feat: v0.5.0 — comprehensive documentation release
Theme: documentation and project polish. No public-API changes; this is the v0.5 release that elevates heuropt's docs/onboarding/governance to bar-setting status. Adds: - mdbook user guide at docs/book/ with intro, getting-started, defining-problems, choosing-an-algorithm, cookbook (7 recipes), comparison vs other libraries, stability/SemVer, migration guides. Deploys to https://swaits.github.io/heuropt/ via .github/workflows/ docs.yml. - Runnable rustdoc examples on every algorithm (35 of them), all exercised by cargo test --doc. - Three real-world examples: portfolio.rs (multi-obj with budget constraint), hyperparam_tuning.rs (BO + TPE), scheduling.rs (permutation via SA + SwapMutation against Smith's-rule oracle). - Governance: CONTRIBUTING.md, SECURITY.md, CODE_OF_CONDUCT.md (adopting builderscode.org's Builder's Code of Conduct), GitHub issue templates, PR template. Polishes: - README hero with badges + user-guide link. - lib.rs crate-level docs. - CHANGELOG entry for 0.5.0. Bumps Cargo.toml to 0.5.0.
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# Choosing an algorithm
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The README has a compact decision tree. This chapter expands it with
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the *reasoning* behind each branch.
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## Step 0: How expensive is one evaluation?
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This is the first fork because it changes everything that comes
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after it.
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| Eval cost | Budget you can afford | Algorithm family |
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|----------------------------|---------------------------|-----------------------------|
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| Microseconds (pure math) | 10 000 – 1 000 000 evals | Population-based |
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| Milliseconds (sim, IO) | 1 000 – 10 000 evals | Population-based |
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| Seconds (small training) | 100 – 1 000 evals | Sample-efficient (BO, TPE) |
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| Minutes+ (full training) | 50 – 500 evals | Sample-efficient + multi-fidelity |
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For the cheap-eval branch, you have the run of the catalog. For the
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expensive branch, classical evolutionary methods waste your evaluation
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budget — go to [`BayesianOpt`] or [`Tpe`]. For the *very* expensive
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branch where each eval has a tunable budget (epochs, MC samples, sim
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steps), [`Hyperband`] over the [`PartialProblem`] trait is the move.
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## Step 1: How many objectives?
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The biggest fork.
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- **One** — there's a single best answer. Pick from the
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single-objective branch.
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- **Two or three** — a Pareto front. Pick from the multi-objective
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branch.
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- **Four or more** — a many-objective Pareto front; classical
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multi-objective methods break down here because almost every pair
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of points is non-dominated. Pick from the many-objective branch.
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> **Pareto front:** the set of decisions where you cannot improve any
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> objective without sacrificing another. In a 2-objective minimize
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> problem, plot every solution; the Pareto front is the lower-left
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> envelope.
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If you found yourself staring at a single composite score that's a
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weighted sum of conflicting goals, you probably have a multi-objective
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problem in disguise. A weighted sum bakes in your preferences before
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you've seen the trade-off; running a multi-objective optimizer first
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and picking off the front later is almost always a better workflow
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(see [Pick one answer off a Pareto front](./cookbook/pick-one.md)).
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## Step 2 — single-objective continuous
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These all take `Vec<f64>` decisions.
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### Smooth, low-to-moderate dimension
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[`CmaEs`] is the strong default. It adapts the search distribution's
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covariance to the local landscape. On the comparison harness it
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hits machine epsilon on Rosenbrock at 30 000 evaluations.
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For very low-dimensional smooth problems (≤ 5 dim), [`NelderMead`] is
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deterministic and converges to f = 0 exactly on Rosenbrock.
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### High dimension, smooth
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[`SeparableNes`] uses a diagonal covariance — cheaper per step than
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CmaEs at the cost of being unable to model rotated landscapes. Worth
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trying when CmaEs's `O(d²)` per-step cost hurts.
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### Multimodal landscapes
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Multimodal = many local minima that aren't the global one. Rastrigin
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and Ackley are classic traps.
