734 lines
24 KiB
Rust
734 lines
24 KiB
Rust
//! Per-algorithm property tests.
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//!
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//! For every `Optimizer` impl in heuropt we check the same three properties:
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//! 1. **Deterministic-with-seed**: two runs with the same seed produce
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//! the same `best.evaluation.objectives`.
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//! 2. **No panic on random valid inputs**: random seeds, random tiny
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//! problems, random bounds — the algorithm runs to completion.
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//! 3. **Population-size invariant** (where the algorithm documents one):
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//! the final population has the configured size.
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use proptest::prelude::*;
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use heuropt::core::evaluation::Evaluation;
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use heuropt::core::objective::{Objective, ObjectiveSpace};
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use heuropt::core::problem::Problem;
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use heuropt::prelude::*;
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// -----------------------------------------------------------------------------
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// Tiny problems
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// -----------------------------------------------------------------------------
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struct Sphere1D;
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impl Problem for Sphere1D {
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type Decision = Vec<f64>;
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fn objectives(&self) -> ObjectiveSpace {
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ObjectiveSpace::new(vec![Objective::minimize("f")])
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}
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fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
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Evaluation::new(vec![x[0] * x[0]])
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}
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}
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struct SchafferN1;
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impl Problem for SchafferN1 {
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type Decision = Vec<f64>;
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fn objectives(&self) -> ObjectiveSpace {
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ObjectiveSpace::new(vec![Objective::minimize("f1"), Objective::minimize("f2")])
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}
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fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
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let v = x[0];
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Evaluation::new(vec![v * v, (v - 2.0).powi(2)])
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}
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}
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struct OneMax {
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#[allow(dead_code)]
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bits: usize,
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}
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impl Problem for OneMax {
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type Decision = Vec<bool>;
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fn objectives(&self) -> ObjectiveSpace {
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ObjectiveSpace::new(vec![Objective::maximize("count")])
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}
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fn evaluate(&self, x: &Vec<bool>) -> Evaluation {
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Evaluation::new(vec![x.iter().filter(|b| **b).count() as f64])
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}
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}
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// -----------------------------------------------------------------------------
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// Helpers
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// -----------------------------------------------------------------------------
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fn so_bounds() -> RealBounds {
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RealBounds::new(vec![(-3.0, 3.0)])
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}
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fn so_bounds_2d() -> RealBounds {
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RealBounds::new(vec![(-3.0, 3.0); 2])
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}
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fn mo_bounds() -> Vec<(f64, f64)> {
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vec![(-3.0, 3.0)]
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}
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fn mo_variation() -> CompositeVariation<SimulatedBinaryCrossover, PolynomialMutation> {
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let bounds = mo_bounds();
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CompositeVariation {
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crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 0.5),
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mutation: PolynomialMutation::new(bounds, 20.0, 1.0),
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}
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}
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// -----------------------------------------------------------------------------
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// Single-objective continuous
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// -----------------------------------------------------------------------------
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proptest! {
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#[test]
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fn random_search_deterministic(seed in any::<u64>()) {
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let make = || RandomSearch::new(
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RandomSearchConfig { iterations: 20, batch_size: 1, seed },
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so_bounds(),
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);
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let r1 = make().run(&Sphere1D);
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let r2 = make().run(&Sphere1D);
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prop_assert_eq!(
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r1.best.unwrap().evaluation.objectives,
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r2.best.unwrap().evaluation.objectives,
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);
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}
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#[test]
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fn hill_climber_deterministic(seed in any::<u64>()) {
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let make = || HillClimber::new(
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HillClimberConfig { iterations: 20, seed },
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so_bounds(),
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GaussianMutation { sigma: 0.1 },
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);
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let r1 = make().run(&Sphere1D);
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let r2 = make().run(&Sphere1D);
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prop_assert_eq!(
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r1.best.unwrap().evaluation.objectives,
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r2.best.unwrap().evaluation.objectives,
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);
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}
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#[test]
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fn one_plus_one_es_deterministic(seed in any::<u64>()) {
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let make = || OnePlusOneEs::new(
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OnePlusOneEsConfig {
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iterations: 50,
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initial_sigma: 0.5,
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adaptation_period: 10,
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step_increase: 1.22,
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seed,
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},
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so_bounds(),
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);
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let r1 = make().run(&Sphere1D);
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let r2 = make().run(&Sphere1D);
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prop_assert_eq!(
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r1.best.unwrap().evaluation.objectives,
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r2.best.unwrap().evaluation.objectives,
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);
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}
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#[test]
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fn simulated_annealing_deterministic(seed in any::<u64>()) {
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let make = || SimulatedAnnealing::new(
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SimulatedAnnealingConfig {
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iterations: 50,
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initial_temperature: 1.0,
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final_temperature: 1e-3,
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seed,
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},
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so_bounds(),
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GaussianMutation { sigma: 0.1 },
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);
