docs(0.9): release notes, cookbook recipe, README polish

Companion to the feat(explorer) commit. Bumps the version and
brings every cross-referencing doc up to v0.9 currency.

- Cargo.toml: version 0.8.0 -> 0.9.0.
- CHANGELOG: 0.9.0 entry covering the explorer export, the
  Problem-side metadata additions, the AlgorithmInfo trait, the
  pick_a_car example, and the new cookbook recipe.
- README: closing paragraph of the PickACar example points users
  at the explorer with a one-call snippet
  (`ExplorerExport::from_result(...).with_algorithm_info(...)
  .to_file(...)?`). Version snippets bumped 0.8 -> 0.9.
- New cookbook recipe at docs/book/src/cookbook/explorer.md
  covering: enabling the serde feature, enriching Problem with
  labels/units/decision-schema, the export call, the JSON schema,
  and custom decision-type handling.
- SUMMARY.md and cookbook.md link the new recipe.
- migration.md: new "To 0.9" section documenting the additive
  changes (purely backwards-compatible upgrade from 0.8.x).
- introduction.md, comparison.md, choosing-an-algorithm.md,
  stability.md: version refs bumped 0.8 -> 0.9.
- cookbook/parallel.md, cookbook/async.md: version refs bumped
  0.8 -> 0.9.
- getting-started.md: version refs bumped, serde feature
  description expanded to mention the explorer module.
- SECURITY.md: supported-versions table moves to 0.9.x.

.claude/scheduled_tasks.lock

@@ -1 +0,0 @@
-{"sessionId":"ac44d107-52ca-4cd4-9586-ae2fe91bc9f7","pid":2366937,"procStart":"77336928","acquiredAt":1778002505967}

.gitignore

@@ -1,2 +1,5 @@
 /target
 /Cargo.lock
+
+# Generated by `cargo run --example pick_a_car`
+/pick_a_car.json

CHANGELOG.md

@@ -7,6 +7,151 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [0.10.0] — 2026-05-06
+
+Theme: every algorithm now returns its **canonical name** as it
+appears in the literature, with an academic long form available
+alongside, and the docs use those names everywhere. Plus the
+explorer JSON export now carries both forms so display tools can
+show the short name with a hover tooltip for the long one.
+
+No public-API breaks beyond the value of `AlgorithmInfo::name()`,
+which previously returned the Rust type name and now returns the
+literature short name (`"NSGA-II"` vs `"Nsga2"`). If your code
+matched on those strings you'll need to update — but the trait
+shape itself is unchanged and `algorithm.name()` continues to be
+the way to read it.
+
+### Added
+
+- `AlgorithmInfo::full_name(&self) -> &'static str` — academic
+  long form, e.g. `"Non-dominated Sorting Genetic Algorithm II"`.
+  Defaults to `name()` for algorithms whose short and long forms
+  coincide (Random Search, Hill Climber, Tabu Search).
+- Every built-in algorithm overrides `full_name()` with its
+  expanded literature name. Mapping table is in the cookbook
+  recipe at `docs/book/src/cookbook/explorer.md`.
+- `ExplorerExport`'s `RunMeta` gained an optional
+  `algorithm_full_name: Option<String>` field. The
+  `with_algorithm_info()` builder populates both that and
+  `algorithm` from the same `AlgorithmInfo` source. Schema
+  version stays at **1** — the new field is `#[serde(default)]`,
+  so older readers tolerate it and older writers' output still
+  loads cleanly.
+
+### Changed
+
+- `AlgorithmInfo::name()` return values for every built-in
+  algorithm. Examples: `"Nsga2"` → `"NSGA-II"`, `"Cmaes"` →
+  `"CMA-ES"`, `"Mopso"` → `"MOPSO"`, `"Moead"` → `"MOEA/D"`,
+  `"EpsilonMoea"` → `"ε-MOEA"`. Full table in the cookbook recipe.
+- README, mdbook chapters, decision tree, choosing-an-algorithm
+  guide, comparison page, getting-started, defining-problems,
+  cookbook recipes, and migration notes now all use the canonical
+  algorithm names in body prose. Code blocks (which reference the
+  Rust types like `Nsga2::new(...)` or `Nsga2Config { … }`)
+  unchanged — those are still the API.
+- Default `cargo run --release --example pick_a_car` output now
+  reads `"algorithm": "NSGA-III", "algorithm_full_name":
+  "Non-dominated Sorting Genetic Algorithm III"` in the JSON
+  envelope instead of `"Nsga3"`.
+
+### Migration
+
+If you display `optimizer.name()` in your own UI, you'll suddenly
+get the proper short name for free — usually a strict improvement.
+The only break: code that pattern-matched on the Rust-type-shaped
+strings (e.g. `if name == "Nsga3"`) needs updating to the new
+canonical strings. The names are stable now (they match the
+literature), so this is a one-time fix.
+
+[0.10.0]: https://github.com/swaits/heuropt/releases/tag/v0.10.0
+
+## [0.9.0] — 2026-05-06
+
+Theme: explorer JSON export. Real Pareto fronts have 50–200+
+candidates spanning 2–7+ objectives — too many to read as numbers
+in a terminal. 0.9.0 adds a tiny additive surface that turns any
+`OptimizationResult` into a self-describing JSON file you can drop
+into [heuropt-explorer](https://swaits.github.io/heuropt-explorer/)
+to filter, brush, pin, and rank candidates interactively.
+
+No public-API breaks. The new surface lives behind the existing
+`serde` feature and the new methods on `Problem` / the new
+`AlgorithmInfo` trait have working defaults so existing impls
+compile untouched.
+
+### Added
+
+#### Explorer export (the headline feature)
+
+- New `heuropt::explorer` module (gated on the `serde` feature).
+  Defines `ExplorerExport`, `ExplorerCandidate`, `RunMeta`, the
+  `ToDecisionValues` adapter trait, and free functions
+  `to_json` / `to_writer` / `to_file`.
+- Schema is versioned (`SCHEMA_VERSION = 1`); the explorer webapp
+  refuses to load files with an unknown version.
+- `front_rank` is computed once via `non_dominated_sort` at export
+  time and attached to every candidate so downstream tools don't
+  have to re-derive it.
+- `ToDecisionValues` is implemented for `Vec<f64>`, `Vec<bool>`,
+  `Vec<usize>`, and `Vec<i64>` out of the box; users with custom
+  decision types implement it themselves (one method).
+
+#### Problem-side metadata (single source of truth, no duplication)
+
+- `Objective` gained optional `label: Option<String>` and
+  `unit: Option<String>` fields plus fluent builders
+  `.with_label("Price")` / `.with_unit("$k")`. Existing
+  `Objective::minimize("name")` / `Objective::maximize("name")`
+  unchanged. Backwards-compatible at source level and at the JSON
+  level (the new fields use `#[serde(default,
+  skip_serializing_if = "Option::is_none")]`).
+- `Problem` trait gained an optional `fn decision_schema(&self)
+  -> Vec<DecisionVariable>` with default empty impl. Override it
+  to provide pretty names / labels / units / bounds for the
+  explorer; the default produces fallback `x[0]`, `x[1]`, … names.
+- New `DecisionVariable` type at `heuropt::core::DecisionVariable`,
+  re-exported via the prelude. Builder methods: `with_label`,
+  `with_unit`, `with_bounds`.
+
+#### Algorithm metadata for the export header
+
+- New `heuropt::traits::AlgorithmInfo` trait with `name() ->
+  &'static str` (required) and `seed() -> Option<u64>` (default
+  `None`). Every built-in algorithm — all 33 — implements it.
+  Separate from `Optimizer<P>` so multi-fidelity algorithms
+  (Hyperband, which uses `PartialProblem`) implement it uniformly.
+- `ExplorerExport::with_algorithm_info(&optimizer)` pulls the
+  algorithm name and seed from this trait into the export's `run`
+  metadata.
+
+#### Worked example
+
+- New `examples/pick_a_car.rs` (gated on `serde`). Implements the
+  README's `PickACar` multi-objective problem with a fully
+  enriched `decision_schema` and labelled / unit-tagged objectives,
+  runs NSGA-III, and writes `pick_a_car.json` ready to drop into
+  the explorer.
+
+#### Documentation
+
+- New cookbook recipe at `docs/book/src/cookbook/explorer.md`
+  covering Problem enrichment, the export call, the JSON schema,
+  and custom decision-type handling.
+
+### Notes
+
+- The explorer webapp itself lives in a separate repo
+  (`heuropt-explorer`) on its own release cadence. The schema in
+  `heuropt::explorer` is the contract between them; bumping
+  `SCHEMA_VERSION` is reserved for breaking changes.
+- Phase 1 is additive only. No existing test breaks; the lib test
+  count went from 229 to 242 (10 new explorer tests + 3 from the
+  new `Objective` / `DecisionVariable` builders).
+
+[0.9.0]: https://github.com/swaits/heuropt/releases/tag/v0.9.0
+
 ## [0.8.0] — 2026-05-06
 
 Theme: async evaluation, plus the docs / governance / CI catch-up
@@ -552,5 +697,5 @@ Initial release.
   `RandomSearch`, `Nsga2`, and `DifferentialEvolution`. Seeded runs stay
   bit-identical to serial mode.
 
-[Unreleased]: https://github.com/swaits/heuropt/compare/v0.8.0...HEAD
+[Unreleased]: https://github.com/swaits/heuropt/compare/v0.10.0...HEAD
 [0.1.0]: https://github.com/swaits/heuropt/releases/tag/v0.1.0

Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "heuropt"
-version = "0.9.0"
+version = "0.10.0"
 edition = "2024"
 rust-version = "1.85"
 authors = ["Stephen Waits <steve@waits.net>"]

README.md

@@ -12,7 +12,7 @@ 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 `RandomSearch`, you can write your
+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](https://swaits.github.io/heuropt/) · [API reference](https://docs.rs/heuropt).
@@ -21,7 +21,7 @@ Docs: [user guide](https://swaits.github.io/heuropt/) · [API reference](https:/
 
 ```toml
 [dependencies]
-heuropt = "0.8"
+heuropt = "0.10"
 
 # Optional features:
 # - "serde":     derive Serialize/Deserialize on the core data types.
@@ -30,7 +30,7 @@ heuropt = "0.8"
 # - "async":     AsyncProblem / AsyncPartialProblem traits and a
 #                run_async(&problem, concurrency).await method on
 #                every algorithm — for IO-bound evaluations.
-# heuropt = { version = "0.8", features = ["serde", "parallel", "async"] }
+# heuropt = { version = "0.10", features = ["serde", "parallel", "async"] }
 ```
 
 ## Define a problem and run an optimizer
@@ -186,6 +186,32 @@ 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](https://swaits.github.io/heuropt-explorer/)
+webapp to browse interactively — parallel coordinates, scatter,
+range filters, weighted ranking:
+
+```rust,ignore
+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`:
+
+```text
+cargo run --release --example pick_a_car --features serde
+```
+
+See the [Explore your results](https://swaits.github.io/heuropt/cookbook/explorer.html)
+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>`:
@@ -288,12 +314,12 @@ 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):** `BayesianOpt` (Gaussian-process
-  surrogate + Expected Improvement) or `Tpe` (Parzen-density
+- **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`
+  steps, MC samples):** Hyperband. Implement the `PartialProblem`
   trait on your problem and Hyperband allocates compute aggressively
   across promising configs.
 
