rlevo_evolution/algorithms/metaheuristic/

woa.rs

//! Whale Optimization Algorithm.
//!
//! Each whale chooses per generation between two behaviours, driven by a
//! uniform random `p ∈ U[0, 1]`:
//!
//! - `p < 0.5` — **encircle / search**:
//!     - `|A| < 1`: exploit the current best (`X ← X_best − A·|C·X_best − X|`),
//!     - `|A| ≥ 1`: explore by pulling toward a random other whale
//!       (`X ← X_rand − A·|C·X_rand − X|`).
//! - `p ≥ 0.5` — **spiral bubble-net**:
//!   `X ← |X_best − X|·exp(b·l)·cos(2π·l) + X_best`, `l ∈ U[−1, 1]`,
//!   `b = 1` (canonical).
//!
//! `A = 2a·r − a`, `C = 2r`, with `a` linearly decreased from 2 to 0
//! over the budget.
//!
//! The branches are realized as two boolean masks and three tensor
//! candidates — no divergent kernel paths.
//!
//! # Candor
//!
//! Legacy comparator. The spiral bubble-net and encircle-best operators
//! compose to a motion pattern that is equivalent in expectation to a
//! weighted PSO update toward the current best (Camacho Villalón et al.
//! 2023 review the structural similarities — the 2020 paper by the same
//! authors covers only GWO/FA/BA, not WOA). Ship it for API coverage;
//! prefer CMA-ES or LSHADE when available.
//!
//! # References
//!
//! - Mirjalili & Lewis (2016), *The Whale Optimization Algorithm*.
//! - Camacho Villalón, Dorigo & Stützle (2023), *Exposing the grey wolf,
//!   moth-flame, whale, firefly, bat, and antlion algorithms: six misleading
//!   optimization techniques inspired by bestial metaphors*, International
//!   Transactions in Operational Research 30:2945-2971.

use std::f32::consts::PI;
use std::marker::PhantomData;

use burn::tensor::{Int, Tensor, TensorData, backend::Backend};
use rand::Rng;
use rand::RngExt;

use rlevo_core::bounds::Bounds;
use rlevo_core::config::{self, ConfigError, Validate};

use super::len_matches_pop;
use crate::ops::selection::argmax_host;
use crate::rng::{SeedPurpose, seed_stream};
use crate::strategy::{Strategy, StrategyMetrics};

/// `exp(x)` overflows f32 for x ≳ 88.7; clamp the spiral exponent so an
/// out-of-range `b` can never produce `inf`/`NaN` in the spiral update.
const MAX_SPIRAL_EXP: f32 = 80.0; // exp(80) ≈ 5.5e34, well under f32::MAX

/// Per-element spiral bubble-net factor `exp(b·l)·cos(2π·l)`.
///
/// The exponent `b·l` is clamped to `±`[`MAX_SPIRAL_EXP`] before
/// exponentiation. Without the clamp an out-of-range `b` drives
/// `exp(b·l)` past f32's overflow threshold to `inf`; where `cos(2π·l)`
/// is zero the product is `inf · 0 = NaN` (and elsewhere it is `±inf`) —
/// non-finite values that the downstream bounds clamp does not sanitize.
/// Clamping the exponent keeps the factor finite for every `l`.
///
/// This is the pure host-side core the `ask` spiral branch is built on;
/// keeping it out of the tensor pipeline makes the overflow guard directly
/// unit-testable with injected pathological `(l, b)` pairs.
fn spiral_factor(l: f32, b: f32) -> f32 {
    (b * l).clamp(-MAX_SPIRAL_EXP, MAX_SPIRAL_EXP).exp() * (2.0 * PI * l).cos()
}

/// Static configuration for [`WhaleOptimization`].
#[derive(Debug, Clone)]
pub struct WoaConfig {
    /// Number of whales.
    pub pop_size: usize,
    /// Genome dimensionality.
    pub genome_dim: usize,
    /// Search-space bounds.
    pub bounds: Bounds,
    /// Budget pacing `a = 2·(1 − t/max_generations)`.
    pub max_generations: usize,
    /// Spiral shape constant (Mirjalili's canonical `b = 1`).
    pub b: f32,
}

impl WoaConfig {
    /// Default configuration for a given population size and genome dimensionality.
    #[must_use]
    pub fn default_for(pop_size: usize, genome_dim: usize) -> Self {
        Self {
            pop_size,
            genome_dim,
            bounds: Bounds::new(-5.12, 5.12),
            max_generations: 500,
            b: 1.0,
        }
    }
}

impl Validate for WoaConfig {
    fn validate(&self) -> Result<(), ConfigError> {
        const C: &str = "WoaConfig";
        config::at_least(C, "pop_size", self.pop_size, 1)?;
        config::nonzero(C, "genome_dim", self.genome_dim)?;
        config::at_least(C, "max_generations", self.max_generations, 1)?;
        config::positive(C, "b", f64::from(self.b))?;
        Ok(())
    }
}