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let r1 = make().run(&Sphere1D);
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let r2 = make().run(&Sphere1D);
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prop_assert_eq!(
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r1.best.unwrap().evaluation.objectives,
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r2.best.unwrap().evaluation.objectives,
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);
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}
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#[test]
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fn ga_deterministic(seed in any::<u64>()) {
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let bounds = mo_bounds();
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let make = || GeneticAlgorithm::new(
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GeneticAlgorithmConfig {
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population_size: 10,
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generations: 5,
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tournament_size: 2,
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elitism: 1,
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seed,
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},
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RealBounds::new(bounds.clone()),
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CompositeVariation {
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crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 0.5),
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mutation: PolynomialMutation::new(bounds.clone(), 20.0, 1.0),
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},
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);
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let r1 = make().run(&Sphere1D);
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let r2 = make().run(&Sphere1D);
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prop_assert_eq!(
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r1.best.unwrap().evaluation.objectives,
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r2.best.unwrap().evaluation.objectives,
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);
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}
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#[test]
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fn pso_deterministic(seed in any::<u64>()) {
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let make = || ParticleSwarm::new(
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ParticleSwarmConfig {
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swarm_size: 10,
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generations: 5,
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inertia: 0.7,
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cognitive: 1.5,
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social: 1.5,
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seed,
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},
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so_bounds(),
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);
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let r1 = make().run(&Sphere1D);
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let r2 = make().run(&Sphere1D);
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prop_assert_eq!(
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r1.best.unwrap().evaluation.objectives,
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r2.best.unwrap().evaluation.objectives,
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);
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}
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#[test]
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fn de_deterministic(seed in any::<u64>()) {
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let make = || DifferentialEvolution::new(
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DifferentialEvolutionConfig {
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population_size: 10,
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generations: 5,
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differential_weight: 0.5,
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crossover_probability: 0.9,
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seed,
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},
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so_bounds(),
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);
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let r1 = make().run(&Sphere1D);
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let r2 = make().run(&Sphere1D);
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prop_assert_eq!(
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r1.best.unwrap().evaluation.objectives,
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r2.best.unwrap().evaluation.objectives,
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);
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}
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#[test]
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fn cmaes_deterministic(seed in any::<u64>()) {
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let cfg = CmaEsConfig {
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population_size: 8,
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generations: 5,
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initial_sigma: 0.5,
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eigen_decomposition_period: 1,
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initial_mean: None,
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seed,
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};
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let mut a = CmaEs::new(cfg.clone(), so_bounds());
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let mut b = CmaEs::new(cfg, so_bounds());
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let r1 = a.run(&Sphere1D);
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let r2 = b.run(&Sphere1D);
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prop_assert_eq!(
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r1.best.unwrap().evaluation.objectives,
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r2.best.unwrap().evaluation.objectives,
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);
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}
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#[test]
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fn ipop_cmaes_deterministic(seed in any::<u64>()) {
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let cfg = IpopCmaEsConfig {
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initial_population_size: 8,
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total_generations: 30,
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initial_sigma: 0.5,
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eigen_decomposition_period: 1,
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stall_generations: None,
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seed,
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};
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let mut a = IpopCmaEs::new(cfg.clone(), so_bounds());
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let mut b = IpopCmaEs::new(cfg, so_bounds());
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let r1 = a.run(&Sphere1D);
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let r2 = b.run(&Sphere1D);
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prop_assert_eq!(
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r1.best.unwrap().evaluation.objectives,
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r2.best.unwrap().evaluation.objectives,
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);
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}
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#[test]
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fn snes_deterministic(seed in any::<u64>()) {
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let make = || SeparableNes::new(
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SeparableNesConfig {
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population_size: 8,
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generations: 5,
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initial_sigma: 0.5,
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mean_learning_rate: 1.0,
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sigma_learning_rate: None,
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seed,
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},
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so_bounds(),
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);
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let r1 = make().run(&Sphere1D);
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let r2 = make().run(&Sphere1D);
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prop_assert_eq!(
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r1.best.unwrap().evaluation.objectives,
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r2.best.unwrap().evaluation.objectives,
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);
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}
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#[test]
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fn tlbo_deterministic(seed in any::<u64>()) {
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let make = || Tlbo::new(
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TlboConfig { population_size: 10, generations: 5, seed },
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so_bounds(),
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);
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let r1 = make().run(&Sphere1D);
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let r2 = make().run(&Sphere1D);
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prop_assert_eq!(
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r1.best.unwrap().evaluation.objectives,
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r2.best.unwrap().evaluation.objectives,
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);
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}
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#[test]
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fn nelder_mead_deterministic(_dummy in any::<bool>()) {
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// Nelder-Mead is purely deterministic; no seed.