@@ -339,12 +365,12 @@ START
  │   │
  │   ├─ Yes → sample-efficient regime
  │   │    ├─ Standard expensive black-box, single-objective
- │   │    │     → BayesianOpt   (GP + Expected Improvement; gold
+ │   │    │     → 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,
+ │   │    │     → TPE           (KDE-based; cheaper per-step,
  │   │    │                      more robust without tuning)
  │   │    │
  │   │    └─ Each eval has a tunable fidelity (epochs, sim steps, …)
@@ -359,60 +385,60 @@ START
      │    │
      │    ├─ Decision is Vec<f64> (continuous)
      │    │   ├─ Smooth landscape (well-conditioned)
-     │    │   │     → CmaEs        (full-cov adaptive Gaussian)
-     │    │   │     → SeparableNes (cheaper diag-cov; high-dim)
-     │    │   │     → NelderMead   (low-dim, deterministic, simple)
+     │    │   │     → CMA-ES       (full-cov adaptive Gaussian)
+     │    │   │     → sNES         (cheaper diag-cov; high-dim)
+     │    │   │     → Nelder-Mead  (low-dim, deterministic, simple)
      │    │   ├─ Multimodal landscape
-     │    │   │     → IpopCmaEs            (CMA-ES with restart;
+     │    │   │     → IPOP-CMA-ES          (CMA-ES with restart;
      │    │   │                              fixes vanilla CMA-ES's
      │    │   │                              multimodal failure)
-     │    │   │     → DifferentialEvolution (rarely beaten on cheap
+     │    │   │     → Differential Evolution (rarely beaten on cheap
      │    │   │                              multimodal continuous)
-     │    │   │     → SimulatedAnnealing   (cheap & generic)
+     │    │   │     → Simulated Annealing  (cheap & generic)
      │    │   ├─ Want parameter-free (no F, CR, w, σ to tune)
-     │    │   │     → Tlbo
+     │    │   │     → TLBO
      │    │   ├─ Want minimum self-adapting baseline
-     │    │   │     → OnePlusOneEs          (one-fifth rule,
+     │    │   │     → (1+1)-ES              (one-fifth rule,
      │    │   │                               smallest possible ES)
      │    │   ├─ Just want a strong default for cheap continuous
-     │    │   │     → DifferentialEvolution
+     │    │   │     → Differential Evolution
      │    │   └─ Just want a baseline
-     │    │         → RandomSearch
+     │    │         → Random Search
      │    │
      │    ├─ Decision is Vec<bool> (binary)
      │    │   ├─ Independent bits, smooth fitness
-     │    │   │     → Umda            (per-bit marginal EDA)
+     │    │   │     → UMDA            (per-bit marginal EDA)
      │    │   └─ Bit interactions matter
-     │    │         → GeneticAlgorithm with BitFlipMutation +
+     │    │         → GA with BitFlipMutation +
      │    │           a bit-string crossover
      │    │
      │    ├─ Decision is Vec<usize> (permutation, e.g., TSP)
-     │    │     → AntColonyTsp (with a distance matrix)
-     │    │     → TabuSearch  (with your own neighbor function)
-     │    │     → SimulatedAnnealing with SwapMutation
+     │    │     → Ant Colony (with a distance matrix)
+     │    │     → Tabu Search (with your own neighbor function)
+     │    │     → Simulated Annealing with SwapMutation
      │    │
      │    └─ Custom decision type (a struct, a tree, …)
-     │          → SimulatedAnnealing or HillClimber
+     │          → Simulated Annealing or Hill Climber
      │            with your own Variation impl
      │
      ├─ 2 or 3 (multi-objective)
      │    │
      │    ├─ Strong default, fast, well-understood
-     │    │     → Nsga2
+     │    │     → NSGA-II
      │    │
      │    ├─ Real-valued, smooth front, want best convergence
-     │    │     → Mopso    (multi-objective PSO; on the benches
+     │    │     → 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 NSGA-II
-     │    │     → Ibea     (indicator-based; consistently the best
+     │    │     → 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)
-     │    │     → SmsEmoa  (hypervolume-contribution selection;
+     │    │     → 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
@@ -420,42 +446,42 @@ START
      │    │                 the right discriminator)
      │    │
      │    ├─ Want decomposition / weight-vector style
-     │    │     → Moead    (very fast per generation, scales well)
+     │    │     → MOEA/D   (very fast per generation, scales well)
      │    │
      │    ├─ Disconnected or non-convex front
-     │    │     → AgeMoea  (estimates front geometry adaptively)
-     │    │     → Knea     (favors knee points)
-     │    │     → Ibea
+     │    │     → AGE-MOEA (estimates front geometry adaptively)
+     │    │     → KnEA     (favors knee points)
+     │    │     → IBEA
      │    │
      │    ├─ Want region-based diversity
-     │    │     → PesaII   (grid hyperboxes drive selection)
-     │    │     → EpsilonMoea (ε-grid archive,
-     │    │                     archive size auto-limits)
+     │    │     → 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)
+     │          → PAES  (1+1 ES with a Pareto archive)
      │
      └─ 4+ (many-objective)
           │
           ├─ Linear / simplex-shaped front (e.g., DTLZ1)
-          │     → Grea       (grid coords drive ranking; on DTLZ1
+          │     → GrEA       (grid coords drive ranking; on DTLZ1
           │                    here it beats NSGA-III by 3× and
           │                    AGE-MOEA by 2.5×)
-          │     → Moead      (decomposition shines on linear fronts;
+          │     → MOEA/D     (decomposition shines on linear fronts;
           │                    second on DTLZ1, also among the
           │                    fastest per generation)
           │
           ├─ Curved / unknown front geometry
-          │     → Nsga3      (reference-point niching, canonical;
+          │     → NSGA-III   (reference-point niching, canonical;
           │                    a strong default when the front
           │                    isn't simplex-shaped)
-          │     → AgeMoea    (estimates L_p geometry per generation)
-          │     → Rvea       (reference vectors with adaptive penalty)
+          │     → 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
+          │     → IBEA       (additive ε-indicator; doesn't degrade
           │                   at high obj count)
-          │     → Hype       (Monte Carlo HV estimation; scales
+          │     → HypE       (Monte Carlo HV estimation; scales
           │                   to arbitrary M)
 ```
 
@@ -465,54 +491,54 @@ START
 
 | Algorithm        | Objectives | Decision   | Strengths |
 |---|---|---|---|
-| `BayesianOpt`    | 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` |
+| **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 |
 |---|---|
-| `RandomSearch`           | sanity baseline |
-| `HillClimber`            | simplest greedy local search |
-| `OnePlusOneEs`           | one-fifth-rule self-adapting baseline |
-| `SimulatedAnnealing`     | escapes local optima |
-| `GeneticAlgorithm`       | classic SO GA with elitism |
-| `ParticleSwarm`          | simple swarm baseline |
-| `DifferentialEvolution`  | strong default for cheap continuous |
-| `Tlbo`                   | parameter-free (no F, CR, w, σ) |
-| `CmaEs`                  | smooth landscapes; full covariance |
-| `IpopCmaEs`              | CMA-ES + restart for multimodal |
-| `SeparableNes`           | diagonal-cov NES; cheap per-step |
-| `NelderMead`             | classical simplex; deterministic |
+| **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 |
-| `TabuSearch`     | any             | discrete, you supply neighbors |
-| `AntColonyTsp`   | `Vec<usize>`    | TSP / permutation |
+| **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 |
 |---|---|---|
-| `Paes`        | 2–3        | 1+1 ES with Pareto archive |
-| `Nsga2`       | 2–3        | canonical Pareto-based EA |
-| `Spea2`       | 2–3        | strength + density |
-| `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 |
-| `SmsEmoa`     | 2+         | exact HV-contribution selection; high per-step cost, modest gain |
-| `Hype`        | 2+         | Monte Carlo HV estimation |
-| `EpsilonMoea` | 2+         | ε-grid archive; auto-sized |
-| `PesaII`      | 2+         | grid-based region selection |
-| `AgeMoea`     | 2+         | adaptive front-geometry estimation |
-| `Knea`        | 2+         | knee-point favored survival |
-| `Moead`       | 2+         | decomposition; fast per-gen |
-| `Nsga3`       | 4+         | reference-point niching; strong on curved fronts |
-| `Rvea`        | 4+         | reference vectors with penalty |
-| `Grea`        | 4+         | grid coords drive selection; particularly strong on linear/simplex fronts |
+| **PAES**      | 2–3        | 1+1 ES with Pareto archive |
+| **NSGA-II**   | 2–3        | canonical Pareto-based EA |
+| **SPEA2**     | 2–3        | strength + density |
+| **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 |
+| **SMS-EMOA**  | 2+         | exact HV-contribution selection; high per-step cost, modest gain |
+| **HypE**      | 2+         | Monte Carlo HV estimation |
+| **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 |
+| **MOEA/D**    | 2+         | decomposition; fast per-gen |
+| **NSGA-III**  | 4+         | reference-point niching; strong on curved fronts |
+| **RVEA**      | 4+         | reference vectors with penalty |
+| **GrEA**      | 4+         | grid coords drive selection; particularly strong on linear/simplex fronts |
 
 ## Current algorithms
 
@@ -520,48 +546,48 @@ The full list with one-line descriptions:
 
 **Sample-efficient / multi-fidelity:**
 
-- `BayesianOpt` — Gaussian-process surrogate + Expected Improvement.
-- `Tpe` — Bergstra et al. 2011 Tree-structured Parzen Estimator.
-- `Hyperband` — Li et al. 2017 multi-fidelity (uses `PartialProblem`).
+- **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:**
 
-- `RandomSearch` — sample-evaluate-keep baseline.
-- `HillClimber` — greedy single-step local search.
-- `OnePlusOneEs` — Rechenberg 1973 (1+1)-ES with one-fifth rule.
-- `SimulatedAnnealing` — Kirkpatrick et al. 1983, generic over decision type.
-- `TabuSearch` — Glover 1986, with a user-supplied neighbor generator.
-- `GeneticAlgorithm` — generational GA with tournament selection + elitism.
-- `ParticleSwarm` — Eberhart & Kennedy 1995 PSO for `Vec<f64>`.
-- `DifferentialEvolution` — Storn & Price DE/rand/1/bin for `Vec<f64>`.
-- `Tlbo` — Rao 2011 Teaching-Learning-Based Optimization (parameter-free).
-- `CmaEs` — Hansen & Ostermeier 2001 covariance-matrix adaptation.
-- `IpopCmaEs` — Auger & Hansen 2005 CMA-ES with restart, for multimodal.
-- `SeparableNes` — Wierstra et al. 2008/2014 diagonal-cov NES.
-- `NelderMead` — Nelder & Mead 1965 simplex direct search.
-- `Umda` — Mühlenbein 1997 univariate marginal-distribution EDA for `Vec<bool>`.
-- `AntColonyTsp` — Dorigo Ant System for permutation problems.
+- **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.
-- `Nsga2` — Deb et al. 2002, the canonical Pareto-based EA.
-- `Spea2` — Zitzler, Laumanns & Thiele 2001 strength-Pareto EA.
-- `Moead` — Zhang & Li 2007 decomposition-based MOEA with Tchebycheff scalarization.
-- `Mopso` — Coello, Pulido & Lechuga 2004 multi-objective PSO.