/// Generation state for [`WhaleOptimization`].
#[derive(Debug, Clone)]
pub struct WoaState<B: Backend> {
    /// Current positions, shape `(pop_size, D)`.
    positions: Tensor<B, 2>,
    /// Host-side fitness cache.
    fitness: Vec<f32>,
    /// Best-so-far genome.
    best_genome: Option<Tensor<B, 2>>,
    /// Best-so-far fitness.
    best_fitness: f32,
    /// Generation counter.
    generation: usize,
}

impl<B: Backend> WoaState<B> {
    /// Assembles a whale-swarm state, checking the fitness cache matches `pop`.
    ///
    /// # Errors
    ///
    /// Returns a [`ConfigError`] if `positions` has zero rows or if `fitness`
    /// is non-empty with a length other than `pop_size`.
    pub fn try_new(
        positions: Tensor<B, 2>,
        fitness: Vec<f32>,
        best_genome: Option<Tensor<B, 2>>,
        best_fitness: f32,
        generation: usize,
    ) -> Result<Self, ConfigError> {
        let pop = positions.dims()[0];
        config::nonzero("WoaState", "pop_size", pop)?;
        len_matches_pop("WoaState", "fitness", pop, fitness.len())?;
        Ok(Self {
            positions,
            fitness,
            best_genome,
            best_fitness,
            generation,
        })
    }

    /// Current positions, shape `(pop_size, D)`.
    #[must_use]
    pub fn positions(&self) -> &Tensor<B, 2> {
        &self.positions
    }

    /// Host-side fitness cache (empty at bootstrap, else `pop_size` long).
    #[must_use]
    pub fn fitness(&self) -> &[f32] {
        &self.fitness
    }

    /// Best-so-far genome, or `None` before the first `tell`.
    #[must_use]
    pub fn best_genome(&self) -> Option<&Tensor<B, 2>> {
        self.best_genome.as_ref()
    }

    /// Best-so-far (canonical, maximise) fitness.
    #[must_use]
    pub fn best_fitness(&self) -> f32 {
        self.best_fitness
    }

    /// Generation counter.
    #[must_use]
    pub fn generation(&self) -> usize {
        self.generation
    }
}

/// Whale Optimization Algorithm strategy.
///
/// # Panics
///
/// [`Strategy::ask`] panics if called a second time without an intervening
/// [`Strategy::tell`] — specifically, if `state.best_genome` is `None` after
/// the first generation. In normal harness-driven usage this cannot happen:
/// `ask` on the first call returns the initial positions unevaluated;
/// `tell` then sets `best_genome`; and every subsequent `ask` finds it
/// populated. Bypassing the harness and calling `ask` twice in a row without
/// a `tell` in between will trigger the assert.
///
/// # Example
///
/// ```no_run
/// use burn::backend::Flex;
/// use rlevo_evolution::algorithms::metaheuristic::woa::{WhaleOptimization, WoaConfig};
///
/// let strategy = WhaleOptimization::<Flex>::new();
/// let params = WoaConfig::default_for(32, 10);
/// let _ = (strategy, params);
/// ```
#[derive(Debug, Clone, Copy, Default)]
pub struct WhaleOptimization<B: Backend> {
    _backend: PhantomData<fn() -> B>,
}

impl<B: Backend> WhaleOptimization<B> {
    /// Builds a new (stateless) strategy object.
    #[must_use]
    pub fn new() -> Self {
        Self {
            _backend: PhantomData,
        }
    }
}

impl<B: Backend> Strategy<B> for WhaleOptimization<B>
where
    B::Device: Clone,
{
    type Params = WoaConfig;
    type State = WoaState<B>;
    type Genome = Tensor<B, 2>;