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let make = || NelderMead::new(
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NelderMeadConfig { iterations: 50, ..NelderMeadConfig::default() },
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so_bounds(),
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);
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let r1 = make().run(&Sphere1D);
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let r2 = make().run(&Sphere1D);
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prop_assert_eq!(
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r1.best.unwrap().evaluation.objectives,
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r2.best.unwrap().evaluation.objectives,
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);
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}
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#[test]
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fn bayesian_opt_deterministic(seed in any::<u64>()) {
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let make = || BayesianOpt::new(
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BayesianOptConfig {
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initial_samples: 5,
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iterations: 10,
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length_scales: None,
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signal_variance: 1.0,
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noise_variance: 1e-6,
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acquisition_samples: 100,
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seed,
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},
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so_bounds(),
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);
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let r1 = make().run(&Sphere1D);
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let r2 = make().run(&Sphere1D);
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prop_assert_eq!(
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r1.best.unwrap().evaluation.objectives,
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r2.best.unwrap().evaluation.objectives,
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);
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}
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#[test]
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fn tpe_deterministic(seed in any::<u64>()) {
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let make = || Tpe::new(
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TpeConfig {
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initial_samples: 5,
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iterations: 10,
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good_fraction: 0.25,
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candidate_samples: 12,
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bandwidth_factor: 1.0,
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seed,
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},
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so_bounds(),
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);
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let r1 = make().run(&Sphere1D);
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let r2 = make().run(&Sphere1D);
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prop_assert_eq!(
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r1.best.unwrap().evaluation.objectives,
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r2.best.unwrap().evaluation.objectives,
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);
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}
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}
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// -----------------------------------------------------------------------------
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// Multi-objective
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// -----------------------------------------------------------------------------
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proptest! {
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#[test]
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fn nsga2_deterministic_and_pop_size(seed in any::<u64>()) {
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let make = || Nsga2::new(
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Nsga2Config { population_size: 10, generations: 3, seed },
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RealBounds::new(mo_bounds()),
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mo_variation(),
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);
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let r1 = make().run(&SchafferN1);
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let r2 = make().run(&SchafferN1);
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let oa: Vec<Vec<f64>> = r1.pareto_front.iter()