-- `Ibea` — Zitzler & Künzli 2004 indicator-based EA.
-- `SmsEmoa` — Beume, Naujoks & Emmerich 2007 hypervolume-selection EMOA.
-- `Hype` — Bader & Zitzler 2011 Hypervolume Estimation Algorithm.
-- `EpsilonMoea` — Deb, Mohan & Mishra 2003 ε-dominance MOEA.
-- `PesaII` — Corne et al. 2001 Pareto Envelope Selection II.
-- `AgeMoea` — Panichella 2019 Adaptive Geometry Estimation MOEA.
-- `Knea` — Zhang, Tian & Jin 2015 Knee point-driven EA.
+- **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+):**
 
-- `Nsga3` — Deb & Jain 2014 reference-point NSGA-III.
-- `Rvea` — Cheng et al. 2016 Reference Vector-guided EA.
-- `Grea` — Yang et al. 2013 Grid-based EA.
+- **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`,
@@ -576,7 +602,7 @@ and the metrics `spacing` and `hypervolume_2d`.
   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. `RandomSearch` is the recommended file to read before
+  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.

SECURITY.md

@@ -8,8 +8,8 @@ needed.
 
 | Version | Supported          |
 |---------|--------------------|
-| 0.8.x   | ✅                  |
-| ≤ 0.7.x | ❌ (please upgrade) |
+| 0.10.x  | ✅                  |
+| ≤ 0.9.x | ❌ (please upgrade) |
 
 heuropt is pre-1.0; the public API may change between minor versions.
 Once 1.0.0 ships, the support window will be at least the latest two

docs/book/src/SUMMARY.md

@@ -18,6 +18,7 @@
   - [Optimize a permutation (TSP-style)](./cookbook/permutation.md)
   - [Constrain your search with `Repair`](./cookbook/constraints.md)
   - [Pick one answer off a Pareto front](./cookbook/pick-one.md)
+  - [Explore your results in a webapp](./cookbook/explorer.md)
   - [Write your own algorithm](./cookbook/custom-optimizer.md)
 
 # Reference

docs/book/src/choosing-an-algorithm.md

@@ -17,9 +17,9 @@ after it.
 
 For the cheap-eval branch, you have the run of the catalog. For the
 expensive branch, classical evolutionary methods waste your evaluation
-budget — go to [`BayesianOpt`] or [`Tpe`]. For the *very* expensive
+budget — go to [Bayesian Optimization][BayesianOpt] or [TPE]. For the *very* expensive
 branch where each eval has a tunable budget (epochs, MC samples, sim
-steps), [`Hyperband`] over the [`PartialProblem`] trait is the move.
+steps), [Hyperband] over the [`PartialProblem`] trait is the move.
 
 ## Step 1: How many objectives?
 
@@ -51,48 +51,48 @@ These all take `Vec<f64>` decisions.
 
 ### Smooth, low-to-moderate dimension
 
-[`CmaEs`] is the strong default. It adapts the search distribution's
+[CMA-ES][CmaEs] is the strong default. It adapts the search distribution's
 covariance to the local landscape. On the comparison harness it
 hits machine epsilon on Rosenbrock at 30 000 evaluations.
 
-For very low-dimensional smooth problems (≤ 5 dim), [`NelderMead`] is
+For very low-dimensional smooth problems (≤ 5 dim), [Nelder-Mead][NelderMead] is
 deterministic and converges to f = 0 exactly on Rosenbrock.
 
 ### High dimension, smooth
 
-[`SeparableNes`] uses a diagonal covariance — cheaper per step than
-CmaEs at the cost of being unable to model rotated landscapes. Worth
-trying when CmaEs's `O(d²)` per-step cost hurts.
+[sNES][SeparableNes] uses a diagonal covariance — cheaper per step than
+CMA-ES at the cost of being unable to model rotated landscapes. Worth
+trying when CMA-ES's `O(d²)` per-step cost hurts.
 
 ### Multimodal landscapes
 
 Multimodal = many local minima that aren't the global one. Rastrigin
 and Ackley are classic traps.
 
-[`IpopCmaEs`] is CmaEs with an increasing-population restart strategy
-specifically designed for this. On the harness it drops vanilla CmaEs's
+[IPOP-CMA-ES][IpopCmaEs] is CMA-ES with an increasing-population restart strategy
+specifically designed for this. On the harness it drops vanilla CMA-ES's
 Rastrigin score from f = 2.35 to f = 0.13.
 
-[`DifferentialEvolution`] is rarely beaten on cheap multimodal
+[Differential Evolution][DifferentialEvolution] is rarely beaten on cheap multimodal
 continuous problems. On Rastrigin it ties with `(1+1)-ES` at f = 0.
 
-[`SimulatedAnnealing`] is a cheap, generic baseline that escapes local
+[Simulated Annealing][SimulatedAnnealing] is a cheap, generic baseline that escapes local
 optima via temperature decay.
 
 ### Want parameter-free
 
-[`Tlbo`] (Teaching-Learning-Based Optimization) has no `F`, `CR`, `w`,
+[TLBO][Tlbo] (Teaching-Learning-Based Optimization) has no `F`, `CR`, `w`,
 or `σ` to tune. Often a respectable middle-of-the-pack performer.
 
 ### Smallest possible self-adapting baseline
 
-[`OnePlusOneEs`] — Rechenberg's 1973 `(1+1)`-ES with the one-fifth
+[(1+1)-ES][OnePlusOneEs] — Rechenberg's 1973 `(1+1)`-ES with the one-fifth
 success rule. On the harness it hits f = 0 on Rastrigin in 50 000
 evaluations.
 
 ### Just want a baseline
 
-[`RandomSearch`]. Useful as a sanity check: if your fancy optimizer
+[Random Search][RandomSearch]. Useful as a sanity check: if your fancy optimizer
 can't beat random search, something is wrong (with the fancy
 optimizer or with the problem).
 
@@ -100,69 +100,69 @@ optimizer or with the problem).
 
 | Decision type | Algorithm | Notes |
 |---|---|---|
-| `Vec<bool>` | [`Umda`] | Per-bit marginal EDA. Independent-bit assumption. |
-| `Vec<bool>` | [`GeneticAlgorithm`] + [`BitFlipMutation`] | When bit interactions matter. |
-| `Vec<usize>` (permutation) | [`AntColonyTsp`] | TSP-style with a distance matrix. |
-| `Vec<usize>` (permutation) | [`SimulatedAnnealing`] + [`SwapMutation`] | Generic discrete baseline. |
-| `Vec<usize>` or custom | [`TabuSearch`] | You supply the neighbor function. |
-| Custom struct | [`SimulatedAnnealing`] / [`HillClimber`] | With your own `Variation` impl. |
+| `Vec<bool>` | [UMDA][Umda] | Per-bit marginal EDA. Independent-bit assumption. |
+| `Vec<bool>` | [GA][GeneticAlgorithm] + [`BitFlipMutation`] | When bit interactions matter. |
+| `Vec<usize>` (permutation) | [Ant Colony][AntColonyTsp] | TSP-style with a distance matrix. |
+| `Vec<usize>` (permutation) | [Simulated Annealing][SimulatedAnnealing] + [`SwapMutation`] | Generic discrete baseline. |
+| `Vec<usize>` or custom | [Tabu Search][TabuSearch] | You supply the neighbor function. |
+| Custom struct | [Simulated Annealing][SimulatedAnnealing] / [Hill Climber][HillClimber] | With your own `Variation` impl. |
 
 ## Step 2 — multi-objective (2 or 3)
 
 ### Strong default
 
-[`Nsga2`] is the canonical Pareto-based EA. Fast, well-understood,
+[NSGA-II][Nsga2] is the canonical Pareto-based EA. Fast, well-understood,
 maintains diversity via crowding distance. On the harness it lands
 on the Pareto front of every test problem.
 
 ### Real-valued, smooth front, want best convergence
 
-[`Mopso`] (multi-objective PSO with archive). On ZDT1 it wins
+[MOPSO][Mopso] (multi-objective PSO with archive). On ZDT1 it wins
 hypervolume outright and converges 100× tighter than the
 dominance-based methods.
 
 ### Better front quality than NSGA-II
 
-[`Ibea`] (indicator-based) is consistently the best of the
+[IBEA][Ibea] (indicator-based) is consistently the best of the
 dominance-based methods on the harness — wins ZDT3 hypervolume and
 DTLZ2 mean distance by 24×. It uses an additive ε-indicator for
 selection rather than dominance + crowding.
 
-[`Spea2`] (strength + density) — solid alternative; explicit external
+[SPEA2][Spea2] (strength + density) — solid alternative; explicit external
 archive separate from the population.
 
-[`SmsEmoa`] uses exact hypervolume contribution for selection. Elegant
+[SMS-EMOA][SmsEmoa] uses exact hypervolume contribution for selection. Elegant
 in theory; in practice on the harness budgets here it underperforms
 NSGA-II. Worth the higher per-step cost only when exact HV
 contribution is the right discriminator.
 
 ### Decomposition / weight-vector style
 
-[`Moead`] decomposes the multi-objective problem into many scalar
+[MOEA/D][Moead] decomposes the multi-objective problem into many scalar
 sub-problems (Tchebycheff or weighted sum) and solves them in
 parallel. Very fast per generation; scales naturally to many
 objectives.
 
 ### Disconnected or non-convex front
 
-[`AgeMoea`] estimates the front geometry adaptively (the L_p
+[AGE-MOEA][AgeMoea] estimates the front geometry adaptively (the L_p
 parameter `p` is fit from data each generation).
 
-[`Knea`] favors knee points — the regions of the front where small
+[KnEA][Knea] favors knee points — the regions of the front where small
 gains in one objective cost large losses in another.
 
-[`Ibea`] also handles disconnected fronts well.
+[IBEA][Ibea] also handles disconnected fronts well.
 
 ### Region-based diversity
 
-[`PesaII`] uses grid hyperboxes to drive selection — divide the
+[PESA-II][PesaII] uses grid hyperboxes to drive selection — divide the
 objective space into a grid, pick from the least-crowded boxes.
 
-[`EpsilonMoea`] uses an ε-grid archive that auto-limits its size.
+[ε-MOEA][EpsilonMoea] uses an ε-grid archive that auto-limits its size.
 
 ### Just one starting decision (no population budget)
 
-[`Paes`] — `(1+1)`-ES with a Pareto archive. Cheap, simple, useful
+[PAES][Paes] — `(1+1)`-ES with a Pareto archive. Cheap, simple, useful
 when your evaluations are expensive enough that you can't afford a
 population.
 
@@ -170,26 +170,26 @@ population.
 
 ### Linear / simplex-shaped front (e.g., DTLZ1)
 
-[`Grea`] — grid coords drive ranking. On DTLZ1 it beats NSGA-III by
+[GrEA][Grea] — grid coords drive ranking. On DTLZ1 it beats NSGA-III by
 3× and AGE-MOEA by 2.5×.
 
-[`Moead`] — decomposition shines on linear fronts; second on DTLZ1
+[MOEA/D][Moead] — decomposition shines on linear fronts; second on DTLZ1
 and among the fastest per generation.
 
 ### Curved / unknown front geometry
 
-[`Nsga3`] — reference-point niching; canonical many-objective method;
+[NSGA-III][Nsga3] — reference-point niching; canonical many-objective method;
 strong default when the front isn't simplex-shaped.
 
-[`AgeMoea`] — estimates L_p geometry per generation.
+[AGE-MOEA][AgeMoea] — estimates L_p geometry per generation.
 
-[`Rvea`] — reference vectors with adaptive penalty.
+[RVEA][Rvea] — reference vectors with adaptive penalty.
 
 ### Indicator-based selection
 
-[`Ibea`] — additive ε-indicator; doesn't degrade at high obj count.
+[IBEA][Ibea] — additive ε-indicator; doesn't degrade at high obj count.
 
-[`HypE`] — Monte Carlo hypervolume estimation; scales to arbitrary
+[HypE][Hype] — Monte Carlo hypervolume estimation; scales to arbitrary
 objective count where exact HV is too expensive.
 
 ## Step 3: Are there hard constraints?
@@ -216,13 +216,13 @@ for worked examples.
 ## Step 4: Should you parallelize?
 
 Enable the `parallel` feature flag if your `evaluate` takes more
-than ~50 µs. Population-based algorithms ([`RandomSearch`], [`Nsga2`],
-[`DifferentialEvolution`], [`Spea2`], [`Ibea`], [`Mopso`], …) batch-
+than ~50 µs. Population-based algorithms ([Random Search][RandomSearch], [NSGA-II][Nsga2],
+[Differential Evolution][DifferentialEvolution], [SPEA2][Spea2], [IBEA][Ibea], [MOPSO][Mopso], …) batch-
 evaluate via rayon when the feature is on. **Seeded runs stay
 bit-identical** to serial mode.
 
