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CmaEsState

fugue_evo::algorithms::cmaes

Struct CmaEsState 

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pub struct CmaEsState {
Show 23 fields
    pub mean: Vec<f64>,
    pub sigma: f64,
    pub covariance: Vec<Vec<f64>>,
    pub path_sigma: Vec<f64>,
    pub path_c: Vec<f64>,
    pub eigenvalues: Vec<f64>,
    pub eigenvectors: Vec<Vec<f64>>,
    pub eigen_eval: usize,
    pub dimension: usize,
    pub lambda: usize,
    pub mu: usize,
    pub weights: Vec<f64>,
    pub mu_eff: f64,
    pub c_1: f64,
    pub c_mu: f64,
    pub c_sigma: f64,
    pub d_sigma: f64,
    pub c_c: f64,
    pub chi_n: f64,
    pub generation: usize,
    pub evaluations: usize,
    pub best_fitness: f64,
    pub best_solution: Vec<f64>,

}
Expand description

CMA-ES state containing all adaptation parameters

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§mean: Vec<f64>

Current mean of the search distribution

§sigma: f64

Global step size (σ)

§covariance: Vec<Vec<f64>>

Covariance matrix C (stored as upper triangular for efficiency)

§path_sigma: Vec<f64>

Evolution path for σ adaptation (p_σ)

§path_c: Vec<f64>

Evolution path for C adaptation (p_c)

§eigenvalues: Vec<f64>

Eigenvalues of C (D²)

§eigenvectors: Vec<Vec<f64>>

Eigenvectors of C (B)

§eigen_eval: usize

Generation counter for eigendecomposition

§dimension: usize

Problem dimension

§lambda: usize

Population size (λ)

§mu: usize

Parent number (μ)

§weights: Vec<f64>

Recombination weights

§mu_eff: f64

Variance effective selection mass (μ_eff)

§c_1: f64

Learning rate for rank-1 update

§c_mu: f64

Learning rate for rank-μ update

§c_sigma: f64

Learning rate for cumulation for σ control

§d_sigma: f64

Damping for σ

§c_c: f64

Learning rate for cumulation for C

§chi_n: f64

Expected length of random vector ||N(0, I)||

§generation: usize

Current generation

§evaluations: usize

Total evaluations

§best_fitness: f64

Best fitness found

§best_solution: Vec<f64>

Best solution found

Implementations§

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impl CmaEsState

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pub fn new( initial_mean: Vec<f64>, initial_sigma: f64, lambda: Option<usize>, ) -> Self

Create a new CMA-ES state

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pub fn sample_population<R: Rng>(&self, rng: &mut R) -> Vec<RealVector>

Sample lambda offspring from the current distribution

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pub fn update(&mut self, offspring: &[(RealVector, f64)])

Update the CMA-ES state based on evaluated offspring

Offspring should be sorted by fitness (best first for minimization).

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pub fn has_converged(&self) -> bool

Check if algorithm has converged

Trait Implementations§

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impl Clone for CmaEsState

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fn clone(&self) -> CmaEsState

Returns a duplicate of the value. Read more
1.0.0 · Source§

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

Performs copy-assignment from source. Read more
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impl Debug for CmaEsState

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

Formats the value using the given formatter. Read more

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