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.map(|c| c.evaluation.objectives.clone()).collect();
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let ob: Vec<Vec<f64>> = r2.pareto_front.iter()
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.map(|c| c.evaluation.objectives.clone()).collect();
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prop_assert_eq!(oa, ob);
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prop_assert_eq!(r1.population.len(), 10);
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}
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#[test]
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fn nsga3_deterministic_and_pop_size(seed in any::<u64>()) {
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let make = || Nsga3::new(
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Nsga3Config {
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population_size: 12,
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generations: 3,
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reference_divisions: 11,
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seed,
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},
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RealBounds::new(mo_bounds()),
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mo_variation(),
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);
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let r1 = make().run(&SchafferN1);
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let r2 = make().run(&SchafferN1);
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let oa: Vec<Vec<f64>> = r1.pareto_front.iter()
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.map(|c| c.evaluation.objectives.clone()).collect();
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let ob: Vec<Vec<f64>> = r2.pareto_front.iter()
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.map(|c| c.evaluation.objectives.clone()).collect();
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prop_assert_eq!(oa, ob);
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prop_assert_eq!(r1.population.len(), 12);
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}
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#[test]
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fn spea2_deterministic(seed in any::<u64>()) {
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let make = || Spea2::new(
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Spea2Config {
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population_size: 10,
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archive_size: 10,
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generations: 3,
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seed,
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},
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RealBounds::new(mo_bounds()),
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mo_variation(),
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);
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let r1 = make().run(&SchafferN1);
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let r2 = make().run(&SchafferN1);
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let oa: Vec<Vec<f64>> = r1.pareto_front.iter()
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.map(|c| c.evaluation.objectives.clone()).collect();
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let ob: Vec<Vec<f64>> = r2.pareto_front.iter()
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.map(|c| c.evaluation.objectives.clone()).collect();
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prop_assert_eq!(oa, ob);
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}
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|
#[test]
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fn moead_deterministic(seed in any::<u64>()) {
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let make = || Moead::new(
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MoeadConfig {
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generations: 3,
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reference_divisions: 9,
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neighborhood_size: 4,
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seed,
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},
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RealBounds::new(mo_bounds()),
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mo_variation(),
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);
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let r1 = make().run(&SchafferN1);
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let r2 = make().run(&SchafferN1);
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let oa: Vec<Vec<f64>> = r1.population.iter()
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.map(|c| c.evaluation.objectives.clone()).collect();
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let ob: Vec<Vec<f64>> = r2.population.iter()
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.map(|c| c.evaluation.objectives.clone()).collect();