 ```toml
-heuropt = { version = "0.8", features = ["parallel"] }
+heuropt = { version = "0.10", features = ["parallel"] }
 ```
 
 If your evaluation is **IO-bound** (HTTP request, RPC, subprocess)
@@ -235,55 +235,55 @@ method on every algorithm in the catalog. See the
 
 | Situation | Pick |
 |---|---|
-| Smooth single-objective continuous | [`CmaEs`] |
-| Multimodal single-objective continuous | [`IpopCmaEs`] or [`DifferentialEvolution`] |
-| Expensive single-objective | [`BayesianOpt`] or [`Tpe`] |
-| Multi-fidelity single-objective | [`Hyperband`] |
-| 2- or 3-objective default | [`Nsga2`] |
-| 2-objective real-valued smooth front | [`Mopso`] |
-| Disconnected / non-convex front | [`Ibea`] |
-| Many-objective default (curved front) | [`Nsga3`] |
-| Many-objective linear / simplex front | [`Grea`] |
-| Permutation problem | [`AntColonyTsp`] |
-| Binary problem | [`Umda`] |
-| Custom decision type | [`SimulatedAnnealing`] + your `Variation` |
-| Sanity baseline | [`RandomSearch`] |
+| Smooth single-objective continuous | [CMA-ES][CmaEs] |
+| Multimodal single-objective continuous | [IPOP-CMA-ES][IpopCmaEs] or [Differential Evolution][DifferentialEvolution] |
+| Expensive single-objective | [Bayesian Optimization][BayesianOpt] or [TPE] |
+| Multi-fidelity single-objective | [Hyperband] |
+| 2- or 3-objective default | [NSGA-II][Nsga2] |
+| 2-objective real-valued smooth front | [MOPSO][Mopso] |
+| Disconnected / non-convex front | [IBEA][Ibea] |
+| Many-objective default (curved front) | [NSGA-III][Nsga3] |
+| Many-objective linear / simplex front | [GrEA][Grea] |
+| Permutation problem | [Ant Colony][AntColonyTsp] |
+| Binary problem | [UMDA][Umda] |
+| Custom decision type | [Simulated Annealing][SimulatedAnnealing] + your `Variation` |
+| Sanity baseline | [Random Search][RandomSearch] |
 
-[`CmaEs`]: https://docs.rs/heuropt/latest/heuropt/algorithms/cma_es/struct.CmaEs.html
-[`IpopCmaEs`]: https://docs.rs/heuropt/latest/heuropt/algorithms/ipop_cma_es/struct.IpopCmaEs.html
-[`SeparableNes`]: https://docs.rs/heuropt/latest/heuropt/algorithms/snes/struct.SeparableNes.html
-[`NelderMead`]: https://docs.rs/heuropt/latest/heuropt/algorithms/nelder_mead/struct.NelderMead.html
-[`DifferentialEvolution`]: https://docs.rs/heuropt/latest/heuropt/algorithms/differential_evolution/struct.DifferentialEvolution.html
-[`SimulatedAnnealing`]: https://docs.rs/heuropt/latest/heuropt/algorithms/simulated_annealing/struct.SimulatedAnnealing.html
-[`Tlbo`]: https://docs.rs/heuropt/latest/heuropt/algorithms/tlbo/struct.Tlbo.html
-[`OnePlusOneEs`]: https://docs.rs/heuropt/latest/heuropt/algorithms/one_plus_one_es/struct.OnePlusOneEs.html
-[`RandomSearch`]: https://docs.rs/heuropt/latest/heuropt/algorithms/random_search/struct.RandomSearch.html
-[`HillClimber`]: https://docs.rs/heuropt/latest/heuropt/algorithms/hill_climber/struct.HillClimber.html
-[`BayesianOpt`]: https://docs.rs/heuropt/latest/heuropt/algorithms/bayesian_opt/struct.BayesianOpt.html
-[`Tpe`]: https://docs.rs/heuropt/latest/heuropt/algorithms/tpe/struct.Tpe.html
-[`Hyperband`]: https://docs.rs/heuropt/latest/heuropt/algorithms/hyperband/struct.Hyperband.html
+[CmaEs]: https://docs.rs/heuropt/latest/heuropt/algorithms/cma_es/struct.CmaEs.html
+[IpopCmaEs]: https://docs.rs/heuropt/latest/heuropt/algorithms/ipop_cma_es/struct.IpopCmaEs.html
+[SeparableNes]: https://docs.rs/heuropt/latest/heuropt/algorithms/snes/struct.SeparableNes.html
+[NelderMead]: https://docs.rs/heuropt/latest/heuropt/algorithms/nelder_mead/struct.NelderMead.html
+[DifferentialEvolution]: https://docs.rs/heuropt/latest/heuropt/algorithms/differential_evolution/struct.DifferentialEvolution.html
+[SimulatedAnnealing]: https://docs.rs/heuropt/latest/heuropt/algorithms/simulated_annealing/struct.SimulatedAnnealing.html
+[Tlbo]: https://docs.rs/heuropt/latest/heuropt/algorithms/tlbo/struct.Tlbo.html
+[OnePlusOneEs]: https://docs.rs/heuropt/latest/heuropt/algorithms/one_plus_one_es/struct.OnePlusOneEs.html
+[RandomSearch]: https://docs.rs/heuropt/latest/heuropt/algorithms/random_search/struct.RandomSearch.html
+[HillClimber]: https://docs.rs/heuropt/latest/heuropt/algorithms/hill_climber/struct.HillClimber.html
+[BayesianOpt]: https://docs.rs/heuropt/latest/heuropt/algorithms/bayesian_opt/struct.BayesianOpt.html
+[TPE]: https://docs.rs/heuropt/latest/heuropt/algorithms/tpe/struct.Tpe.html
+[Hyperband]: https://docs.rs/heuropt/latest/heuropt/algorithms/hyperband/struct.Hyperband.html
 [`PartialProblem`]: https://docs.rs/heuropt/latest/heuropt/core/partial_problem/trait.PartialProblem.html
-[`Umda`]: https://docs.rs/heuropt/latest/heuropt/algorithms/umda/struct.Umda.html
-[`GeneticAlgorithm`]: https://docs.rs/heuropt/latest/heuropt/algorithms/genetic_algorithm/struct.GeneticAlgorithm.html
+[Umda]: https://docs.rs/heuropt/latest/heuropt/algorithms/umda/struct.Umda.html
+[GeneticAlgorithm]: https://docs.rs/heuropt/latest/heuropt/algorithms/genetic_algorithm/struct.GeneticAlgorithm.html
 [`BitFlipMutation`]: https://docs.rs/heuropt/latest/heuropt/operators/struct.BitFlipMutation.html
-[`AntColonyTsp`]: https://docs.rs/heuropt/latest/heuropt/algorithms/ant_colony_tsp/struct.AntColonyTsp.html
+[AntColonyTsp]: https://docs.rs/heuropt/latest/heuropt/algorithms/ant_colony_tsp/struct.AntColonyTsp.html
 [`SwapMutation`]: https://docs.rs/heuropt/latest/heuropt/operators/struct.SwapMutation.html
-[`TabuSearch`]: https://docs.rs/heuropt/latest/heuropt/algorithms/tabu_search/struct.TabuSearch.html
-[`Nsga2`]: https://docs.rs/heuropt/latest/heuropt/algorithms/nsga2/struct.Nsga2.html
-[`Nsga3`]: https://docs.rs/heuropt/latest/heuropt/algorithms/nsga3/struct.Nsga3.html
-[`Mopso`]: https://docs.rs/heuropt/latest/heuropt/algorithms/mopso/struct.Mopso.html
-[`Ibea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/ibea/struct.Ibea.html
-[`Spea2`]: https://docs.rs/heuropt/latest/heuropt/algorithms/spea2/struct.Spea2.html
-[`SmsEmoa`]: https://docs.rs/heuropt/latest/heuropt/algorithms/sms_emoa/struct.SmsEmoa.html
-[`Moead`]: https://docs.rs/heuropt/latest/heuropt/algorithms/moead/struct.Moead.html
-[`AgeMoea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/age_moea/struct.AgeMoea.html
-[`Knea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/knea/struct.Knea.html
-[`PesaII`]: https://docs.rs/heuropt/latest/heuropt/algorithms/pesa2/struct.PesaII.html
-[`EpsilonMoea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/epsilon_moea/struct.EpsilonMoea.html
-[`Paes`]: https://docs.rs/heuropt/latest/heuropt/algorithms/paes/struct.Paes.html
-[`Grea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/grea/struct.Grea.html
-[`Rvea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/rvea/struct.Rvea.html
-[`HypE`]: https://docs.rs/heuropt/latest/heuropt/algorithms/hype/struct.Hype.html
+[TabuSearch]: https://docs.rs/heuropt/latest/heuropt/algorithms/tabu_search/struct.TabuSearch.html
+[Nsga2]: https://docs.rs/heuropt/latest/heuropt/algorithms/nsga2/struct.Nsga2.html
+[Nsga3]: https://docs.rs/heuropt/latest/heuropt/algorithms/nsga3/struct.Nsga3.html
+[Mopso]: https://docs.rs/heuropt/latest/heuropt/algorithms/mopso/struct.Mopso.html
+[Ibea]: https://docs.rs/heuropt/latest/heuropt/algorithms/ibea/struct.Ibea.html
+[Spea2]: https://docs.rs/heuropt/latest/heuropt/algorithms/spea2/struct.Spea2.html
+[SmsEmoa]: https://docs.rs/heuropt/latest/heuropt/algorithms/sms_emoa/struct.SmsEmoa.html
+[Moead]: https://docs.rs/heuropt/latest/heuropt/algorithms/moead/struct.Moead.html
+[AgeMoea]: https://docs.rs/heuropt/latest/heuropt/algorithms/age_moea/struct.AgeMoea.html
+[Knea]: https://docs.rs/heuropt/latest/heuropt/algorithms/knea/struct.Knea.html
+[PesaII]: https://docs.rs/heuropt/latest/heuropt/algorithms/pesa2/struct.PesaII.html
+[EpsilonMoea]: https://docs.rs/heuropt/latest/heuropt/algorithms/epsilon_moea/struct.EpsilonMoea.html
+[Paes]: https://docs.rs/heuropt/latest/heuropt/algorithms/paes/struct.Paes.html
+[Grea]: https://docs.rs/heuropt/latest/heuropt/algorithms/grea/struct.Grea.html
+[Rvea]: https://docs.rs/heuropt/latest/heuropt/algorithms/rvea/struct.Rvea.html
+[Hype]: https://docs.rs/heuropt/latest/heuropt/algorithms/hype/struct.Hype.html
 [`Repair<D>`]: https://docs.rs/heuropt/latest/heuropt/traits/trait.Repair.html
 [`ClampToBounds`]: https://docs.rs/heuropt/latest/heuropt/operators/struct.ClampToBounds.html
 [`ProjectToSimplex`]: https://docs.rs/heuropt/latest/heuropt/operators/struct.ProjectToSimplex.html

docs/book/src/comparison.md

@@ -15,7 +15,7 @@ The columns:
 
 | Library | Lang | Algorithms | Multi-obj | Surrogates | Determinism | Async |
 |---|---|---|---|---|---|---|
-| **heuropt 0.8** | Rust | 33 | ✅ NSGA-II/III, SPEA2, IBEA, MOEA/D, MOPSO, SMS-EMOA, HypE, AGE-MOEA, GrEA, KnEA, RVEA, PESA-II, ε-MOEA, PAES | ✅ BO, TPE, Hyperband | ✅ bit-identical seeded | ✅ `AsyncProblem` + `run_async` on every algorithm |
+| **heuropt 0.10** | Rust | 33 | ✅ NSGA-II/III, SPEA2, IBEA, MOEA/D, MOPSO, SMS-EMOA, HypE, AGE-MOEA, GrEA, KnEA, RVEA, PESA-II, ε-MOEA, PAES | ✅ BO, TPE, Hyperband | ✅ bit-identical seeded | ✅ `AsyncProblem` + `run_async` on every algorithm |
 | pymoo | Python | ~25 | ✅ extensive | partial (BO via plug-ins) | ✅ | ❌ |
 | DEAP | Python | flexible toolbox | ✅ | ❌ | ✅ | ❌ |
 | hyperopt | Python | TPE-focused | ❌ | ✅ TPE | partial | partial |
@@ -36,7 +36,7 @@ The columns:
   otherwise.
 - You want a **small, readable codebase** — every algorithm is
   written for clarity, no trait-object plumbing, no GATs in user-
-  facing APIs. Reading `RandomSearch` should be enough to write a
+  facing APIs. Reading Random Search should be enough to write a
   new optimizer.
 - You have **IO-bound evaluations** — calling an HTTP service, an
   RPC, or a subprocess — and want first-class `async fn evaluate`
@@ -64,12 +64,12 @@ heuropt covers the same major Pareto MOEAs as pymoo and MOEA Framework:
 NSGA-II/III, SPEA2, IBEA, MOEA/D, MOPSO, SMS-EMOA, HypE, AGE-MOEA,
 GrEA, KnEA, RVEA, PESA-II, ε-MOEA, PAES.
 
-The expensive-evaluation regime: BayesianOpt + TPE + Hyperband. This
+The expensive-evaluation regime: Bayesian Optimization + TPE + Hyperband. This
 is comparable to optuna's coverage but in pure Rust.
 
 The single-objective continuous catalog (CMA-ES, IPOP-CMA-ES, sNES,
-DE, PSO, GA, TLBO, (1+1)-ES, NelderMead, RandomSearch, HillClimber,
-SimulatedAnnealing) covers the canonical baselines and several modern
+DE, PSO, GA, TLBO, (1+1)-ES, Nelder-Mead, Random Search, Hill Climber,
+Simulated Annealing) covers the canonical baselines and several modern
 variants.
 
 What heuropt does **not** ship that some libraries do:

docs/book/src/cookbook.md

@@ -14,17 +14,21 @@ project.
   optimizer await many evaluations concurrently. The differentiating
   feature vs other optimization libraries.
 - [Tune a model with expensive evaluations](./cookbook/expensive-evaluations.md)
-  — `BayesianOpt`, `Tpe`, and `Hyperband` for the 50–500-eval
+  — Bayesian Optimization, TPE, and Hyperband for the 50–500-eval
   regime.
 - [Compare two algorithms on your problem](./cookbook/compare.md) —
   multi-seed harness pattern straight from `examples/compare.rs`.
 - [Optimize a permutation (TSP-style)](./cookbook/permutation.md) —
-  `AntColonyTsp` with a distance matrix.
+  Ant Colony with a distance matrix.
 - [Constrain your search with `Repair`](./cookbook/constraints.md) —
   bounds, simplex projection, custom repair.
 - [Pick one answer off a Pareto front](./cookbook/pick-one.md) — the
   a-posteriori weighted-decision pattern from the `jiggly_tuning`
   example.
+- [Explore your results in a webapp](./cookbook/explorer.md) — export
+  an `OptimizationResult` to JSON and browse it interactively at
+  [heuropt-explorer](https://swaits.github.io/heuropt-explorer/) —
+  parallel coordinates, scatter, range filters, weighted ranking.
 - [Write your own algorithm](./cookbook/custom-optimizer.md) —
   implement `Optimizer<P>` from scratch, à la the
   `examples/custom_optimizer.rs` walkthrough.

docs/book/src/cookbook/async.md

@@ -13,7 +13,7 @@ evaluation path.
 
