Struct Rosenbrock 

pub struct Rosenbrock { /* private fields */ }

Rosenbrock function: f(x) = Σ[100(xᵢ₊₁-xᵢ²)² + (1-xᵢ)²]

Valley structure, non-separable. Optimum at (1,1,…,1).

Implementations

impl Rosenbrock

pub fn new(dimension: usize) -> Self

Create a new Rosenbrock function

Trait Implementations

impl BenchmarkFunction for Rosenbrock

fn name(&self) -> &'static str

Name of the benchmark function

fn dimension(&self) -> usize

Dimensionality of the problem

fn bounds(&self) -> (f64, f64)

Search space bounds (min, max)

fn optimal_fitness(&self) -> f64

Optimal (minimum) fitness value

fn optimal_solution(&self) -> Option<Vec<f64>>

Optimal solution (if known)

fn evaluate_raw(&self, x: &[f64]) -> f64

Evaluate the function (returns value to be MINIMIZED)

impl Clone for Rosenbrock

fn clone(&self) -> Rosenbrock

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for Rosenbrock

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

Formats the value using the given formatter.

impl Fitness for Rosenbrock

type Genome = RealVector

The genome type being evaluated

type Value = f64

The fitness value type

fn evaluate(&self, genome: &Self::Genome) -> f64

Evaluate fitness (higher = better by convention)

fn as_log_likelihood(&self, genome: &Self::Genome, temperature: f64) -> f64

Convert fitness to log-likelihood for probabilistic selection

fn gradient(&self, _genome: &Self::Genome) -> Option<Vec<f64>>

Optional: Provide gradient for gradient-assisted mutation