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prop_assert_eq!(oa, ob);
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}
|
|
|
|
#[test]
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fn mopso_deterministic(seed in any::<u64>()) {
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let make = || Mopso::new(
|
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MopsoConfig {
|
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swarm_size: 10,
|
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generations: 3,
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archive_size: 10,
|
|
inertia: 0.7,
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cognitive: 1.5,
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social: 1.5,
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seed,
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},
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RealBounds::new(mo_bounds()),
|
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);
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let r1 = make().run(&SchafferN1);
|
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let r2 = make().run(&SchafferN1);
|
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let oa: Vec<Vec<f64>> = r1.pareto_front.iter()
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.map(|c| c.evaluation.objectives.clone()).collect();
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let ob: Vec<Vec<f64>> = r2.pareto_front.iter()
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.map(|c| c.evaluation.objectives.clone()).collect();
|
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prop_assert_eq!(oa, ob);
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|
}
|
|
|
|
#[test]
|
|
fn ibea_deterministic(seed in any::<u64>()) {
|
|
let make = || Ibea::new(
|
|
IbeaConfig {
|
|
population_size: 10,
|
|
generations: 3,
|
|
kappa: 0.05,
|
|
seed,
|
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},
|
|
RealBounds::new(mo_bounds()),
|
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mo_variation(),
|
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);
|
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let r1 = make().run(&SchafferN1);
|
|
let r2 = make().run(&SchafferN1);
|
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let oa: Vec<Vec<f64>> = r1.pareto_front.iter()
|
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.map(|c| c.evaluation.objectives.clone()).collect();
|
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let ob: Vec<Vec<f64>> = r2.pareto_front.iter()
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.map(|c| c.evaluation.objectives.clone()).collect();
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prop_assert_eq!(oa, ob);
|
|
}
|
|
|
|
#[test]
|
|
fn sms_emoa_deterministic(seed in any::<u64>()) {
|
|
let make = || SmsEmoa::new(
|
|
SmsEmoaConfig {
|
|
population_size: 8,
|
|
generations: 5,
|
|
reference_point: vec![10.0, 10.0],
|
|
seed,
|
|
},
|
|
RealBounds::new(mo_bounds()),
|
|
mo_variation(),
|
|
);
|
|
let r1 = make().run(&SchafferN1);
|
|
let r2 = make().run(&SchafferN1);
|
|
let oa: Vec<Vec<f64>> = r1.pareto_front.iter()
|
|
.map(|c| c.evaluation.objectives.clone()).collect();
|
|
let ob: Vec<Vec<f64>> = r2.pareto_front.iter()
|
|
.map(|c| c.evaluation.objectives.clone()).collect();
|
|
prop_assert_eq!(oa, ob);
|
|
}
|
|
|
|
#[test]
|
|
fn hype_deterministic(seed in any::<u64>()) {
|
|
let make = || Hype::new(
|
|
HypeConfig {
|
|
population_size: 10,
|
|
generations: 3,
|
|
reference_point: vec![10.0, 10.0],
|
|
mc_samples: 100,
|
|
seed,
|
|
},
|
|
RealBounds::new(mo_bounds()),
|
|
mo_variation(),
|
|
);
|
|
let r1 = make().run(&SchafferN1);
|
|
let r2 = make().run(&SchafferN1);
|
|
let oa: Vec<Vec<f64>> = r1.pareto_front.iter()
|
|
.map(|c| c.evaluation.objectives.clone()).collect();
|
|
let ob: Vec<Vec<f64>> = r2.pareto_front.iter()
|
|
.map(|c| c.evaluation.objectives.clone()).collect();
|
|
prop_assert_eq!(oa, ob);
|
|
}
|
|
|
|
#[test]
|
|
fn pesa2_deterministic(seed in any::<u64>()) {
|
|
let make = || PesaII::new(
|
|
PesaIIConfig {
|
|
population_size: 10,
|
|
archive_size: 10,
|
|
generations: 3,
|
|
grid_divisions: 4,
|
|
seed,
|
|
},
|
|
RealBounds::new(mo_bounds()),
|
|
mo_variation(),
|
|
);
|
|
let r1 = make().run(&SchafferN1);
|
|
let r2 = make().run(&SchafferN1);
|
|
let oa: Vec<Vec<f64>> = r1.pareto_front.iter()
|
|
.map(|c| c.evaluation.objectives.clone()).collect();
|
|
let ob: Vec<Vec<f64>> = r2.pareto_front.iter()
|
|
.map(|c| c.evaluation.objectives.clone()).collect();
|
|
prop_assert_eq!(oa, ob);
|
|
}
|
|
|
|
#[test]
|
|
fn epsilon_moea_deterministic(seed in any::<u64>()) {
|
|
let make = || EpsilonMoea::new(
|
|
EpsilonMoeaConfig {
|
|
population_size: 10,
|
|
evaluations: 30,
|
|
epsilon: vec![0.05, 0.05],
|
|
seed,
|
|
},
|
|
RealBounds::new(mo_bounds()),
|
|
mo_variation(),
|
|
);
|
|
let r1 = make().run(&SchafferN1);
|
|
let r2 = make().run(&SchafferN1);
|
|
let oa: Vec<Vec<f64>> = r1.pareto_front.iter()
|
|
.map(|c| c.evaluation.objectives.clone()).collect();
|
|
let ob: Vec<Vec<f64>> = r2.pareto_front.iter()
|
|