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[`IpopCmaEs`] is CmaEs with an increasing-population restart strategy
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specifically designed for this. On the harness it drops vanilla CmaEs's
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Rastrigin score from f = 2.35 to f = 0.13.
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[`DifferentialEvolution`] is rarely beaten on cheap multimodal
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continuous problems. On Rastrigin it ties with `(1+1)-ES` at f = 0.
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[`SimulatedAnnealing`] is a cheap, generic baseline that escapes local
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optima via temperature decay.
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### Want parameter-free
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[`Tlbo`] (Teaching-Learning-Based Optimization) has no `F`, `CR`, `w`,
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or `σ` to tune. Often a respectable middle-of-the-pack performer.
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### Smallest possible self-adapting baseline
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[`OnePlusOneEs`] — Rechenberg's 1973 `(1+1)`-ES with the one-fifth
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success rule. On the harness it hits f = 0 on Rastrigin in 50 000
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evaluations.
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### Just want a baseline
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[`RandomSearch`]. Useful as a sanity check: if your fancy optimizer
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can't beat random search, something is wrong (with the fancy
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optimizer or with the problem).
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## Step 2 — single-objective other types
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| Decision type | Algorithm | Notes |
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|---|---|---|
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| `Vec<bool>` | [`Umda`] | Per-bit marginal EDA. Independent-bit assumption. |
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| `Vec<bool>` | [`GeneticAlgorithm`] + [`BitFlipMutation`] | When bit interactions matter. |
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| `Vec<usize>` (permutation) | [`AntColonyTsp`] | TSP-style with a distance matrix. |
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| `Vec<usize>` (permutation) | [`SimulatedAnnealing`] + [`SwapMutation`] | Generic discrete baseline. |
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| `Vec<usize>` or custom | [`TabuSearch`] | You supply the neighbor function. |
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| Custom struct | [`SimulatedAnnealing`] / [`HillClimber`] | With your own `Variation` impl. |
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## Step 2 — multi-objective (2 or 3)
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### Strong default
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[`Nsga2`] is the canonical Pareto-based EA. Fast, well-understood,
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maintains diversity via crowding distance. On the harness it lands
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on the Pareto front of every test problem.
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### Real-valued, smooth front, want best convergence
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[`Mopso`] (multi-objective PSO with archive). On ZDT1 it wins
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hypervolume outright and converges 100× tighter than the
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dominance-based methods.
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### Better front quality than NSGA-II
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[`Ibea`] (indicator-based) is consistently the best of the
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dominance-based methods on the harness — wins ZDT3 hypervolume and
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DTLZ2 mean distance by 24×. It uses an additive ε-indicator for
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selection rather than dominance + crowding.
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[`Spea2`] (strength + density) — solid alternative; explicit external
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archive separate from the population.
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[`SmsEmoa`] uses exact hypervolume contribution for selection. Elegant
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in theory; in practice on the harness budgets here it underperforms
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NSGA-II. Worth the higher per-step cost only when exact HV
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contribution is the right discriminator.
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### Decomposition / weight-vector style
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[`Moead`] decomposes the multi-objective problem into many scalar
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sub-problems (Tchebycheff or weighted sum) and solves them in
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parallel. Very fast per generation; scales naturally to many
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objectives.
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### Disconnected or non-convex front
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[`AgeMoea`] estimates the front geometry adaptively (the L_p
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parameter `p` is fit from data each generation).
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[`Knea`] favors knee points — the regions of the front where small
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gains in one objective cost large losses in another.
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[`Ibea`] also handles disconnected fronts well.
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### Region-based diversity
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[`PesaII`] uses grid hyperboxes to drive selection — divide the
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objective space into a grid, pick from the least-crowded boxes.
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[`EpsilonMoea`] uses an ε-grid archive that auto-limits its size.
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### Just one starting decision (no population budget)
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[`Paes`] — `(1+1)`-ES with a Pareto archive. Cheap, simple, useful
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when your evaluations are expensive enough that you can't afford a
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population.
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## Step 2 — many-objective (4+)
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### Linear / simplex-shaped front (e.g., DTLZ1)
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[`Grea`] — grid coords drive ranking. On DTLZ1 it beats NSGA-III by
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3× and AGE-MOEA by 2.5×.
