Struct NSGAII 

pub struct NSGAII { /* private fields */ }

NSGA-II (Non-dominated Sorting Genetic Algorithm II) implementation

A state-of-the-art evolutionary algorithm for multi-objective optimization that maintains a diverse set of Pareto-optimal solutions.

Examples

use sklears_multioutput::optimization::evolutionary_multi_objective::NSGAII;
// Use SciRS2-Core for arrays and random number generation (SciRS2 Policy)
use scirs2_core::ndarray::array;

// Define a multi-objective function (minimize both objectives)
let objectives = |x: &scirs2_autograd::scirs2_core::ndarray::ArrayView1`<f64>`| {
    let obj1 = x[0].powi(2) + x[1].powi(2);           // Minimize distance from origin
    let obj2 = (x[0] - 1.0).powi(2) + (x[1] - 1.0).powi(2); // Minimize distance from (1,1)
    array![obj1, obj2]
};

let nsga2 = NSGAII::new()
    .population_size(100)
    .n_generations(50)
    .crossover_probability(0.9)
    .mutation_probability(0.1)
    .variable_bounds(vec![(-2.0, 2.0), (-2.0, 2.0)])
    .random_state(42);

let result = nsga2.optimize(objectives, 2).unwrap();
assert!(result.pareto_front().len() > 0);

Implementations

impl NSGAII

pub fn new() -> Self

Create a new NSGA-II optimizer with default parameters

pub fn population_size(self, size: usize) -> Self

Set the population size

pub fn n_generations(self, generations: usize) -> Self

Set the number of generations

pub fn crossover_probability(self, prob: Float) -> Self

Set the crossover probability

pub fn mutation_probability(self, prob: Float) -> Self

Set the mutation probability

pub fn variable_bounds(self, bounds: Vec<(Float, Float)>) -> Self

Set bounds for decision variables

pub fn random_state(self, seed: u64) -> Self

Set random state for reproducibility

pub fn crossover_eta(self, eta: Float) -> Self

Set crossover distribution index (higher values = more uniform crossover)

pub fn mutation_eta(self, eta: Float) -> Self

Set mutation distribution index (higher values = smaller mutations)

pub fn optimize<F>( &self, objective_fn: F, n_objectives: usize, ) -> SklResult<OptimizationResult>

where F: Fn(&ArrayView1<'_, Float>) -> Array1<Float>,

Optimize a multi-objective function using NSGA-II

Trait Implementations

impl Clone for NSGAII

fn clone(&self) -> NSGAII

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for NSGAII

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for NSGAII

fn default() -> Self

Returns the “default value” for a type.