heuropt

A practical Rust toolkit for heuristic optimization. Single-objective. Multi-objective. Many-objective. 33 algorithms — every one of them with a sync run and an async run_async. One small set of traits. Bit-identical seeded determinism. No trait objects, no GATs, no generic-RNG plumbing in the public API.

If you can write a Problem impl and read Random Search, you can write your own optimizer. That's the whole pitch.

Docs: user guide · API reference.

Installation

[dependencies]
heuropt = "0.11"

# Optional features:
# - "serde":     derive Serialize/Deserialize on the core data types.
# - "parallel":  evaluate populations across rayon's thread pool.
#                Seeded runs stay bit-identical to serial mode.
# - "async":     AsyncProblem / AsyncPartialProblem traits and a
#                run_async(&problem, concurrency).await method on
#                every algorithm — for IO-bound evaluations.
# heuropt = { version = "0.11", features = ["serde", "parallel", "async"] }

Define a problem and run an optimizer

You're designing a car. Three things you can pick: engine displacement (1.0–6.0 L), curb weight (1100–2200 kg, where going lighter requires aluminum/carbon and costs money), and aerodynamic drag (Cd from 0.20 to 0.40, where slipperier needs expensive aero R&D). Four things you want to optimize: price, 0-60 acceleration, fuel consumption, idle noise — all in tension.

The relationships between decisions and objectives are nonlinear and coupled: engine cost grows superlinearly with displacement, weight reduction below 1500 kg costs a quadratic premium, drag reduction below 0.35 Cd costs a 1.5-power premium, and 0-60 depends on weight × engine in a non-trivial way. You can't just sweep one slider — the Pareto front is a genuine surface in 3D decision space, and finding it by hand is hopeless.

NSGA-III is the canonical many-objective (4+) optimizer; it uses Das–Dennis reference points to keep the front well-spread.

use heuropt::prelude::*;

struct PickACar;

impl Problem for PickACar {
    type Decision = Vec<f64>; // [engine_liters, weight_kg, drag_cd]

    fn objectives(&self) -> ObjectiveSpace {
        ObjectiveSpace::new(vec![
            Objective::minimize("price_thousand_dollars"),
            Objective::minimize("seconds_to_60mph"),
            Objective::minimize("fuel_gallons_per_100mi"),
            Objective::minimize("noise_db_at_idle"),
        ])
    }

    fn evaluate(&self, x: &Vec<f64>) -> Evaluation {
        let displacement = x[0]; // liters
        let weight       = x[1]; // kg
        let drag         = x[2]; // dimensionless Cd

        // Price ($k): engine cost grows superlinearly; weight reduction
        // below 1500 kg and drag reduction below 0.35 Cd both cost extra.
        let engine_cost = 3.0 * displacement.powf(1.6);
        let weight_cost = ((1500.0 - weight).max(0.0) / 100.0).powi(2) * 2.0;
        let aero_cost   = ((0.35 - drag).max(0.0) * 100.0).powf(1.5) * 0.4;
        let price = 10.0 + engine_cost + weight_cost + aero_cost;

        // 0-60 (s): heavier = slower; bigger engine = quicker but with
        // diminishing returns.
        let weight_factor = (weight - 1100.0) / 1000.0;
        let engine_factor = ((displacement - 1.0) / 5.0).max(0.0).powf(0.7);
        let zero_to_sixty = 5.0 + 5.0 * weight_factor - 4.0 * engine_factor;

        // Fuel consumption (gal/100 mi): all three matter.
        let fuel = 0.5 + 0.5 * displacement + 0.5 * weight / 1000.0 + 4.0 * drag;

        // Idle noise (dB): engine dominates, mildly nonlinear.
        let noise = 60.0 + 3.0 * displacement.powf(1.2);