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[`Moead`] — decomposition shines on linear fronts; second on DTLZ1
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and among the fastest per generation.
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### Curved / unknown front geometry
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[`Nsga3`] — reference-point niching; canonical many-objective method;
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strong default when the front isn't simplex-shaped.
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[`AgeMoea`] — estimates L_p geometry per generation.
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[`Rvea`] — reference vectors with adaptive penalty.
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### Indicator-based selection
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[`Ibea`] — additive ε-indicator; doesn't degrade at high obj count.
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[`HypE`] — Monte Carlo hypervolume estimation; scales to arbitrary
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objective count where exact HV is too expensive.
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## Step 3: Are there hard constraints?
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heuropt models constraints as a single scalar `constraint_violation`
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on each `Evaluation`. Three escalations when the feasibility region
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is hard to find:
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1. **Penalty-only.** Just set `constraint_violation > 0` for
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infeasible decisions. The default tournament/Pareto comparisons
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prefer feasibles automatically.
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2. **Repair.** Implement [`Repair<D>`] (or use the provided
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[`ClampToBounds`] / [`ProjectToSimplex`]) to project infeasible
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decisions back into the feasible region. Pair with a `Variation`
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in a [`CompositeVariation`] for bounds-aware variants.
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3. **Stochastic ranking.** Use [`stochastic_ranking_select`] instead
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of `tournament_select_single_objective`. It probabilistically
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explores near-feasibility instead of strict feasibility-first
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ordering, which helps when feasible regions are narrow.
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See [Constrain your search with `Repair`](./cookbook/constraints.md)
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for worked examples.
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## Step 4: Should you parallelize?
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Enable the `parallel` feature flag if your `evaluate` takes more
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than ~50 µs. Population-based algorithms ([`RandomSearch`], [`Nsga2`],
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[`DifferentialEvolution`], [`Spea2`], [`Ibea`], [`Mopso`], …) batch-
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evaluate via rayon when the feature is on. **Seeded runs stay
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bit-identical** to serial mode.
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```toml
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heuropt = { version = "0.5", features = ["parallel"] }
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```
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## TL;DR table
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| Situation | Pick |
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|---|---|
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| Smooth single-objective continuous | [`CmaEs`] |
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| Multimodal single-objective continuous | [`IpopCmaEs`] or [`DifferentialEvolution`] |
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| Expensive single-objective | [`BayesianOpt`] or [`Tpe`] |
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| Multi-fidelity single-objective | [`Hyperband`] |
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| 2- or 3-objective default | [`Nsga2`] |
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| 2-objective real-valued smooth front | [`Mopso`] |
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| Disconnected / non-convex front | [`Ibea`] |
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| Many-objective default (curved front) | [`Nsga3`] |
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| Many-objective linear / simplex front | [`Grea`] |
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| Permutation problem | [`AntColonyTsp`] |
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| Binary problem | [`Umda`] |
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| Custom decision type | [`SimulatedAnnealing`] + your `Variation` |
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| Sanity baseline | [`RandomSearch`] |
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[`CmaEs`]: https://docs.rs/heuropt/latest/heuropt/algorithms/cma_es/struct.CmaEs.html
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[`IpopCmaEs`]: https://docs.rs/heuropt/latest/heuropt/algorithms/ipop_cma_es/struct.IpopCmaEs.html
