Struct RastriginProblem 

pub struct RastriginProblem { /* private fields */ }

Rastrigin: a classic multimodal continuous minimization benchmark.

Minimize: f(x) = 10n + sum(x_i^2 - 10 cos(2 pi x_i))

Variables: x_i in [lower_bound, upper_bound] Global optimum: f(0, …, 0) = 0

Implementations

impl RastriginProblem

pub fn new( number_of_variables: usize, lower_bound: f64, upper_bound: f64, ) -> Self

pub fn new_default() -> Self

pub fn number_of_variables(&self) -> usize

pub fn lower_bound(&self) -> f64

pub fn upper_bound(&self) -> f64

Trait Implementations

impl Clone for RastriginProblem

fn clone(&self) -> RastriginProblem

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Problem<f64> for RastriginProblem

fn dominates( &self, solution_a: &Solution<f64, f64>, solution_b: &Solution<f64, f64>, ) -> bool

The solution that dominates is the one who is near to zero

fn new() -> Self

fn evaluate(&self, solution: &mut Solution<f64>)

Evaluates a solution and updates its quality/fitness

fn better_fitness_fn(&self) -> fn(f64, f64) -> bool

fn create_solution(&self, rng: &mut Random) -> Solution<f64>

Creates a new random solution for this problem that serves as a starting point for the algorithm

fn set_problem_description(&mut self, description: String)

fn get_problem_description(&self) -> String

fn format_solution(&self, solution: &Solution<f64>) -> String

Returns a human-friendly representation for one solution.

fn is_better_fitness(&self, candidate: f64, reference: f64) -> bool

fn non_improving_fitness_loss(&self, current: f64, candidate: f64) -> f64

fn get_problem_parameters_payload(&self) -> String