        Evaluation::new(vec![price, zero_to_sixty, fuel, noise])
    }
}

fn main() {
    let bounds = vec![
        (1.0_f64, 6.0_f64),       // engine
        (1100.0_f64, 2200.0_f64), // weight
        (0.20_f64, 0.40_f64),     // drag
    ];

    let mut optimizer = Nsga3::new(
        Nsga3Config {
            population_size: 100,
            generations: 200,
            reference_divisions: 5,
            seed: 42,
        },
        RealBounds::new(bounds.clone()),
        CompositeVariation {
            crossover: SimulatedBinaryCrossover::new(bounds.clone(), 15.0, 0.9),
            mutation:  PolynomialMutation::new(bounds, 20.0, 1.0 / 3.0),
        },
    );
    let result = optimizer.run(&PickACar);

    let mut front: Vec<_> = result.pareto_front.iter().collect();
    front.sort_by(|a, b| {
        a.evaluation.objectives[0]
            .partial_cmp(&b.evaluation.objectives[0]).unwrap()
    });
    println!("{:>5} {:>5} {:>4}    {:>6} {:>5} {:>5} {:>5}",
             "L", "kg", "Cd", "$k", "0-60", "fuel", "dB");
    for c in &front {
        let d = &c.decision;
        let o = &c.evaluation.objectives;
        println!("{:>5.2} {:>5.0} {:>4.2}    {:>6.1} {:>5.1} {:>5.2} {:>5.1}",
                 d[0], d[1], d[2], o[0], o[1], o[2], o[3]);
    }
}

Run it (cargo run --release) and you get 100 cars on the front. A representative slice from the actual output, hand-picked across the spectrum:

    L    kg   Cd        $k  0-60  fuel    dB     ← role
 1.00  1505 0.35      13.0   7.0  3.17  63.0     cheap baseline
 2.00  1370 0.35      22.4   5.1  3.56  66.7     sensible sport sedan
 2.45  1330 0.38      28.5   4.5  3.92  68.8     quicker midprice
 1.00  1430 0.21      35.8   6.6  2.54  63.0     fuel-saver (small + slippery)
 3.50  1300 0.25      52.9   3.5  3.88  73.3     genuine sports car
 5.27  1100 0.20     108.1   1.4  4.48  82.0     hypercar corner

Reading the result

Every row is non-dominated — no row is strictly better than another on every metric. The interesting part is what each one does differently:

• The cheap baseline ($13k) takes the path of least resistance: smallest engine, no weight reduction, average drag. Slow but affordable.
• The sensible sedan ($22k) trades $9k for 2 seconds off 0-60 by running a 2.0L engine with mild weight reduction.
• The fuel-saver is interesting: it's a 1.0L econobox engine, but it spends $22k just on aero (0.21 Cd) to push fuel consumption down to 2.54 gal/100mi. The optimizer figured out that aero matters more than displacement at this fuel point. No human would pick this combo by intuition.
• The sports car ($53k) doesn't blow money on the lightest possible weight — it picks 1300 kg, because dropping further costs disproportionately and the 3.5L engine is doing most of the acceleration work.
• The hypercar corner ($108k) is the optimizer pushing every decision to its ceiling: minimum weight (1100 kg), minimum drag (0.20 Cd), big engine (5.3L). Sub-1.5 second 0-60, but you pay for it on every other axis except fuel (because the weight + aero savings partly cancel the V8's thirst).

That last point is the kind of insight a Pareto front gives you that no single-objective optimizer would: the cheapest fuel- efficient car is not the smallest engine alone, it's a small engine + aggressive aero. The lightest sports car is not the lightest possible, it's the point where weight cost stops paying back in 0-60. The optimizer doesn't tell you what to buy — it hands you the frontier of every defensible compromise and lets you pick by your own priorities.

Explore it interactively

Six hand-picked rows out of a hundred is a sample, not a search. With the serde feature enabled, the same result becomes one JSON file you can drop into the heuropt-explorer webapp to browse interactively — parallel coordinates, scatter, range filters, weighted ranking:

heuropt::explorer::ExplorerExport::from_result(&PickACar, &result)
    .with_algorithm_info(&optimizer)
    .with_problem_name("Pick a car")
    .to_file("results.json")?;

The full worked example (which produces this output verbatim) is at examples/pick_a_car.rs:

cargo run --release --example pick_a_car --features serde

See the Explore your results cookbook recipe for the export schema and how to enrich your Problem with display labels and units.

