY = 7


def knapsack_fitness(genes: TwoDArray) -> TwoDArray:
    # Calculate total profit
    profit_sum = np.sum(PROFITS * genes, axis=1, keepdims=True)
    # Calculate total quality
    quality_sum = np.sum(QUALITIES * genes, axis=1, keepdims=True)

    # We want to maximize profit and quality,
    # so in pymoors we minimize the negative values
    f1 = -profit_sum
    f2 = -quality_sum
    return np.column_stack([f1, f2])


def knapsack_constraint(genes: TwoDArray) -> TwoDArray:
    # Calculate total weight
    weight_sum = np.sum(WEIGHTS * genes, axis=1, keepdims=True)
    # Inequality constraint: weight_sum <= capacity
    return weight_sum - CAPACITY


algorithm = Nsga2(
    sampler=RandomSamplingBinary(),
    crossover=SinglePointBinaryCrossover(),
    mutation=BitFlipMutation(gene_mutation_rate=0.5),
    fitness_fn=knapsack_fitness,
    constraints_fn=knapsack_constraint,
    duplicates_cleaner=ExactDuplicatesCleaner(),
    n_vars=5,
    population_size=32,
    num_offsprings=32,
    num_iterations=10,
    mutation_rate=0.1,
    crossover_rate=0.9,
    keep_infeasible=False,
)

algorithm.run()
pop = algorithm.population
# Get genes
>>> pop.genes
array([[1., 0., 0., 1., 1.],
       [0., 1., 0., 0., 1.],
       [1., 1., 0., 1., 0.],
       ...])
# Get fitness
>>> pop.fitness
array([[ -7., -15.],
       [ -7.,  -6.],
       [ -6., -13.],
       ...])
# Get constraints evaluation
>>> pop.constraints
array([[ 0.],
       [-1.],
       [ 0.],
       ...])
# Get rank
>>> pop.rank
array([0, 1, 1, 2, ...], dtype=uint64)
# Get best individuals
>>> pop.best
[<pymoors.schemas.Individual object at 0x...>]
>>> pop.best[0].genes
array([1., 0., 0., 1., 1.])
>>> pop.best[0].fitness
array([ -7., -15.])
>>> pop.best[0].constraints
array([0.])