genetic_algorithms 2.2.0

Library for solving genetic algorithm problems
Documentation 
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/// Direction of optimization for a single objective.
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
pub enum ObjectiveDirection {
    /// Minimize this objective (lower is better).
    Minimize,
    /// Maximize this objective (higher is better).
    Maximize,
}

/// Configuration for the NSGA-II multi-objective genetic algorithm.
///
/// # Examples
///
/// ```
/// use genetic_algorithms::nsga2::configuration::{Nsga2Configuration, ObjectiveDirection};
///
/// let config = Nsga2Configuration::new()
///     .with_num_objectives(2)
///     .with_population_size(100)
///     .with_max_generations(500)
///     .with_objective_directions(vec![
///         ObjectiveDirection::Minimize,
///         ObjectiveDirection::Maximize,
///     ]);
///
/// assert_eq!(config.num_objectives, 2);
/// assert_eq!(config.population_size, 100);
/// assert_eq!(config.max_generations, 500);
/// assert_eq!(config.objective_directions.len(), 2);
/// ```
#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
pub struct Nsga2Configuration {
    /// Number of objective functions.
    pub num_objectives: usize,
    /// Population size.
    pub population_size: usize,
    /// Maximum number of generations.
    pub max_generations: usize,
    /// Per-objective optimization direction. If empty, all objectives default to `Minimize`.
    /// When set, the length must match `num_objectives`.
    pub objective_directions: Vec<ObjectiveDirection>,
}

impl Default for Nsga2Configuration {
    fn default() -> Self {
        Nsga2Configuration {
            num_objectives: 2,
            population_size: 100,
            max_generations: 200,
            objective_directions: Vec::new(),
        }
    }
}

impl Nsga2Configuration {
    /// Creates a new `Nsga2Configuration` with default values.
    pub fn new() -> Self {
        Self::default()
    }

    /// Sets the number of objectives.
    pub fn with_num_objectives(mut self, n: usize) -> Self {
        self.num_objectives = n;
        self
    }

    /// Sets the population size.
    pub fn with_population_size(mut self, size: usize) -> Self {
        self.population_size = size;
        self
    }

    /// Sets the maximum number of generations.
    pub fn with_max_generations(mut self, gens: usize) -> Self {
        self.max_generations = gens;
        self
    }

    /// Sets the per-objective optimization directions.
    ///
    /// The length of `directions` must match `num_objectives` at validation time.
    /// If not set, all objectives default to `Minimize`.
    pub fn with_objective_directions(mut self, directions: Vec<ObjectiveDirection>) -> Self {
        self.objective_directions = directions;
        self
    }

    /// Returns the effective directions, defaulting to `Minimize` for each objective
    /// when `objective_directions` is empty.
    pub fn effective_directions(&self) -> Vec<ObjectiveDirection> {
        if self.objective_directions.is_empty() {
            vec![ObjectiveDirection::Minimize; self.num_objectives]
        } else {
            self.objective_directions.clone()
        }
    }
}