Implement a custom optimizer

A new optimizer is just an implementation of Optimizer<P>:

use heuropt::prelude::*;

struct MyOptimizer { /* state */ }

impl<P> Optimizer<P> for MyOptimizer
where
    P: Problem<Decision = Vec<f64>>,
{
    fn run(&mut self, problem: &P) -> OptimizationResult<P::Decision> {
        // Generate candidates.
        // Evaluate them with `problem.evaluate(...)`.
        // Keep the best, or maintain a Pareto archive.
        // Return an OptimizationResult.
        todo!()
    }
}

A complete worked example is in examples/custom_optimizer.rs.

Choosing an algorithm

Optimization is a noisy field with a lot of jargon. This section walks you through picking a starting algorithm for a real problem, defining the terms as they come up. If you already know the vocabulary, jump to the quick-reference table at the bottom.

Step 1: What is your problem?

Three ingredients describe any optimization problem:

• A decision — the thing the algorithm is allowed to change. Examples: five real numbers (Vec<f64>), a yes/no flag for each of 100 features (Vec<bool>), or an ordering of cities to visit (Vec<usize>).
• One or more objectives — numbers you want to make small (or large). Examples: a model's prediction error, a tour's total length, a circuit's power draw.
• An optional set of constraints — conditions a decision must satisfy to be valid. Examples: "the budget cannot exceed $1M," or "every car must be visited exactly once."

Your job is to express the problem; heuropt's job is to search for decisions that score well on the objectives without violating the constraints.

Step 2: How many objectives?

The biggest fork in the road. Algorithms specialize sharply by objective count:

• Single-objective (1) — one number to optimize. There's a clear "best" answer. Examples: minimize loss, maximize throughput.
• Multi-objective (2 or 3) — several conflicting goals. There is no single best; instead there is a Pareto front: the set of decisions where you cannot improve any objective without sacrificing another. Each point on the front is a different tradeoff.
• Many-objective (4+) — same idea, but classical multi-objective algorithms break down because almost every pair of points is non-dominated (neither one is strictly better) once you have lots of objectives.

Dominance: Decision A dominates decision B if A is at least as good as B on every objective and strictly better on at least one. The Pareto front is what you get after deleting every dominated decision.

If you found yourself staring at a single composite score that's a weighted sum of conflicting goals, you probably actually have a multi-objective problem in disguise.

Step 3: What does the search space look like?

A few questions about the geometry of your problem:

• Is the decision continuous (real numbers), discrete (integers, bits), or a permutation (an ordering)?
• Is the landscape unimodal (one hill, easy to climb) or multimodal (lots of local optima that aren't the global one)? Rastrigin and Ackley are classic multimodal traps.
• How smooth is it? Smooth landscapes (e.g., a quadratic bowl) reward gradient-like methods (CMA-ES); jagged or noisy ones reward population-based methods (DE, GA).

If you don't know, treat it as multimodal — it's the cautious default.

Step 4: How expensive is each evaluation?

Cheap evaluations (a few microseconds — pure math, simple simulation) let you afford 100k+ evaluations per run. Expensive evaluations (a training run, a CFD simulation, a real-world measurement that costs money) force you to be sample-efficient: 50–500 evaluations total.

This decides whether you can afford a population-based algorithm that throws hundreds of evaluations at each generation, or whether you need a sample-efficient or multi-fidelity approach:

• Cheap (1k+ evals affordable): any of the population-based algorithms — DE, GA, CMA-ES, NSGA-II, etc.
• Expensive (50–500 evals): Bayesian Optimization (Gaussian-process surrogate + Expected Improvement) or TPE (Parzen-density surrogate, cheaper per step, more robust without hyperparameter tuning).
• Multi-fidelity (each eval has a tunable budget — epochs, sim steps, MC samples): Hyperband. Implement the PartialProblem trait on your problem and Hyperband allocates compute aggressively across promising configs.