    /// Samples the initial whale positions uniformly within
    /// [`WoaConfig::bounds`] using the host-RNG convention and sets the
    /// generation counter to zero.
    fn init(
        &self,
        params: &WoaConfig,
        rng: &mut dyn Rng,
        device: &<B as burn::tensor::backend::BackendTypes>::Device,
    ) -> WoaState<B> {
        debug_assert!(
            params.validate().is_ok(),
            "invalid WoaConfig reached init: {params:?}"
        );
        let (lo, hi): (f32, f32) = params.bounds.into();
        // Host-sample the initial population from a deterministic
        // `seed_stream` rather than the process-wide Flex RNG (`B::seed` +
        // `Tensor::random`), whose draws interleave with sibling tests under
        // the parallel runner and are not reproducible across schedules.
        let pop = params.pop_size;
        let genome_dim = params.genome_dim;
        let mut stream = seed_stream(rng.next_u64(), 0, SeedPurpose::Init);
        let mut position_rows = Vec::with_capacity(pop * genome_dim);
        for _ in 0..pop * genome_dim {
            position_rows.push(lo + (hi - lo) * stream.random::<f32>());
        }
        let positions =
            Tensor::<B, 2>::from_data(TensorData::new(position_rows, [pop, genome_dim]), device);
        WoaState {
            positions,
            fitness: Vec::new(),
            best_genome: None,
            best_fitness: f32::NEG_INFINITY,
            generation: 0,
        }
    }

    /// Proposes the next whale positions.
    ///
    /// On the first call returns the initial positions unchanged. On
    /// subsequent calls, each whale independently selects one of three
    /// moves based on host-sampled scalars `p` and `|A|`:
    ///
    /// - `p < 0.5, |A| < 1` — encircle the current best,
    /// - `p < 0.5, |A| ≥ 1` — search toward a random other whale,
    /// - `p ≥ 0.5` — spiral toward the current best.
    ///
    /// The three candidate tensors are computed in parallel and composed
    /// with boolean masks; no divergent kernel paths are used. Results are
    /// clamped to [`WoaConfig::bounds`].
    #[allow(clippy::many_single_char_names)]
    fn ask(
        &self,
        params: &WoaConfig,
        state: &WoaState<B>,
        rng: &mut dyn Rng,
        device: &<B as burn::tensor::backend::BackendTypes>::Device,
    ) -> (Tensor<B, 2>, WoaState<B>) {
        // First call: evaluate initial whales so `tell` can record fitness.
        if state.fitness.is_empty() {
            return (state.positions.clone(), state.clone());
        }

        let pop_size = params.pop_size;
        let genome_dim = params.genome_dim;

        // Linear schedule for a.
        #[allow(clippy::cast_precision_loss)]
        let t = state.generation as f32;
        #[allow(clippy::cast_precision_loss)]
        let max_t = params.max_generations.max(1) as f32;
        let a = 2.0 * (1.0 - (t / max_t).min(1.0));

        // Per-whale scalars: A, C, p, l. Sample on host via the scope
        // splitmix stream so the seed contract is fully reproducible.
        let mut stream = seed_stream(rng.next_u64(), state.generation as u64, SeedPurpose::Other);
        let mut rand_idx: Vec<i64> = Vec::with_capacity(pop_size);
        let mut a_scalar: Vec<f32> = Vec::with_capacity(pop_size);
        let mut c_scalar: Vec<f32> = Vec::with_capacity(pop_size);
        let mut p_scalar: Vec<f32> = Vec::with_capacity(pop_size);
        let mut l_scalar: Vec<f32> = Vec::with_capacity(pop_size);
        let mut abs_a_lt_one: Vec<i64> = Vec::with_capacity(pop_size);
        let mut p_lt_half: Vec<i64> = Vec::with_capacity(pop_size);
        for i in 0..pop_size {
            let r_a: f32 = stream.random::<f32>();
            let r_c: f32 = stream.random::<f32>();
            let p: f32 = stream.random::<f32>();
            let l: f32 = 2.0 * stream.random::<f32>() - 1.0;
            let a_val = 2.0 * a * r_a - a;
            let c_val = 2.0 * r_c;
            a_scalar.push(a_val);
            c_scalar.push(c_val);
            p_scalar.push(p);
            l_scalar.push(l);
            abs_a_lt_one.push(i64::from(a_val.abs() < 1.0));
            p_lt_half.push(i64::from(p < 0.5));
            // Pick a different index for the "search" branch.
            let mut r = stream.random_range(0..pop_size);
            if r == i {
                r = (r + 1) % pop_size;
            }
            #[allow(clippy::cast_possible_wrap)]
            rand_idx.push(r as i64);
        }