 ```toml
 [dependencies]
-heuropt = { version = "0.8", features = ["async"] }
+heuropt = { version = "0.10", features = ["async"] }
 
 # Pick whatever async runtime you want; heuropt itself depends only on
 # `futures`. The example below uses tokio.
@@ -112,8 +112,8 @@ results back to the algorithm.
 
 ## What the worked example shows
 
-`examples/async_eval.rs` runs `RandomSearch` (200 evaluations × 20 ms
-each) at `concurrency = 1, 4, 16` and `DifferentialEvolution` at
+`examples/async_eval.rs` runs Random Search (200 evaluations × 20 ms
+each) at `concurrency = 1, 4, 16` and Differential Evolution at
 `concurrency = 8`. On a recent machine:
 
 ```text

docs/book/src/cookbook/expensive-evaluations.md

@@ -7,9 +7,9 @@ algorithms aimed at this regime.
 
 | Algorithm | Surrogate | Best for |
 |---|---|---|
-| [`BayesianOpt`] | Gaussian process + Expected Improvement | The textbook choice; needs kernel tuning to shine |
-| [`Tpe`] | Kernel-density estimate of good vs bad points | Cheaper per step; more robust without tuning |
-| [`Hyperband`] | (none — it's a multi-fidelity scheduler) | When each eval has a tunable budget (epochs, MC samples) |
+| [Bayesian Optimization][BayesianOpt] | Gaussian process + Expected Improvement | The textbook choice; needs kernel tuning to shine |
+| [TPE] | Kernel-density estimate of good vs bad points | Cheaper per step; more robust without tuning |
+| [Hyperband] | (none — it's a multi-fidelity scheduler) | When each eval has a tunable budget (epochs, MC samples) |
 
 ## When each is right
 
@@ -101,7 +101,7 @@ canonical Bergstra value.
 
 ## Hyperband
 
-[`Hyperband`] needs your problem to implement [`PartialProblem`] —
+[Hyperband] needs your problem to implement [`PartialProblem`] —
 that is, you can evaluate at a tunable fidelity (e.g. number of
 training epochs). The algorithm schedules many cheap-fidelity runs
 and promotes only the survivors to higher fidelity.
@@ -156,9 +156,9 @@ The state of the art (BOHB) combines BO with Hyperband: TPE picks the
 configurations Hyperband then evaluates at increasing fidelity.
 heuropt doesn't ship a unified BOHB but the building blocks are
 there — wrap your `PartialProblem` with a TPE-driven sampler and
-feed the picks into `Hyperband`. PRs welcome.
+feed the picks into Hyperband. PRs welcome.
 
-[`BayesianOpt`]: https://docs.rs/heuropt/latest/heuropt/algorithms/bayesian_opt/struct.BayesianOpt.html
-[`Tpe`]: https://docs.rs/heuropt/latest/heuropt/algorithms/tpe/struct.Tpe.html
-[`Hyperband`]: https://docs.rs/heuropt/latest/heuropt/algorithms/hyperband/struct.Hyperband.html
+[BayesianOpt]: https://docs.rs/heuropt/latest/heuropt/algorithms/bayesian_opt/struct.BayesianOpt.html
+[TPE]: https://docs.rs/heuropt/latest/heuropt/algorithms/tpe/struct.Tpe.html
+[Hyperband]: https://docs.rs/heuropt/latest/heuropt/algorithms/hyperband/struct.Hyperband.html
 [`PartialProblem`]: https://docs.rs/heuropt/latest/heuropt/core/partial_problem/trait.PartialProblem.html

docs/book/src/cookbook/explorer.md

@@ -14,7 +14,7 @@ install needed beyond a browser.
 
 ```toml
 [dependencies]
-heuropt = { version = "0.9", features = ["serde"] }
+heuropt = { version = "0.10", features = ["serde"] }
 ```
 
 The export uses `serde_json` under the hood, so the explorer module

docs/book/src/cookbook/parallel.md

@@ -9,7 +9,7 @@ population, and rayon parallelizes that batch.
 
 ```toml
 [dependencies]
-heuropt = { version = "0.8", features = ["parallel"] }
+heuropt = { version = "0.10", features = ["parallel"] }
 ```
 
 There's nothing else to opt into in your code. The
@@ -29,14 +29,14 @@ pass.
 
 Algorithms with a per-generation `evaluate_batch`:
 
-- [`RandomSearch`], [`Nsga2`], [`Nsga3`], [`Spea2`], [`Moead`],
-  [`Mopso`], [`Ibea`], [`SmsEmoa`], [`HypE`], [`PesaII`],
-  [`EpsilonMoea`], [`AgeMoea`], [`Knea`], [`Grea`], [`Rvea`].
-- [`DifferentialEvolution`] and [`GeneticAlgorithm`] benefit on the
+- [Random Search][RandomSearch], [NSGA-II][Nsga2], [NSGA-III][Nsga3], [SPEA2][Spea2], [MOEA/D][Moead],
+  [MOPSO][Mopso], [IBEA][Ibea], [SMS-EMOA][SmsEmoa], [HypE][Hype], [PESA-II][PesaII],
+  [ε-MOEA][EpsilonMoea], [AGE-MOEA][AgeMoea], [KnEA][Knea], [GrEA][Grea], [RVEA][Rvea].
+- [Differential Evolution][DifferentialEvolution] and [GA][GeneticAlgorithm] benefit on the
   initial population and offspring batches.
 
-Steady-state algorithms ([`Paes`], [`SimulatedAnnealing`],
-[`HillClimber`], [`OnePlusOneEs`]) only evaluate one or a few
+Steady-state algorithms ([PAES][Paes], [Simulated Annealing][SimulatedAnnealing],
+[Hill Climber][HillClimber], [(1+1)-ES][OnePlusOneEs]) only evaluate one or a few
 candidates per iteration, so the parallel feature gives them
 nothing — leave it off if those are your primary optimizers.
 
@@ -102,7 +102,7 @@ to scope it.
 - You're already running multiple seeds in parallel at the harness
   level (see [Compare two algorithms](./compare.md)). Stacking
   parallelism rarely helps.
-- The algorithm is steady-state (Paes, SA, hill climber).
+- The algorithm is steady-state (PAES, SA, hill climber).
 
 ## `parallel` vs `async`
 
@@ -114,24 +114,24 @@ to scope it.
 Both can be on at once if your evaluation does *both* substantial
 CPU work *and* IO. The two features are independent.
 
-[`RandomSearch`]: https://docs.rs/heuropt/latest/heuropt/algorithms/random_search/struct.RandomSearch.html
-[`Nsga2`]: https://docs.rs/heuropt/latest/heuropt/algorithms/nsga2/struct.Nsga2.html
-[`Nsga3`]: https://docs.rs/heuropt/latest/heuropt/algorithms/nsga3/struct.Nsga3.html
-[`Spea2`]: https://docs.rs/heuropt/latest/heuropt/algorithms/spea2/struct.Spea2.html
-[`Moead`]: https://docs.rs/heuropt/latest/heuropt/algorithms/moead/struct.Moead.html
-[`Mopso`]: https://docs.rs/heuropt/latest/heuropt/algorithms/mopso/struct.Mopso.html
-[`Ibea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/ibea/struct.Ibea.html
-[`SmsEmoa`]: https://docs.rs/heuropt/latest/heuropt/algorithms/sms_emoa/struct.SmsEmoa.html
-[`HypE`]: https://docs.rs/heuropt/latest/heuropt/algorithms/hype/struct.Hype.html
-[`PesaII`]: https://docs.rs/heuropt/latest/heuropt/algorithms/pesa2/struct.PesaII.html
-[`EpsilonMoea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/epsilon_moea/struct.EpsilonMoea.html
-[`AgeMoea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/age_moea/struct.AgeMoea.html
-[`Knea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/knea/struct.Knea.html
-[`Grea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/grea/struct.Grea.html
-[`Rvea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/rvea/struct.Rvea.html
-[`DifferentialEvolution`]: https://docs.rs/heuropt/latest/heuropt/algorithms/differential_evolution/struct.DifferentialEvolution.html
-[`GeneticAlgorithm`]: https://docs.rs/heuropt/latest/heuropt/algorithms/genetic_algorithm/struct.GeneticAlgorithm.html
-[`Paes`]: https://docs.rs/heuropt/latest/heuropt/algorithms/paes/struct.Paes.html
-[`SimulatedAnnealing`]: https://docs.rs/heuropt/latest/heuropt/algorithms/simulated_annealing/struct.SimulatedAnnealing.html
-[`HillClimber`]: https://docs.rs/heuropt/latest/heuropt/algorithms/hill_climber/struct.HillClimber.html
-[`OnePlusOneEs`]: https://docs.rs/heuropt/latest/heuropt/algorithms/one_plus_one_es/struct.OnePlusOneEs.html
+[RandomSearch]: https://docs.rs/heuropt/latest/heuropt/algorithms/random_search/struct.RandomSearch.html
+[Nsga2]: https://docs.rs/heuropt/latest/heuropt/algorithms/nsga2/struct.Nsga2.html
+[Nsga3]: https://docs.rs/heuropt/latest/heuropt/algorithms/nsga3/struct.Nsga3.html
+[Spea2]: https://docs.rs/heuropt/latest/heuropt/algorithms/spea2/struct.Spea2.html
+[Moead]: https://docs.rs/heuropt/latest/heuropt/algorithms/moead/struct.Moead.html
+[Mopso]: https://docs.rs/heuropt/latest/heuropt/algorithms/mopso/struct.Mopso.html
+[Ibea]: https://docs.rs/heuropt/latest/heuropt/algorithms/ibea/struct.Ibea.html
+[SmsEmoa]: https://docs.rs/heuropt/latest/heuropt/algorithms/sms_emoa/struct.SmsEmoa.html
+[Hype]: https://docs.rs/heuropt/latest/heuropt/algorithms/hype/struct.Hype.html
+[PesaII]: https://docs.rs/heuropt/latest/heuropt/algorithms/pesa2/struct.PesaII.html
+[EpsilonMoea]: https://docs.rs/heuropt/latest/heuropt/algorithms/epsilon_moea/struct.EpsilonMoea.html
+[AgeMoea]: https://docs.rs/heuropt/latest/heuropt/algorithms/age_moea/struct.AgeMoea.html
+[Knea]: https://docs.rs/heuropt/latest/heuropt/algorithms/knea/struct.Knea.html
+[Grea]: https://docs.rs/heuropt/latest/heuropt/algorithms/grea/struct.Grea.html
+[Rvea]: https://docs.rs/heuropt/latest/heuropt/algorithms/rvea/struct.Rvea.html
+[DifferentialEvolution]: https://docs.rs/heuropt/latest/heuropt/algorithms/differential_evolution/struct.DifferentialEvolution.html
+[GeneticAlgorithm]: https://docs.rs/heuropt/latest/heuropt/algorithms/genetic_algorithm/struct.GeneticAlgorithm.html
+[Paes]: https://docs.rs/heuropt/latest/heuropt/algorithms/paes/struct.Paes.html
+[SimulatedAnnealing]: https://docs.rs/heuropt/latest/heuropt/algorithms/simulated_annealing/struct.SimulatedAnnealing.html
+[HillClimber]: https://docs.rs/heuropt/latest/heuropt/algorithms/hill_climber/struct.HillClimber.html
+[OnePlusOneEs]: https://docs.rs/heuropt/latest/heuropt/algorithms/one_plus_one_es/struct.OnePlusOneEs.html

docs/book/src/cookbook/permutation.md

@@ -2,11 +2,11 @@
 
 When your decision is "an ordering" — visiting cities, scheduling
 jobs, routing — the natural representation is `Vec<usize>` and the
-specialized algorithm is [`AntColonyTsp`]. Generic alternatives are
-[`SimulatedAnnealing`] + [`SwapMutation`] for any permutation, and
-[`TabuSearch`] when you have a custom neighbor function.
+specialized algorithm is [Ant Colony][AntColonyTsp]. Generic alternatives are
+[Simulated Annealing][SimulatedAnnealing] + [`SwapMutation`] for any permutation, and
+[Tabu Search][TabuSearch] when you have a custom neighbor function.
 
-## TSP with `AntColonyTsp`
+## TSP with Ant Colony
 