The parallel feature flag also matters here — if your evaluate function takes more than ~50 µs, enabling rayon-backed parallel population evaluation will speed runs up significantly.

Step 5: Are there hard constraints?

heuropt models constraints as a single scalar constraint violation on each Evaluation. The convention: 0.0 (or negative) means feasible; positive means infeasible, and bigger numbers are worse violations. Every Pareto-comparison and tournament-selection helper in the crate prefers feasible candidates and breaks ties on violation magnitude, so the rule "feasibility comes first" is enforced automatically.

If your constraints are very tight and the search keeps hitting them, you have three options:

• Repair: implement the Repair<D> trait (or use the provided ClampToBounds / ProjectToSimplex impls) to in-place project infeasible decisions back into the feasible region. Pair with a Variation operator to get bounds-aware variants without writing a custom Variation impl.
• Stochastic ranking: use stochastic_ranking_select instead of tournament_select_single_objective. It probabilistically explores near-feasibility instead of strict feasibility-first ordering, which helps when feasible regions are narrow.
• Penalty-only: stick with constraint_violation — the simplest, works well when the feasible region is large and convex.

---

The decision tree

A flow you can run mentally:

START
 │
 ├─ Is each evaluation EXPENSIVE (>1 sec) or BUDGETED (50–500 total)?
 │   │
 │   ├─ Yes → sample-efficient regime
 │   │    ├─ Standard expensive black-box, single-objective
 │   │    │     → Bayesian Optimization (GP + Expected Improvement; gold
 │   │    │                      standard *with* per-problem kernel
 │   │    │                      tuning. The default RBF kernel at
 │   │    │                      60 evals is honestly bad — give it
 │   │    │                      more evals or tune the kernel.)
 │   │    │     → TPE           (KDE-based; cheaper per-step,
 │   │    │                      more robust without tuning)
 │   │    │
 │   │    └─ Each eval has a tunable fidelity (epochs, sim steps, …)
 │   │          → Hyperband     (implement PartialProblem; allocates
 │   │                           compute across configs adaptively)
 │   │
 │   └─ No → continue to the population-based branches below
 │
 └─ How many objectives?
     │
     ├─ 1 (single-objective)
     │    │
     │    ├─ Decision is Vec<f64> (continuous)
     │    │   ├─ Smooth landscape (well-conditioned)
     │    │   │     → CMA-ES       (full-cov adaptive Gaussian)
     │    │   │     → sNES         (cheaper diag-cov; high-dim)
     │    │   │     → Nelder-Mead  (low-dim, deterministic, simple)
     │    │   ├─ Multimodal landscape
     │    │   │     → IPOP-CMA-ES          (CMA-ES with restart;
     │    │   │                              fixes vanilla CMA-ES's
     │    │   │                              multimodal failure)
     │    │   │     → Differential Evolution (rarely beaten on cheap
     │    │   │                              multimodal continuous)
     │    │   │     → Simulated Annealing  (cheap & generic)
     │    │   ├─ Want parameter-free (no F, CR, w, σ to tune)
     │    │   │     → TLBO
     │    │   ├─ Want minimum self-adapting baseline
     │    │   │     → (1+1)-ES              (one-fifth rule,
     │    │   │                               smallest possible ES)
     │    │   ├─ Just want a strong default for cheap continuous
     │    │   │     → Differential Evolution
     │    │   └─ Just want a baseline
     │    │         → Random Search
     │    │
     │    ├─ Decision is Vec<bool> (binary)
     │    │   ├─ Independent bits, smooth fitness
     │    │   │     → UMDA            (per-bit marginal EDA)
     │    │   └─ Bit interactions matter
     │    │         → GA with BitFlipMutation +
     │    │           a bit-string crossover
     │    │
     │    ├─ Decision is Vec<usize> (permutation: TSP, JSS, …)
     │    │     → Ant Colony (TSP, with a distance matrix)
     │    │     → Simulated Annealing / Tabu Search (strong on
     │    │        sequencing — they win the harness TSP and JSS
     │    │        tables — you supply the neighbour move)
     │    │     → GA + permutation toolkit (ERX for TSP-shaped
     │    │        instances)
     │    │
     │    └─ Custom decision type (a struct, a tree, …)