        let a_row = Tensor::<B, 1>::from_data(TensorData::new(a_scalar, [pop_size]), device)
            .unsqueeze_dim::<2>(1)
            .expand([pop_size, genome_dim]);
        let c_row = Tensor::<B, 1>::from_data(TensorData::new(c_scalar, [pop_size]), device)
            .unsqueeze_dim::<2>(1)
            .expand([pop_size, genome_dim]);
        let rand_idx_t =
            Tensor::<B, 1, Int>::from_data(TensorData::new(rand_idx, [pop_size]), device);
        let x_rand = state.positions.clone().select(0, rand_idx_t);

        let x_best = state
            .best_genome
            .as_ref()
            .expect("best_genome populated after the first tell")
            .clone()
            .expand([pop_size, genome_dim]);

        // Encircle toward X_best:  X_best − A · |C · X_best − X|
        let enc_best = x_best.clone()
            - a_row
                .clone()
                .mul((c_row.clone().mul(x_best.clone()) - state.positions.clone()).abs());
        // Search toward X_rand:    X_rand − A · |C · X_rand − X|
        let enc_rand =
            x_rand.clone() - a_row.mul((c_row.mul(x_rand) - state.positions.clone()).abs());
        // Spiral toward X_best:    |X_best − X| · exp(b·l) · cos(2π·l) + X_best.
        // The per-element factor `exp(b·l)·cos(2π·l)` is computed host-side by
        // `spiral_factor`, which clamps the exponent so an out-of-range `b`
        // can never produce `inf · 0 = NaN` in the spiral update.
        let dist = (x_best.clone() - state.positions.clone()).abs();
        let factor_host: Vec<f32> = l_scalar
            .iter()
            .map(|&l| spiral_factor(l, params.b))
            .collect();
        let factor = Tensor::<B, 1>::from_data(TensorData::new(factor_host, [pop_size]), device);
        let factor_mat = factor.unsqueeze_dim::<2>(1).expand([pop_size, genome_dim]);
        let spiral = dist.mul(factor_mat) + x_best;

        // Compose: p < 0.5 ? (|A|<1 ? enc_best : enc_rand) : spiral.
        let m_abs_a_lt_one =
            Tensor::<B, 1, Int>::from_data(TensorData::new(abs_a_lt_one, [pop_size]), device)
                .equal_elem(1)
                .unsqueeze_dim::<2>(1)
                .expand([pop_size, genome_dim]);
        let m_p_lt_half =
            Tensor::<B, 1, Int>::from_data(TensorData::new(p_lt_half, [pop_size]), device)
                .equal_elem(1)
                .unsqueeze_dim::<2>(1)
                .expand([pop_size, genome_dim]);

        let encircle = enc_rand.mask_where(m_abs_a_lt_one, enc_best);
        let new_positions = spiral.mask_where(m_p_lt_half, encircle);

        let (lo, hi): (f32, f32) = params.bounds.into();
        let new_positions = new_positions.clamp(lo, hi);

        let mut next = state.clone();
        next.positions.clone_from(&new_positions);
        (new_positions, next)
    }

    /// Records evaluated fitness, updates the best-so-far (food source), and
    /// increments the generation counter.
    ///
    /// Returns the updated [`WoaState`] and a [`StrategyMetrics`] snapshot
    /// for the completed generation.
    fn tell(
        &self,
        _params: &WoaConfig,
        population: Tensor<B, 2>,
        fitness: Tensor<B, 1>,
        mut state: WoaState<B>,
        _rng: &mut dyn Rng,
    ) -> (WoaState<B>, StrategyMetrics) {
        let fitness_host = fitness
            .into_data()
            .into_vec::<f32>()
            .expect("fitness tensor must be readable as f32");
        state.fitness.clone_from(&fitness_host);
        state.positions.clone_from(&population);
        let best_idx = argmax_host(&fitness_host);
        if fitness_host[best_idx] > state.best_fitness {
            state.best_fitness = fitness_host[best_idx];
            let device = population.device();
            #[allow(clippy::cast_possible_wrap)]
            let idx = Tensor::<B, 1, Int>::from_data(
                TensorData::new(vec![best_idx as i64], [1]),
                &device,
            );
            state.best_genome = Some(population.select(0, idx));
        }
        state.generation += 1;
        let m =
            StrategyMetrics::from_host_fitness(state.generation, &fitness_host, state.best_fitness);
        state.best_fitness = m.best_fitness_ever();
        (state, m)
    }