 ```rust,no_run
 use heuropt::prelude::*;
@@ -138,10 +138,10 @@ println!("schedule: {:?}", best.decision);
 `SwapMutation` swaps two random indices in the permutation —
 preserves the "every element appears once" invariant for free.
 
-## Custom neighborhoods: `TabuSearch`
+## Custom neighborhoods: Tabu Search
 
 When swap isn't the right move set (e.g., 2-opt for TSP, insert /
-shift for scheduling), use [`TabuSearch`] with your own neighbor
+shift for scheduling), use [Tabu Search][TabuSearch] with your own neighbor
 function.
 
 ```rust,ignore
@@ -161,7 +161,7 @@ let neighbors = |x: &Vec<usize>, _rng: &mut Rng| -> Vec<Vec<usize>> {
 // Pass `neighbors` to TabuSearch::new(...).
 ```
 
-[`AntColonyTsp`]: https://docs.rs/heuropt/latest/heuropt/algorithms/ant_colony_tsp/struct.AntColonyTsp.html
-[`SimulatedAnnealing`]: https://docs.rs/heuropt/latest/heuropt/algorithms/simulated_annealing/struct.SimulatedAnnealing.html
+[AntColonyTsp]: https://docs.rs/heuropt/latest/heuropt/algorithms/ant_colony_tsp/struct.AntColonyTsp.html
+[SimulatedAnnealing]: https://docs.rs/heuropt/latest/heuropt/algorithms/simulated_annealing/struct.SimulatedAnnealing.html
 [`SwapMutation`]: https://docs.rs/heuropt/latest/heuropt/operators/struct.SwapMutation.html
-[`TabuSearch`]: https://docs.rs/heuropt/latest/heuropt/algorithms/tabu_search/struct.TabuSearch.html
+[TabuSearch]: https://docs.rs/heuropt/latest/heuropt/algorithms/tabu_search/struct.TabuSearch.html

docs/book/src/defining-problems.md

@@ -87,8 +87,8 @@ impl Problem for Zdt1 {
 ```
 
 For multi-objective problems, pick a Pareto-aware optimizer:
-[`Nsga2`] is the canonical default; [`Mopso`] often wins on
-smooth-front 2-objective problems; [`Ibea`] often wins on
+[NSGA-II][Nsga2] is the canonical default; [MOPSO][Mopso] often wins on
+smooth-front 2-objective problems; [IBEA][Ibea] often wins on
 disconnected fronts. See [choosing-an-algorithm](./choosing-an-algorithm.md).
 
 ## Maximizing instead of minimizing
@@ -166,8 +166,8 @@ impl Problem for OneMax {
 }
 ```
 
-For `Vec<bool>` problems, [`Umda`] is a parameter-free EDA;
-[`GeneticAlgorithm`] with [`BitFlipMutation`] is the GA route.
+For `Vec<bool>` problems, [UMDA][Umda] is a parameter-free EDA;
+[GA][GeneticAlgorithm] with [`BitFlipMutation`] is the GA route.
 
 ### Permutations (`Vec<usize>`)
 
@@ -191,9 +191,9 @@ impl Problem for Tsp {
 }
 ```
 
-For permutations, [`AntColonyTsp`] specializes on TSP-style problems;
-[`TabuSearch`] takes a user-supplied neighbor function for arbitrary
-discrete neighborhoods; [`SimulatedAnnealing`] with [`SwapMutation`]
+For permutations, [Ant Colony][AntColonyTsp] specializes on TSP-style problems;
+[Tabu Search][TabuSearch] takes a user-supplied neighbor function for arbitrary
+discrete neighborhoods; [Simulated Annealing][SimulatedAnnealing] with [`SwapMutation`]
 is the simplest baseline.
 
 ### Custom decision types
@@ -232,13 +232,13 @@ through the decision tree.
 [`Evaluation`]: https://docs.rs/heuropt/latest/heuropt/core/evaluation/struct.Evaluation.html
 [`Evaluation::new`]: https://docs.rs/heuropt/latest/heuropt/core/evaluation/struct.Evaluation.html#method.new
 [`Evaluation::constrained`]: https://docs.rs/heuropt/latest/heuropt/core/evaluation/struct.Evaluation.html#method.constrained
-[`Nsga2`]: https://docs.rs/heuropt/latest/heuropt/algorithms/nsga2/struct.Nsga2.html
-[`Mopso`]: https://docs.rs/heuropt/latest/heuropt/algorithms/mopso/struct.Mopso.html
-[`Ibea`]: https://docs.rs/heuropt/latest/heuropt/algorithms/ibea/struct.Ibea.html
-[`Umda`]: https://docs.rs/heuropt/latest/heuropt/algorithms/umda/struct.Umda.html
-[`GeneticAlgorithm`]: https://docs.rs/heuropt/latest/heuropt/algorithms/genetic_algorithm/struct.GeneticAlgorithm.html
+[Nsga2]: https://docs.rs/heuropt/latest/heuropt/algorithms/nsga2/struct.Nsga2.html
+[Mopso]: https://docs.rs/heuropt/latest/heuropt/algorithms/mopso/struct.Mopso.html
+[Ibea]: https://docs.rs/heuropt/latest/heuropt/algorithms/ibea/struct.Ibea.html
+[Umda]: https://docs.rs/heuropt/latest/heuropt/algorithms/umda/struct.Umda.html
+[GeneticAlgorithm]: https://docs.rs/heuropt/latest/heuropt/algorithms/genetic_algorithm/struct.GeneticAlgorithm.html
 [`BitFlipMutation`]: https://docs.rs/heuropt/latest/heuropt/operators/struct.BitFlipMutation.html
-[`AntColonyTsp`]: https://docs.rs/heuropt/latest/heuropt/algorithms/ant_colony_tsp/struct.AntColonyTsp.html
-[`TabuSearch`]: https://docs.rs/heuropt/latest/heuropt/algorithms/tabu_search/struct.TabuSearch.html
-[`SimulatedAnnealing`]: https://docs.rs/heuropt/latest/heuropt/algorithms/simulated_annealing/struct.SimulatedAnnealing.html
+[AntColonyTsp]: https://docs.rs/heuropt/latest/heuropt/algorithms/ant_colony_tsp/struct.AntColonyTsp.html
+[TabuSearch]: https://docs.rs/heuropt/latest/heuropt/algorithms/tabu_search/struct.TabuSearch.html
+[SimulatedAnnealing]: https://docs.rs/heuropt/latest/heuropt/algorithms/simulated_annealing/struct.SimulatedAnnealing.html
 [`SwapMutation`]: https://docs.rs/heuropt/latest/heuropt/operators/struct.SwapMutation.html

docs/book/src/getting-started.md

@@ -6,19 +6,21 @@ The shortest path from a fresh project to a working optimizer.
 
 ```toml
 [dependencies]
-heuropt = "0.8"
+heuropt = "0.10"
 ```
 
 The default feature set is small. Optional features:
 
 - `parallel` — rayon-backed parallel population evaluation.
 - `serde` — `Serialize` / `Deserialize` derives on the core data
-  types.
+  types, plus the `heuropt::explorer` JSON export module for the
+  [heuropt-explorer](https://swaits.github.io/heuropt-explorer/)
+  webapp.
 - `async` — `AsyncProblem` trait + per-algorithm `run_async` for
   IO-bound evaluations.
 
 ```toml
-heuropt = { version = "0.8", features = ["parallel"] }
+heuropt = { version = "0.10", features = ["parallel"] }
 ```
 
 ## 2. Define a problem and run an optimizer
@@ -31,7 +33,7 @@ how to score one decision.
 We'll fit a straight line to a handful of `(x, y)` data points by
 finding the slope and intercept that minimize the sum of squared
 errors — same objective as least-squares regression. For a smooth
-single-objective continuous problem like this, [`CmaEs`] is a strong
+single-objective continuous problem like this, [CMA-ES][CmaEs] is a strong
 default.
 