     │          → Simulated Annealing or Hill Climber
     │            with your own Variation impl
     │
     ├─ 2 or 3 (multi-objective)
     │    │
     │    ├─ Strong default — top-3 on every multi- and
     │    │  many-objective table on the harness, fastest or
     │    │  near-fastest every time
     │    │     → MOEA/D   (decomposition into scalar sub-problems;
     │    │                 robust across convex / disconnected /
     │    │                 spherical / linear fronts and 2–10
     │    │                 objectives. Caveat: weight-vector spread
     │    │                 can leave gaps on highly irregular or
     │    │                 degenerate fronts)
     │    │     → NSGA-II  (canonical Pareto EA; well-understood and
     │    │                 the established choice for combinatorial
     │    │                 encodings — but edged out by MOEA/D on
     │    │                 every MO table here, and fades past
     │    │                 ~4 objectives)
     │    │
     │    ├─ Real-valued, smooth front, want best convergence
     │    │     → MOPSO    (multi-objective PSO; on the benches
     │    │                 here it wins ZDT1 on both HV and
     │    │                 convergence by 100× over the
     │    │                 dominance-based methods)
     │    │
     │    ├─ Want better front quality than the default
     │    │     → IBEA     (indicator-based; consistently the best
     │    │                 of the dominance-based methods on these
     │    │                 benches — wins ZDT3 HV and DTLZ2 mean
     │    │                 dist by 24×)
     │    │     → SPEA2    (strength + density)
     │    │     → SMS-EMOA (hypervolume-contribution selection;
     │    │                 elegant in theory but underperforms
     │    │                 NSGA-II on these benches at our budgets —
     │    │                 only worth its higher per-step cost on
     │    │                 fronts where exact HV-contribution is
     │    │                 the right discriminator)
     │    │
     │    ├─ Disconnected front (separate arcs, e.g. ZDT3)
     │    │     → IBEA     (wins ZDT3 hypervolume on the harness;
     │    │                 MOEA/D and NSGA-II follow. Geometry-aware
     │    │                 methods trail when the front is in pieces)
     │    │
     │    ├─ Non-convex but *contiguous* front
     │    │     → AGE-MOEA (estimates front geometry adaptively)
     │    │     → KnEA     (favors knee points)
     │    │
     │    ├─ Want region-based diversity
     │    │     → PESA-II  (grid hyperboxes drive selection)
     │    │     → ε-MOEA   (ε-grid archive,
     │    │                  archive size auto-limits)
     │    │
     │    └─ Just one starting decision (no population budget)
     │          → PAES  (1+1 ES with a Pareto archive)
     │
     └─ 4+ (many-objective)
          │
          ├─ Strong default — #2 on every many-objective table on
          │  the harness (DTLZ2 at 4 and 10 objectives, DTLZ1 at 8);
          │  decomposition sidesteps the dominance collapse that
          │  wrecks Pareto-based EAs at high objective count
          │     → MOEA/D
          │     (NSGA-II is the cautionary tale: on DTLZ2 at 10
          │      objectives it finishes last — behind random search)
          │
          ├─ Linear / simplex-shaped front (e.g., DTLZ1)
          │     → GrEA       (grid coords drive ranking; on DTLZ1
          │                    here it beats NSGA-III by 3× and
          │                    AGE-MOEA by 2.5×, and wins the
          │                    8-objective DTLZ1 table outright)
          │     → MOEA/D     (also #2 on both DTLZ1 tables)
          │
          ├─ Curved / unknown front geometry
          │     → NSGA-III   (reference-point niching; canonical by
          │                    reputation, but MOEA/D outperforms it
          │                    on every harness table)
          │     → AGE-MOEA   (estimates L_p geometry per generation)
          │     → RVEA       (reference vectors with adaptive penalty)
          │
          ├─ Want indicator-based selection
          │     → IBEA       (additive ε-indicator; doesn't degrade
          │                   at high obj count)
          │     → HypE       (Monte Carlo HV estimation; scales
          │                   to arbitrary M)