    /// Returns the best-so-far (food-source) genome and its fitness, or
    /// `None` before the first [`tell`](Strategy::tell) call.
    fn best(&self, state: &WoaState<B>) -> Option<(Tensor<B, 2>, f32)> {
        state
            .best_genome
            .as_ref()
            .map(|g| (g.clone(), state.best_fitness))
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::fitness::FromFitnessEvaluable;
    use crate::strategy::EvolutionaryHarness;
    use burn::backend::Flex;
    use rand::SeedableRng;
    use rand::rngs::StdRng;
    use rlevo_core::fitness::FitnessEvaluable;

    type TestBackend = Flex;

    /// Distinct finite fitness with the maximum at index 0, for direct
    /// (non-harness) `tell` calls. Finite, so it respects ADR 0034.
    #[allow(clippy::trivially_copy_pass_by_ref)] // mirror the by-ref device idiom
    fn finite_fitness(
        n: usize,
        device: &<TestBackend as burn::tensor::backend::BackendTypes>::Device,
    ) -> Tensor<TestBackend, 1> {
        #[allow(clippy::cast_precision_loss)]
        let vals: Vec<f32> = (0..n).map(|i| -(i as f32) - 1.0).collect();
        Tensor::<TestBackend, 1>::from_data(TensorData::new(vals, [n]), device)
    }

    #[test]
    fn try_new_checks_fitness_length() {
        let device = Default::default();
        let pos = Tensor::<TestBackend, 2>::zeros([3, 2], &device);
        assert!(WoaState::try_new(pos.clone(), vec![1.0; 3], None, 1.0, 1).is_ok());
        assert!(WoaState::try_new(pos.clone(), vec![], None, f32::MIN, 0).is_ok());
        assert!(WoaState::try_new(pos, vec![1.0; 2], None, 1.0, 1).is_err());
        let empty = Tensor::<TestBackend, 2>::zeros([0, 2], &device);
        assert!(WoaState::try_new(empty, vec![], None, 1.0, 0).is_err());
    }

    #[test]
    fn default_config_validates() {
        assert!(WoaConfig::default_for(30, 10).validate().is_ok());
    }

    #[test]
    fn rejects_nonpositive_b() {
        let mut cfg = WoaConfig::default_for(30, 10);
        cfg.b = 0.0;
        assert_eq!(cfg.validate().unwrap_err().field, "b");
    }

    struct Sphere;
    struct SphereFit;
    impl FitnessEvaluable for SphereFit {
        type Individual = Vec<f64>;
        type Landscape = Sphere;
        fn evaluate(&self, x: &Self::Individual, _: &Self::Landscape) -> f64 {
            x.iter().map(|v| v * v).sum()
        }
    }

    #[test]
    fn woa_converges_on_sphere_d10() {
        // WOA as "legacy comparator" per module-level candor note. We
        // verify convergence direction — reaching within 1e-4 of the
        // optimum on Sphere-D10 in 600 generations confirms the
        // spiral/encircle composition functions correctly.
        let device = Default::default();
        let strategy = WhaleOptimization::<TestBackend>::new();
        let params = WoaConfig::default_for(32, 10);
        let fitness_fn = FromFitnessEvaluable::new(SphereFit, Sphere);
        let mut harness = EvolutionaryHarness::<TestBackend, _, _>::new(
            strategy, params, fitness_fn, 5, device, 600,
        )
        .expect("valid params");
        harness.reset();
        while !harness.step(()).done {}
        let best = harness.latest_metrics().unwrap().best_fitness_ever();
        assert!(best < 1e-4, "WOA D10 best={best}");
    }