 ```rust,no_run
@@ -138,7 +140,7 @@ problems this clean in well under that budget.
 ## 4. What just happened
 
 - [`Problem`] is the **what** you're optimizing.
-- [`CmaEs`] (or any other optimizer) is the **how**.
+- [CMA-ES][CmaEs] (or any other optimizer) is the **how**.
 - [`CmaEsConfig`] is a plain public-field struct: there are no
   builders, no chained setters, just public fields you set
   directly.
@@ -163,5 +165,5 @@ problems this clean in well under that budget.
 [`Problem`]: https://docs.rs/heuropt/latest/heuropt/core/problem/trait.Problem.html
 [`Optimizer::run`]: https://docs.rs/heuropt/latest/heuropt/traits/trait.Optimizer.html
 [`OptimizationResult`]: https://docs.rs/heuropt/latest/heuropt/core/result/struct.OptimizationResult.html
-[`CmaEs`]: https://docs.rs/heuropt/latest/heuropt/algorithms/cma_es/struct.CmaEs.html
+[CmaEs]: https://docs.rs/heuropt/latest/heuropt/algorithms/cma_es/struct.CmaEs.html
 [`CmaEsConfig`]: https://docs.rs/heuropt/latest/heuropt/algorithms/cma_es/struct.CmaEsConfig.html

docs/book/src/introduction.md

@@ -28,7 +28,7 @@ hyperopt, optuna, DEAP). heuropt's design priorities:
 
 1. **Approachable code.** No trait objects in the public API. No
    GATs, HRTBs, generic-RNG plumbing. A junior Rust engineer should
-   be able to read `RandomSearch` and write a new optimizer by
+   be able to read Random Search and write a new optimizer by
    implementing only the `Optimizer<P>` trait.
 2. **One concrete RNG type.** Seeded determinism is a property tested
    across the crate; identical inputs always produce identical
@@ -44,20 +44,18 @@ hyperopt, optuna, DEAP). heuropt's design priorities:
 
 ## What's in the box
 
-heuropt v0.8 ships **33 algorithms** spanning:
+heuropt v0.10 ships **33 algorithms** spanning:
 
-- Single-objective continuous: `RandomSearch`, `HillClimber`,
-  `OnePlusOneEs`, `SimulatedAnnealing`, `GeneticAlgorithm`,
-  `ParticleSwarm`, `DifferentialEvolution`, `Tlbo`, `CmaEs`,
-  `IpopCmaEs`, `SeparableNes`, `NelderMead`.
-- Single-objective other types: `Umda` (binary), `TabuSearch`
-  (any), `AntColonyTsp` (permutation).
-- Multi-objective (2–3): `Paes`, `Nsga2`, `Spea2`, `Mopso`, `Ibea`,
-  `SmsEmoa`, `HypE`, `EpsilonMoea`, `PesaII`, `AgeMoea`, `Knea`,
-  `Moead`.
-- Many-objective (4+): `Nsga3`, `Rvea`, `Grea`.
-- Sample-efficient / multi-fidelity: `BayesianOpt`, `Tpe`,
-  `Hyperband`.
+- Single-objective continuous: Random Search, Hill Climber,
+  (1+1)-ES, Simulated Annealing, GA, PSO, Differential Evolution,
+  TLBO, CMA-ES, IPOP-CMA-ES, sNES, Nelder-Mead.
+- Single-objective other types: UMDA (binary), Tabu Search (any),
+  Ant Colony (permutation).
+- Multi-objective (2–3): PAES, NSGA-II, SPEA2, MOPSO, IBEA,
+  SMS-EMOA, HypE, ε-MOEA, PESA-II, AGE-MOEA, KnEA, MOEA/D.
+- Many-objective (4+): NSGA-III, RVEA, GrEA.
+- Sample-efficient / multi-fidelity: Bayesian Optimization, TPE,
+  Hyperband.
 
 Plus the operators (SBX, PolynomialMutation, BoundedGaussianMutation,
 LevyMutation, BitFlipMutation, SwapMutation, ClampToBounds,

docs/book/src/migration.md

@@ -3,6 +3,70 @@
 Per-release notes for upgrading between heuropt versions. Skip the
 sections that don't apply to your starting version.
 
+## To 0.10
+
+### From 0.9.x
+
+**Almost additive.** Bumping `heuropt = "0.10"` recompiles
+without touching most code. The one breaking change is the value
+returned by `AlgorithmInfo::name()`:
+
+| Before (`0.9`) | After (`0.10`) |
+|---|---|
+| `"Nsga2"` | `"NSGA-II"` |
+| `"Nsga3"` | `"NSGA-III"` |
+| `"Cmaes"` | `"CMA-ES"` |
+| `"Mopso"` | `"MOPSO"` |
+| `"Moead"` | `"MOEA/D"` |
+| `"EpsilonMoea"` | `"ε-MOEA"` |
+| (and 27 more) | … |
+
+If you pattern-matched on those strings (e.g. for branching
+display logic), update to the new canonical strings. They now
+match the literature and will be stable going forward.
+
+What's new and additive:
+
+- `AlgorithmInfo::full_name(&self) -> &'static str` — academic
+  long form (`"Non-dominated Sorting Genetic Algorithm II"`).
+  Defaults to `name()` for algorithms whose long and short
+  forms coincide.
+- `ExplorerExport`'s `RunMeta` gained `algorithm_full_name:
+  Option<String>`. Schema version stays at **1** (the new field
+  is `#[serde(default)]`); display tools can use the long form
+  as a hover tooltip on the short name.
+
+## To 0.9
+
+### From 0.8.x
+
+**Additive only.** Bumping `heuropt = "0.9"` works for all 0.8.x
+code untouched. The new surfaces ship behind the existing `serde`
+feature.
+
+What's new:
+
+- `heuropt::explorer` module (gated on `serde`) — turns an
+  `OptimizationResult` into a self-describing JSON file that the
+  [heuropt-explorer](https://swaits.github.io/heuropt-explorer/)
+  webapp can load. See the
+  [Explore your results](./cookbook/explorer.md) recipe.
+- `Objective` gained optional `label` and `unit` fields with
+  fluent builders `.with_label("…")` / `.with_unit("…")`. Existing
+  `Objective::minimize("…")` / `Objective::maximize("…")` are
+  unchanged. The serde representation is forward- and backward-
+  compatible (new fields are `#[serde(default)]`).
+- `Problem` trait gained a default-empty
+  `fn decision_schema(&self) -> Vec<DecisionVariable>` method.
+  Existing impls compile untouched; override it to provide pretty
+  names / labels / units / bounds for the explorer.
+- `heuropt::traits::AlgorithmInfo` — every built-in algorithm
+  exposes its short canonical name (`"Nsga3"`, …) and its seed.
+  Used by the explorer JSON export.
+
+If you don't want any of this, no migration needed — just bump
+the version.
+
 ## To 0.8
 
 ### From 0.5.x
@@ -101,9 +165,9 @@ from v0.3 are still numerically accurate but will run faster.
 
 ### From 0.2.x
 
-**Additive only.** New algorithms (`BayesianOpt`, `Tpe`,
-`OnePlusOneEs`, `IpopCmaEs`, `SeparableNes`, `NelderMead`,
-`Hyperband`), new operators (`LevyMutation`, `ClampToBounds`,
+**Additive only.** New algorithms (Bayesian Optimization, TPE,
+(1+1)-ES, IPOP-CMA-ES, sNES, Nelder-Mead,
+Hyperband), new operators (`LevyMutation`, `ClampToBounds`,
 `ProjectToSimplex`), new traits (`PartialProblem`, `Repair<D>`).
 
 `CmaEsConfig` gained an `initial_mean: Option<Vec<f64>>` field;
@@ -114,8 +178,8 @@ existing call sites need a `.. CmaEsConfig { initial_mean: None,
 
 ### From 0.1.x
 
-**Additive.** New algorithms across the catalog (HillClimber, SA,
-GA, PSO, CMA-ES, TabuSearch, AntColonyTsp, Umda, TLBO, MOPSO, IBEA,
+**Additive.** New algorithms across the catalog (Hill Climber, SA,
+GA, PSO, CMA-ES, Tabu Search, Ant Colony, UMDA, TLBO, MOPSO, IBEA,
 SMS-EMOA, HypE, RVEA, PESA-II, ε-MOEA, AGE-MOEA, GrEA, KnEA), new
 operators (`SimulatedBinaryCrossover`, `PolynomialMutation`,
 `CompositeVariation`, `BoundedGaussianMutation`), and the

docs/book/src/stability.md

@@ -18,10 +18,10 @@ versions — use them at your own risk.
 
 While we are pre-1.0:
 
-- **Minor bumps (`0.8 → 0.9`) may break the public API.** The
+- **Minor bumps (`0.10 → 0.11`) may break the public API.** The
   CHANGELOG calls out everything that changed, and a **migration
   guide** in this book documents the move.
-- **Patch bumps (`0.8.0 → 0.8.1`) only contain bug fixes,
+- **Patch bumps (`0.10.0 → 0.10.1`) only contain bug fixes,
   performance improvements, and additive non-breaking features.**
   No deprecations, no removals.
 
@@ -61,7 +61,7 @@ optimizations has been bit-identical against the v0.3.0 reference.
 
 ## MSRV (minimum supported Rust version)
 
-heuropt's MSRV is **1.85** as of v0.8. This is tested in CI against
+heuropt's MSRV is **1.85** as of v0.10. This is tested in CI against
 every PR.
 
 MSRV bumps are treated as patch-bump-eligible (they don't break the

src/algorithms/age_moea.rs

@@ -426,7 +426,10 @@ fn estimate_p(front_indices: &[usize], translated: &[Vec<f64>], m: usize) -> f64
 
 impl<I, V> crate::traits::AlgorithmInfo for AgeMoea<I, V> {
     fn name(&self) -> &'static str {
-        "AgeMoea"
+        "AGE-MOEA"
+    }
+    fn full_name(&self) -> &'static str {
+        "Adaptive Geometry Estimation Multi-Objective Evolutionary Algorithm"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/ant_colony_tsp.rs

@@ -431,7 +431,10 @@ fn better_than_so(
 
 impl crate::traits::AlgorithmInfo for AntColonyTsp {
     fn name(&self) -> &'static str {
-        "AntColonyTsp"
+        "Ant Colony"
+    }
+    fn full_name(&self) -> &'static str {
+        "Ant Colony System for TSP"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/bayesian_opt.rs

@@ -542,7 +542,10 @@ impl BayesianOpt {
 
 impl crate::traits::AlgorithmInfo for BayesianOpt {
     fn name(&self) -> &'static str {
-        "BayesianOpt"
+        "Bayesian Optimization"
+    }
+    fn full_name(&self) -> &'static str {
+        "Gaussian Process Bayesian Optimization with Expected Improvement"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/cma_es.rs

@@ -630,7 +630,10 @@ fn better_than_so(
 
 impl crate::traits::AlgorithmInfo for CmaEs {
     fn name(&self) -> &'static str {
-        "CmaEs"
+        "CMA-ES"
+    }
+    fn full_name(&self) -> &'static str {
+        "Covariance Matrix Adaptation Evolution Strategy"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/differential_evolution.rs

@@ -305,7 +305,10 @@ fn pick_three_distinct(
 
 impl crate::traits::AlgorithmInfo for DifferentialEvolution {
     fn name(&self) -> &'static str {
-        "DifferentialEvolution"
+        "DE"
+    }
+    fn full_name(&self) -> &'static str {
+        "Differential Evolution"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/epsilon_moea.rs

@@ -398,7 +398,10 @@ fn box_dominates(a: &[i64], b: &[i64]) -> bool {
 
 impl<I, V> crate::traits::AlgorithmInfo for EpsilonMoea<I, V> {
     fn name(&self) -> &'static str {
-        "EpsilonMoea"
+        "ε-MOEA"
+    }
+    fn full_name(&self) -> &'static str {
+        "ε-dominance Multi-Objective Evolutionary Algorithm"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/genetic_algorithm.rs

@@ -319,7 +319,10 @@ fn compare_for_fitness<D>(
 
 impl<I, V> crate::traits::AlgorithmInfo for GeneticAlgorithm<I, V> {
     fn name(&self) -> &'static str {
-        "GeneticAlgorithm"
+        "GA"
+    }
+    fn full_name(&self) -> &'static str {
+        "Genetic Algorithm"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/grea.rs

@@ -336,7 +336,10 @@ fn environmental_selection<D: Clone>(
 
 impl<I, V> crate::traits::AlgorithmInfo for Grea<I, V> {
     fn name(&self) -> &'static str {
-        "Grea"
+        "GrEA"
+    }
+    fn full_name(&self) -> &'static str {
+        "Grid-based Evolutionary Algorithm"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/hill_climber.rs

@@ -226,7 +226,10 @@ impl<I, V> HillClimber<I, V> {
 
 impl<I, V> crate::traits::AlgorithmInfo for HillClimber<I, V> {
     fn name(&self) -> &'static str {
-        "HillClimber"
+        "Hill Climber"
+    }
+    fn full_name(&self) -> &'static str {
+        "Hill Climbing"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/hype.rs

@@ -461,7 +461,10 @@ fn binary_tournament(fitness: &[f64], rng: &mut Rng) -> usize {
 
 impl<I, V> crate::traits::AlgorithmInfo for Hype<I, V> {
     fn name(&self) -> &'static str {
-        "Hype"
+        "HypE"
+    }
+    fn full_name(&self) -> &'static str {
+        "Hypervolume Estimation Algorithm"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/hyperband.rs