Quick reference

Sample-efficient / expensive evaluation (50–500 evals):

Algorithm	Objectives	Decision	Strengths
Bayesian Optimization	1	Vec<f64>	GP surrogate + EI; gold standard with per-problem kernel tuning (default RBF at 60 evals is honestly bad)
TPE	1	Vec<f64>	KDE surrogate; robust without hyperparameter tuning
Hyperband	1	any	multi-fidelity; needs PartialProblem

Single-objective continuous (Vec<f64>):

Algorithm	Strengths
Random Search	sanity baseline
Hill Climber	simplest greedy local search
(1+1)-ES	one-fifth-rule self-adapting baseline
Simulated Annealing	escapes local optima
GA	classic SO GA with elitism
PSO	simple swarm baseline
Differential Evolution	strong default for cheap continuous
TLBO	parameter-free (no F, CR, w, σ)
CMA-ES	smooth landscapes; full covariance
IPOP-CMA-ES	CMA-ES + restart for multimodal
sNES	diagonal-cov NES; cheap per-step
Nelder-Mead	classical simplex; deterministic

Single-objective other decision types:

Algorithm	Decision	Strengths
UMDA	Vec<bool>	independent-bit EDA
Tabu Search	any	discrete, you supply neighbors
Ant Colony	Vec<usize>	TSP / permutation

Multi-objective (2–3) and many-objective (4+):

Algorithm	Objectives	Strengths
MOEA/D	2+	decomposition; the most consistent all-rounder — top-3 on every MO/many-objective table here, fastest or near-fastest
NSGA-II	2–3	canonical Pareto-based EA; well-understood, the go-to for combinatorial encodings — but fades past ~4 objectives
MOPSO	2–3	multi-objective PSO; best convergence on smooth real-valued 2-obj fronts
IBEA	2+	indicator-based; consistently best of the dominance-based methods; wins disconnected fronts
SPEA2	2–3	strength + density
SMS-EMOA	2+	exact HV-contribution selection; high per-step cost, modest gain
HypE	2+	Monte Carlo HV estimation; strong on spherical many-objective fronts
ε-MOEA	2+	ε-grid archive; auto-sized
PESA-II	2+	grid-based region selection
AGE-MOEA	2+	adaptive front-geometry estimation
KnEA	2+	knee-point favored survival
PAES	2–3	1+1 ES with Pareto archive
NSGA-III	4+	reference-point niching; strong on curved fronts
RVEA	4+	reference vectors with penalty
GrEA	4+	grid coords drive selection; wins linear/simplex fronts at any objective count

Current algorithms

The full list with one-line descriptions:

Sample-efficient / multi-fidelity:

• Bayesian Optimization — Gaussian-process surrogate + Expected Improvement.
• TPE — Bergstra et al. 2011 Tree-structured Parzen Estimator.
• Hyperband — Li et al. 2017 multi-fidelity (uses PartialProblem).

Single-objective:

• Random Search — sample-evaluate-keep baseline.
• Hill Climber — greedy single-step local search.
• (1+1)-ES — Rechenberg 1973 (1+1)-ES with one-fifth rule.
• Simulated Annealing — Kirkpatrick et al. 1983, generic over decision type.
• Tabu Search — Glover 1986, with a user-supplied neighbor generator.
• GA — generational GA with tournament selection + elitism.
• PSO — Eberhart & Kennedy 1995 PSO for Vec<f64>.
• Differential Evolution — Storn & Price DE/rand/1/bin for Vec<f64>.
• TLBO — Rao 2011 Teaching-Learning-Based Optimization (parameter-free).
• CMA-ES — Hansen & Ostermeier 2001 covariance-matrix adaptation.
• IPOP-CMA-ES — Auger & Hansen 2005 CMA-ES with restart, for multimodal.
• sNES — Wierstra et al. 2008/2014 diagonal-cov NES.
• Nelder-Mead — Nelder & Mead 1965 simplex direct search.
• UMDA — Mühlenbein 1997 univariate marginal-distribution EDA for Vec<bool>.
• Ant Colony — Dorigo Ant System for permutation problems.