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[`SeparableNes`]: https://docs.rs/heuropt/latest/heuropt/algorithms/snes/struct.SeparableNes.html
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[`NelderMead`]: https://docs.rs/heuropt/latest/heuropt/algorithms/nelder_mead/struct.NelderMead.html
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[`DifferentialEvolution`]: https://docs.rs/heuropt/latest/heuropt/algorithms/differential_evolution/struct.DifferentialEvolution.html
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[`SimulatedAnnealing`]: https://docs.rs/heuropt/latest/heuropt/algorithms/simulated_annealing/struct.SimulatedAnnealing.html
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[`Tlbo`]: https://docs.rs/heuropt/latest/heuropt/algorithms/tlbo/struct.Tlbo.html
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[`OnePlusOneEs`]: https://docs.rs/heuropt/latest/heuropt/algorithms/one_plus_one_es/struct.OnePlusOneEs.html
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[`RandomSearch`]: https://docs.rs/heuropt/latest/heuropt/algorithms/random_search/struct.RandomSearch.html
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[`HillClimber`]: https://docs.rs/heuropt/latest/heuropt/algorithms/hill_climber/struct.HillClimber.html
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[`BayesianOpt`]: https://docs.rs/heuropt/latest/heuropt/algorithms/bayesian_opt/struct.BayesianOpt.html
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[`Tpe`]: https://docs.rs/heuropt/latest/heuropt/algorithms/tpe/struct.Tpe.html
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[`Hyperband`]: https://docs.rs/heuropt/latest/heuropt/algorithms/hyperband/struct.Hyperband.html
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[`PartialProblem`]: https://docs.rs/heuropt/latest/heuropt/core/partial_problem/trait.PartialProblem.html
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[`Umda`]: https://docs.rs/heuropt/latest/heuropt/algorithms/umda/struct.Umda.html
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[`GeneticAlgorithm`]: https://docs.rs/heuropt/latest/heuropt/algorithms/genetic_algorithm/struct.GeneticAlgorithm.html
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[`BitFlipMutation`]: https://docs.rs/heuropt/latest/heuropt/operators/struct.BitFlipMutation.html
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[`AntColonyTsp`]: https://docs.rs/heuropt/latest/heuropt/algorithms/ant_colony_tsp/struct.AntColonyTsp.html
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[`SwapMutation`]: https://docs.rs/heuropt/latest/heuropt/operators/struct.SwapMutation.html
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[`TabuSearch`]: https://docs.rs/heuropt/latest/heuropt/algorithms/tabu_search/struct.TabuSearch.html
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[`Nsga2`]: https://docs.rs/heuropt/latest/heuropt/algorithms/nsga2/struct.Nsga2.html
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[`Nsga3`]: https://docs.rs/heuropt/latest/heuropt/algorithms/nsga3/struct.Nsga3.html
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[`Mopso`]: https://docs.rs/heuropt/latest/heuropt/algorithms/mopso/struct.Mopso.html
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[`Ibea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/ibea/struct.Ibea.html
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[`Spea2`]: https://docs.rs/heuropt/latest/heuropt/algorithms/spea2/struct.Spea2.html
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[`SmsEmoa`]: https://docs.rs/heuropt/latest/heuropt/algorithms/sms_emoa/struct.SmsEmoa.html
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[`Moead`]: https://docs.rs/heuropt/latest/heuropt/algorithms/moead/struct.Moead.html
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[`AgeMoea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/age_moea/struct.AgeMoea.html
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[`Knea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/knea/struct.Knea.html
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[`PesaII`]: https://docs.rs/heuropt/latest/heuropt/algorithms/pesa2/struct.PesaII.html
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[`EpsilonMoea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/epsilon_moea/struct.EpsilonMoea.html
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[`Paes`]: https://docs.rs/heuropt/latest/heuropt/algorithms/paes/struct.Paes.html
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[`Grea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/grea/struct.Grea.html
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[`Rvea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/rvea/struct.Rvea.html
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[`HypE`]: https://docs.rs/heuropt/latest/heuropt/algorithms/hype/struct.Hype.html
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[`Repair<D>`]: https://docs.rs/heuropt/latest/heuropt/traits/trait.Repair.html
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[`ClampToBounds`]: https://docs.rs/heuropt/latest/heuropt/operators/struct.ClampToBounds.html
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[`ProjectToSimplex`]: https://docs.rs/heuropt/latest/heuropt/operators/struct.ProjectToSimplex.html
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[`stochastic_ranking_select`]: https://docs.rs/heuropt/latest/heuropt/selection/tournament/fn.stochastic_ranking_select.html
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[`CompositeVariation`]: https://docs.rs/heuropt/latest/heuropt/operators/struct.CompositeVariation.html
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