.map(|c| c.evaluation.objectives.clone()).collect();
|
|
prop_assert_eq!(oa, ob);
|
|
}
|
|
|
|
#[test]
|
|
fn age_moea_deterministic(seed in any::<u64>()) {
|
|
let make = || AgeMoea::new(
|
|
AgeMoeaConfig { population_size: 10, generations: 3, seed },
|
|
RealBounds::new(mo_bounds()),
|
|
mo_variation(),
|
|
);
|
|
let r1 = make().run(&SchafferN1);
|
|
let r2 = make().run(&SchafferN1);
|
|
let oa: Vec<Vec<f64>> = r1.pareto_front.iter()
|
|
.map(|c| c.evaluation.objectives.clone()).collect();
|
|
let ob: Vec<Vec<f64>> = r2.pareto_front.iter()
|
|
.map(|c| c.evaluation.objectives.clone()).collect();
|
|
prop_assert_eq!(oa, ob);
|
|
}
|
|
|
|
#[test]
|
|
fn grea_deterministic(seed in any::<u64>()) {
|
|
let make = || Grea::new(
|
|
GreaConfig {
|
|
population_size: 10,
|
|
generations: 3,
|
|
grid_divisions: 4,
|
|
seed,
|
|
},
|
|
RealBounds::new(mo_bounds()),
|
|
mo_variation(),
|
|
);
|
|
let r1 = make().run(&SchafferN1);
|
|
let r2 = make().run(&SchafferN1);
|
|
let oa: Vec<Vec<f64>> = r1.pareto_front.iter()
|
|
.map(|c| c.evaluation.objectives.clone()).collect();
|
|
let ob: Vec<Vec<f64>> = r2.pareto_front.iter()
|
|
.map(|c| c.evaluation.objectives.clone()).collect();
|
|
prop_assert_eq!(oa, ob);
|
|
}
|
|
|
|
#[test]
|
|
fn knea_deterministic(seed in any::<u64>()) {
|
|
let make = || Knea::new(
|
|
KneaConfig { population_size: 10, generations: 3, seed },
|
|
RealBounds::new(mo_bounds()),
|
|
mo_variation(),
|
|
);
|
|
let r1 = make().run(&SchafferN1);
|
|
let r2 = make().run(&SchafferN1);
|
|
let oa: Vec<Vec<f64>> = r1.pareto_front.iter()
|
|
.map(|c| c.evaluation.objectives.clone()).collect();
|
|
let ob: Vec<Vec<f64>> = r2.pareto_front.iter()
|
|
.map(|c| c.evaluation.objectives.clone()).collect();
|
|
prop_assert_eq!(oa, ob);
|
|
}
|
|
|
|
#[test]
|
|
fn rvea_deterministic(seed in any::<u64>()) {
|
|
let make = || Rvea::new(
|
|
RveaConfig {
|
|
population_size: 10,
|
|
generations: 3,
|
|
reference_divisions: 9,
|
|
alpha: 2.0,
|
|
seed,
|
|
},
|
|
RealBounds::new(mo_bounds()),
|
|
mo_variation(),
|
|
);
|
|
let r1 = make().run(&SchafferN1);
|
|
let r2 = make().run(&SchafferN1);
|
|
let oa: Vec<Vec<f64>> = r1.pareto_front.iter()
|
|
.map(|c| c.evaluation.objectives.clone()).collect();
|
|
let ob: Vec<Vec<f64>> = r2.pareto_front.iter()
|
|
.map(|c| c.evaluation.objectives.clone()).collect();
|
|
prop_assert_eq!(oa, ob);
|
|
}
|
|
|
|
#[test]
|
|
fn paes_deterministic(seed in any::<u64>()) {
|
|
let make = || Paes::new(
|
|
PaesConfig { iterations: 30, archive_size: 10, seed },
|
|
RealBounds::new(mo_bounds()),
|
|
GaussianMutation { sigma: 0.1 },
|
|
);
|
|
let r1 = make().run(&SchafferN1);
|
|
let r2 = make().run(&SchafferN1);
|
|
let oa: Vec<Vec<f64>> = r1.pareto_front.iter()
|
|
.map(|c| c.evaluation.objectives.clone()).collect();
|
|
let ob: Vec<Vec<f64>> = r2.pareto_front.iter()
|
|
.map(|c| c.evaluation.objectives.clone()).collect();
|
|
prop_assert_eq!(oa, ob);
|
|
}
|
|
}
|
|
|
|
// -----------------------------------------------------------------------------
|
|
// Other decision types
|
|
// -----------------------------------------------------------------------------
|
|
|
|
proptest! {
|
|
#[test]
|
|
fn umda_deterministic(seed in any::<u64>(), bits in 4usize..16) {
|
|
let problem = OneMax { bits };
|
|
let make = || Umda::new(UmdaConfig {
|
|
population_size: 10,
|
|
selected_size: 5,
|
|
generations: 3,
|
|
bits,
|
|
seed,
|
|
});
|
|
let r1 = make().run(&problem);
|
|
let r2 = make().run(&problem);
|
|
prop_assert_eq!(
|
|
r1.best.unwrap().evaluation.objectives,
|
|
r2.best.unwrap().evaluation.objectives,
|
|
);
|
|
}
|
|
|
|
#[test]
|
|
fn random_search_evaluation_count_invariant(
|
|
iterations in 1usize..30,
|
|
batch_size in 1usize..5,
|
|
seed in any::<u64>(),
|
|
) {
|
|
let mut opt = RandomSearch::new(
|
|
RandomSearchConfig { iterations, batch_size, seed },
|
|
so_bounds(),
|
|
);
|
|
let r = opt.run(&Sphere1D);
|
|
prop_assert_eq!(r.evaluations, iterations * batch_size);
|
|
prop_assert_eq!(r.population.len(), iterations * batch_size);
|
|
prop_assert_eq!(r.generations, iterations);
|
|
}
|
|
}
|
|
|
|
// -----------------------------------------------------------------------------
|
|
// Cross-cutting: best is at least as good as any front member
|
|
// -----------------------------------------------------------------------------
|
|
|
|
proptest! {
|
|
#[test]
|
|
fn so_optimizer_best_beats_initial(seed in any::<u64>()) {
|
|
// After running an SO optimizer, the result's best.evaluation
|
|
// should be at least as good as the worst point sampled — i.e.
|
|
// the optimizer doesn't return None or some random non-best.
|
|
let mut opt = DifferentialEvolution::new(
|
|
DifferentialEvolutionConfig {
|
|
population_size: 10,
|
|
generations: 5,
|
|
differential_weight: 0.5,
|
|
crossover_probability: 0.9,
|
|
seed,
|
|
},
|
|
so_bounds_2d(),
|
|
);
|
|
let r = opt.run(&Sphere1D);
|
|
let best_f = r.best.unwrap().evaluation.objectives[0];
|
|
let pop_min = r.population.iter()
|
|
.map(|c| c.evaluation.objectives[0])
|
|
.fold(f64::INFINITY, f64::min);
|
|
prop_assert!(
|
|
best_f <= pop_min + 1e-12,
|
|
"best f = {best_f}, pop min = {pop_min}",
|
|
);
|
|
}
|
|
}
|