Multi-objective:

• PAES — Knowles & Corne 1999 Pareto Archived Evolution Strategy.
• NSGA-II — Deb et al. 2002, the canonical Pareto-based EA.
• SPEA2 — Zitzler, Laumanns & Thiele 2001 strength-Pareto EA.
• MOEA/D — Zhang & Li 2007 decomposition-based MOEA with Tchebycheff scalarization.
• MOPSO — Coello, Pulido & Lechuga 2004 multi-objective PSO.
• IBEA — Zitzler & Künzli 2004 indicator-based EA.
• SMS-EMOA — Beume, Naujoks & Emmerich 2007 hypervolume-selection EMOA.
• HypE — Bader & Zitzler 2011 Hypervolume Estimation Algorithm.
• ε-MOEA — Deb, Mohan & Mishra 2003 ε-dominance MOEA.
• PESA-II — Corne et al. 2001 Pareto Envelope Selection II.
• AGE-MOEA — Panichella 2019 Adaptive Geometry Estimation MOEA.
• KnEA — Zhang, Tian & Jin 2015 Knee point-driven EA.

Many-objective (4+):

• NSGA-III — Deb & Jain 2014 reference-point NSGA-III.
• RVEA — Cheng et al. 2016 Reference Vector-guided EA.
• GrEA — Yang et al. 2013 Grid-based EA.

Reusable utilities: pareto_compare, pareto_front, best_candidate, non_dominated_sort, crowding_distance, ParetoArchive, das_dennis, and the metrics spacing and hypervolume_2d.

Design philosophy

• Concrete data, small trait surface. Problem, Optimizer, Initializer, Variation are the only traits a user interacts with day-to-day. Everything else is plain structs.
• No type hell. No trait objects in the core path, no GATs, no HRTBs in user-facing APIs, no generic-RNG plumbing — Rng is a single concrete type alias.
• Readable algorithms. Built-ins are written for clarity, not maximum abstraction reuse. Random Search is the recommended file to read before writing your own optimizer.
• One crate first. No premature splitting into -core/-algorithms/ -operators. Split later if the crate grows.
• Panic on programmer error. Invalid configuration panics with a clear message in v1; the API may grow Result-returning variants later if the base API proves useful.

See docs/heuropt_tech_design_spec.md for the full design rationale.

Testing

heuropt is exhaustively tested across several layers:

• Unit + integration tests (cargo test) — 313 tests covering every algorithm, operator, metric, Pareto utility, and edge case (empty/singleton/duplicate populations, flat fitness, zero-width bounds, infeasible-only populations).
• Property-based tests (proptest) — bounds preservation, Pareto antisymmetry/reflexivity, partition correctness, determinism, and seed-stability checks for every algorithm.
• Coverage-guided fuzzing (cargo +nightly fuzz run <target>) — eight targets at fuzz/fuzz_targets/, soaked for 60 s per target in CI on every PR.
• Instruction-count benchmarks (cargo bench) — gungraun (callgrind) hot-path benchmarks for every algorithm and Pareto utility, machine-stable so PR-level regressions show up.
• Mutation testing (cargo mutants) — advisory; config at .cargo/mutants.toml.
• CI (.github/workflows/ci.yml) — fmt, clippy (-D warnings), test (4-feature matrix), doc, MSRV (1.85), fuzz.

Contributing

See CONTRIBUTING.md for the local-test checklist, conventional-commits requirement, and project-governance docs.

This project follows the Builder's Code of Conduct: stay professional, stay technical, focus on the work and its merit.

For security disclosures, see SECURITY.md.

License

MIT — see LICENSE.

Changelog

See CHANGELOG.md.