Struct RandomSearch 

pub struct RandomSearch<I> {
    pub config: RandomSearchConfig,
    pub initializer: I,
}

Sample-evaluate-keep baseline optimizer.

Each iteration the configured Initializer produces batch_size decisions which are evaluated and pushed into the population. Cheap, parallelism-free, and useful as a sanity-check baseline.

Example

use heuropt::prelude::*;

struct Sphere;
impl Problem for Sphere {
    type Decision = Vec<f64>;
    fn objectives(&self) -> ObjectiveSpace {
        ObjectiveSpace::new(vec![Objective::minimize("f")])
    }
    fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
        Evaluation::new(vec![x.iter().map(|v| v * v).sum::<f64>()])
    }
}

let mut opt = RandomSearch::new(
    RandomSearchConfig { iterations: 200, batch_size: 10, seed: 42 },
    RealBounds::new(vec![(-5.0, 5.0); 3]),
);
let r = opt.run(&Sphere);
assert_eq!(r.evaluations, 200 * 10);
assert!(r.best.is_some());

Fields

config: RandomSearchConfig

Algorithm configuration.

initializer: I

Decision-sampling strategy.

Implementations

impl<I> RandomSearch<I>

pub fn new(config: RandomSearchConfig, initializer: I) -> Self

Construct a RandomSearch from its config and initializer.

Trait Implementations

impl<I> AlgorithmInfo for RandomSearch<I>

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

Canonical short algorithm name — e.g. "NSGA-II", "DE", "CMA-ES". This is the form that should appear in tables, plot legends, and exported JSON metadata.

fn seed(&self) -> Option<u64>

The deterministic seed driving this run, if the algorithm uses one. Default: None. Built-in algorithms return Some(self.config.seed).

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

Academic long name, expanded — e.g. "Non-dominated Sorting Genetic Algorithm II". Defaults to name() for algorithms whose short and long forms coincide (Random Search, Hill Climber, Tabu Search, Hyperband, …).

impl<I: Clone> Clone for RandomSearch<I>

fn clone(&self) -> RandomSearch<I>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<I: Debug> Debug for RandomSearch<I>

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

Formats the value using the given formatter.

impl<P, I> Optimizer<P> for RandomSearch<I>

where P: Problem + Sync, P::Decision: Send, I: Initializer<P::Decision>,

fn run(&mut self, problem: &P) -> OptimizationResult<P::Decision>

Run the optimizer to completion against problem.