    #[test]
    fn spiral_factor_stays_finite_under_overflow() {
        // Deterministic reproducer for #156 (WOA): the spiral factor
        // `exp(b·l)·cos(2π·l)`. A large `b` drives `exp(b·l)` past f32's
        // overflow threshold (≈ e^88.7). At `l = 0.75`, `cos(2π·0.75)` is
        // (numerically) zero, so the *un-clamped* product is `inf · 0 = NaN`;
        // at other overflow points it is `±inf`. Either way the value is
        // non-finite and survives the downstream bounds clamp, poisoning the
        // genome. `spiral_factor` clamps the exponent and stays finite.
        //
        // Each `(l, b)` pair below has `b·l > 88.7`, so the un-clamped
        // reference is non-finite — the assertion on `spiral_factor` would
        // FAIL against the pre-fix (un-clamped) computation, which is exactly
        // the `unguarded` expression checked to be non-finite here.
        for &(l, b) in &[
            (0.75_f32, 200.0_f32),
            (0.5, 200.0),
            (0.9, 200.0),
            (0.75, 500.0),
        ] {
            // What the pre-fix code computed (no exponent clamp).
            let unguarded: f32 = (b * l).exp() * (2.0 * PI * l).cos();
            assert!(
                !unguarded.is_finite(),
                "test setup: expected overflow for l={l}, b={b}, got {unguarded}"
            );
            let guarded: f32 = spiral_factor(l, b);
            assert!(
                guarded.is_finite(),
                "spiral_factor non-finite for l={l}, b={b}: {guarded}"
            );
        }
    }

    #[test]
    fn spiral_factor_matches_unguarded_when_in_range() {
        // Below the overflow threshold the clamp is a no-op: the guarded
        // factor must equal the plain `exp(b·l)·cos(2π·l)` computation.
        for &(l, b) in &[(0.3_f32, 1.0_f32), (-0.7, 2.0), (0.25, 10.0)] {
            let expected: f32 = (b * l).exp() * (2.0 * PI * l).cos();
            let got: f32 = spiral_factor(l, b);
            approx::assert_relative_eq!(got, expected, epsilon = 1e-6);
        }
    }

    #[test]
    fn best_is_none_until_first_tell() {
        let device = Default::default();
        let strategy = WhaleOptimization::<TestBackend>::new();
        let params = WoaConfig::default_for(4, 3);
        let mut rng = StdRng::seed_from_u64(0);
        let state = strategy.init(&params, &mut rng, &device);
        assert!(strategy.best(&state).is_none());
        let (pop, state) = strategy.ask(&params, &state, &mut rng, &device);
        let fitness = finite_fitness(4, &device);
        let (state, _m) = strategy.tell(&params, pop, fitness, state, &mut rng);
        assert!(strategy.best(&state).is_some());
    }

    #[test]
    fn ask_keeps_positions_in_bounds() {
        // Every position proposed by the encircle/search/spiral composition is
        // clamped to bounds; verify across seeds.
        let device = Default::default();
        let strategy = WhaleOptimization::<TestBackend>::new();
        let params = WoaConfig::default_for(6, 4);
        let (lo, hi): (f32, f32) = params.bounds.into();
        for seed in 0..32 {
            let mut rng = StdRng::seed_from_u64(seed);
            let state = strategy.init(&params, &mut rng, &device);
            let (pop1, state) = strategy.ask(&params, &state, &mut rng, &device);
            let fitness = finite_fitness(6, &device);
            let (state, _m) = strategy.tell(&params, pop1, fitness, state, &mut rng);
            let (pop2, _state) = strategy.ask(&params, &state, &mut rng, &device);
            let values = pop2.into_data().into_vec::<f32>().unwrap();
            for v in values {
                assert!(
                    v >= lo - 1e-4 && v <= hi + 1e-4,
                    "seed {seed}: position {v} out of bounds [{lo}, {hi}]"
                );
            }
        }
    }

    #[test]
    fn pop_size_two_runs() {
        // The smallest population where the "search toward a random *other*
        // whale" branch has a distinct target to pick.
        let device = Default::default();
        let strategy = WhaleOptimization::<TestBackend>::new();
        let params = WoaConfig::default_for(2, 3);
        let fitness_fn = FromFitnessEvaluable::new(SphereFit, Sphere);
        let mut harness = EvolutionaryHarness::<TestBackend, _, _>::new(
            strategy, params, fitness_fn, 0, device, 5,
        )
        .expect("valid params");
        harness.reset();
        while !harness.step(()).done {}
        assert!(
            harness
                .latest_metrics()
                .unwrap()
                .best_fitness_ever()
                .is_finite()
        );
    }
}