@@ -337,6 +337,9 @@ where
     fn name(&self) -> &'static str {
         "Hyperband"
     }
+    fn full_name(&self) -> &'static str {
+        "Hyperband multi-fidelity bandit search"
+    }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)
     }

src/algorithms/ibea.rs

@@ -387,7 +387,10 @@ fn binary_tournament(fitness: &[f64], rng: &mut Rng) -> usize {
 
 impl<I, V> crate::traits::AlgorithmInfo for Ibea<I, V> {
     fn name(&self) -> &'static str {
-        "Ibea"
+        "IBEA"
+    }
+    fn full_name(&self) -> &'static str {
+        "Indicator-Based Evolutionary Algorithm"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/ipop_cma_es.rs

@@ -289,7 +289,10 @@ fn better(a: &Evaluation, b: &Evaluation, direction: Direction) -> bool {
 
 impl crate::traits::AlgorithmInfo for IpopCmaEs {
     fn name(&self) -> &'static str {
-        "IpopCmaEs"
+        "IPOP-CMA-ES"
+    }
+    fn full_name(&self) -> &'static str {
+        "Increasing-Population CMA-ES with Restarts"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/knea.rs

@@ -330,7 +330,10 @@ fn perpendicular_distance(point: &[f64], extremes: &[usize], oriented: &[Vec<f64
 
 impl<I, V> crate::traits::AlgorithmInfo for Knea<I, V> {
     fn name(&self) -> &'static str {
-        "Knea"
+        "KnEA"
+    }
+    fn full_name(&self) -> &'static str {
+        "Knee point-driven Evolutionary Algorithm"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/moead.rs

@@ -365,7 +365,10 @@ fn weight_distance(a: &[f64], b: &[f64]) -> f64 {
 
 impl<I, V> crate::traits::AlgorithmInfo for Moead<I, V> {
     fn name(&self) -> &'static str {
-        "Moead"
+        "MOEA/D"
+    }
+    fn full_name(&self) -> &'static str {
+        "Multi-Objective Evolutionary Algorithm based on Decomposition"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/mopso.rs

@@ -332,7 +332,10 @@ impl Mopso {
 
 impl crate::traits::AlgorithmInfo for Mopso {
     fn name(&self) -> &'static str {
-        "Mopso"
+        "MOPSO"
+    }
+    fn full_name(&self) -> &'static str {
+        "Multi-Objective Particle Swarm Optimization"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/nelder_mead.rs

@@ -452,7 +452,10 @@ impl NelderMead {
 
 impl crate::traits::AlgorithmInfo for NelderMead {
     fn name(&self) -> &'static str {
-        "NelderMead"
+        "Nelder-Mead"
+    }
+    fn full_name(&self) -> &'static str {
+        "Nelder-Mead simplex direct search"
     }
 }
 

src/algorithms/nsga2.rs

@@ -366,7 +366,10 @@ fn binary_tournament<D>(entries: &[Nsga2Entry<D>], rng: &mut Rng) -> usize {
 
 impl<I, V> crate::traits::AlgorithmInfo for Nsga2<I, V> {
     fn name(&self) -> &'static str {
-        "Nsga2"
+        "NSGA-II"
+    }
+    fn full_name(&self) -> &'static str {
+        "Non-dominated Sorting Genetic Algorithm II"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/nsga3.rs

@@ -544,7 +544,10 @@ fn associate(
 
 impl<I, V> crate::traits::AlgorithmInfo for Nsga3<I, V> {
     fn name(&self) -> &'static str {
-        "Nsga3"
+        "NSGA-III"
+    }
+    fn full_name(&self) -> &'static str {
+        "Non-dominated Sorting Genetic Algorithm III"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/one_plus_one_es.rs

@@ -287,7 +287,10 @@ impl OnePlusOneEs {
 
 impl crate::traits::AlgorithmInfo for OnePlusOneEs {
     fn name(&self) -> &'static str {
-        "OnePlusOneEs"
+        "(1+1)-ES"
+    }
+    fn full_name(&self) -> &'static str {
+        "(1+1) Evolution Strategy with one-fifth success rule"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/paes.rs

@@ -244,7 +244,10 @@ impl<I, V> Paes<I, V> {
 
 impl<I, V> crate::traits::AlgorithmInfo for Paes<I, V> {
     fn name(&self) -> &'static str {
-        "Paes"
+        "PAES"
+    }
+    fn full_name(&self) -> &'static str {
+        "Pareto Archived Evolution Strategy"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/particle_swarm.rs

@@ -359,7 +359,10 @@ fn best_index(values: &[f64], direction: Direction) -> usize {
 
 impl crate::traits::AlgorithmInfo for ParticleSwarm {
     fn name(&self) -> &'static str {
-        "ParticleSwarm"
+        "PSO"
+    }
+    fn full_name(&self) -> &'static str {
+        "Particle Swarm Optimization"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/pesa2.rs

@@ -411,7 +411,10 @@ fn truncate_by_grid<D: Clone>(archive: &mut ParetoArchive<D>, max_size: usize, d
 
 impl<I, V> crate::traits::AlgorithmInfo for PesaII<I, V> {
     fn name(&self) -> &'static str {
-        "PesaII"
+        "PESA-II"
+    }
+    fn full_name(&self) -> &'static str {
+        "Pareto Envelope-based Selection Algorithm II"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/random_search.rs

@@ -160,7 +160,7 @@ impl<I> RandomSearch<I> {
 
 impl<I> crate::traits::AlgorithmInfo for RandomSearch<I> {
     fn name(&self) -> &'static str {
-        "RandomSearch"
+        "Random Search"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/rvea.rs

@@ -468,7 +468,10 @@ fn smallest_neighbor_angle(references: &[Vec<f64>]) -> f64 {
 
 impl<I, V> crate::traits::AlgorithmInfo for Rvea<I, V> {
     fn name(&self) -> &'static str {
-        "Rvea"
+        "RVEA"
+    }
+    fn full_name(&self) -> &'static str {
+        "Reference Vector-guided Evolutionary Algorithm"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/simulated_annealing.rs

@@ -335,7 +335,7 @@ impl<I, V> SimulatedAnnealing<I, V> {
 
 impl<I, V> crate::traits::AlgorithmInfo for SimulatedAnnealing<I, V> {
     fn name(&self) -> &'static str {
-        "SimulatedAnnealing"
+        "Simulated Annealing"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/sms_emoa.rs

@@ -291,7 +291,10 @@ fn pick_drop_index<D>(
 
 impl<I, V> crate::traits::AlgorithmInfo for SmsEmoa<I, V> {
     fn name(&self) -> &'static str {
-        "SmsEmoa"
+        "SMS-EMOA"
+    }
+    fn full_name(&self) -> &'static str {
+        "S-Metric Selection Evolutionary Multi-Objective Algorithm"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/snes.rs

@@ -391,7 +391,10 @@ fn better(a: &Evaluation, b: &Evaluation, direction: Direction) -> bool {
 
 impl crate::traits::AlgorithmInfo for SeparableNes {
     fn name(&self) -> &'static str {
-        "SeparableNes"
+        "sNES"
+    }
+    fn full_name(&self) -> &'static str {
+        "Separable Natural Evolution Strategy"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/spea2.rs

@@ -505,7 +505,10 @@ fn binary_tournament(fitness: &[f64], rng: &mut Rng) -> usize {
 
 impl<I, V> crate::traits::AlgorithmInfo for Spea2<I, V> {
     fn name(&self) -> &'static str {
-        "Spea2"
+        "SPEA2"
+    }
+    fn full_name(&self) -> &'static str {
+        "Strength Pareto Evolutionary Algorithm 2"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/tabu_search.rs

@@ -338,7 +338,7 @@ where
     N: FnMut(&D, &mut Rng) -> Vec<D>,
 {
     fn name(&self) -> &'static str {
-        "TabuSearch"
+        "Tabu Search"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/tlbo.rs

@@ -327,7 +327,10 @@ fn better(a: &Evaluation, b: &Evaluation, direction: Direction) -> bool {
 
 impl crate::traits::AlgorithmInfo for Tlbo {
     fn name(&self) -> &'static str {
-        "Tlbo"
+        "TLBO"
+    }
+    fn full_name(&self) -> &'static str {
+        "Teaching-Learning-Based Optimization"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/tpe.rs

@@ -481,7 +481,10 @@ impl Tpe {
 
 impl crate::traits::AlgorithmInfo for Tpe {
     fn name(&self) -> &'static str {
-        "Tpe"
+        "TPE"
+    }
+    fn full_name(&self) -> &'static str {
+        "Tree-structured Parzen Estimator"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/algorithms/umda.rs

@@ -354,7 +354,10 @@ fn better_than_so(
 
 impl crate::traits::AlgorithmInfo for Umda {
     fn name(&self) -> &'static str {
-        "Umda"
+        "UMDA"
+    }
+    fn full_name(&self) -> &'static str {
+        "Univariate Marginal Distribution Algorithm"
     }
     fn seed(&self) -> Option<u64> {
         Some(self.config.seed)

src/explorer/mod.rs

@@ -101,10 +101,16 @@ pub struct RunMeta {
     /// Optional human-readable problem name (e.g. `"Pick a car"`).
     #[serde(default, skip_serializing_if = "Option::is_none")]
     pub problem_name: Option<String>,
-    /// Canonical algorithm name (e.g. `"Nsga3"`). Pulled from
-    /// [`AlgorithmInfo::name`] when an algorithm is provided.
+    /// Canonical short algorithm name (e.g. `"NSGA-III"`). Pulled
+    /// from [`AlgorithmInfo::name`] when an algorithm is provided.
     #[serde(default, skip_serializing_if = "Option::is_none")]
     pub algorithm: Option<String>,
+    /// Academic long form (e.g. `"Non-dominated Sorting Genetic
+    /// Algorithm III"`). Pulled from [`AlgorithmInfo::full_name`]
+    /// when an algorithm is provided. Display tools render this
+    /// as a tooltip / aria-label on the short name.
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub algorithm_full_name: Option<String>,
     /// Seed driving this run, if applicable. Pulled from
     /// [`AlgorithmInfo::seed`].
     #[serde(default, skip_serializing_if = "Option::is_none")]
@@ -216,10 +222,12 @@ impl ExplorerExport {
         }
     }
 
-    /// Populate `algorithm` and `seed` from anything implementing
-    /// [`AlgorithmInfo`] — every built-in algorithm does.
+    /// Populate `algorithm`, `algorithm_full_name`, and `seed`
+    /// from anything implementing [`AlgorithmInfo`] — every
+    /// built-in algorithm does.
     pub fn with_algorithm_info<A: AlgorithmInfo>(mut self, algorithm: &A) -> Self {
         self.run.algorithm = Some(algorithm.name().to_owned());
+        self.run.algorithm_full_name = Some(algorithm.full_name().to_owned());
         self.run.seed = algorithm.seed();
         self
     }
@@ -412,6 +420,9 @@ mod tests {
         fn name(&self) -> &'static str {
             "DummyAlgo"
         }
+        fn full_name(&self) -> &'static str {
+            "Dummy Test Algorithm"
+        }
         fn seed(&self) -> Option<u64> {
             Some(123)
         }
@@ -513,6 +524,10 @@ mod tests {
         let result = make_result(vec![vec![0.0, 1.0]], |d| d.to_vec());
         let export = ExplorerExport::from_result(&problem, &result).with_algorithm_info(&DummyAlgo);
         assert_eq!(export.run.algorithm.as_deref(), Some("DummyAlgo"));
+        assert_eq!(
+            export.run.algorithm_full_name.as_deref(),
+            Some("Dummy Test Algorithm"),
+        );
         assert_eq!(export.run.seed, Some(123));
     }
 

src/traits/algorithm_info.rs

@@ -1,25 +1,45 @@
-//! Lightweight metadata about an algorithm — its short canonical name
-//! and the seed driving the current run.
+//! Lightweight metadata about an algorithm — its canonical short
+//! name, an academic long name, and the seed driving the current
+//! run.
 //!
 //! `AlgorithmInfo` is separate from [`Optimizer<P>`](super::Optimizer)
-//! so multi-fidelity algorithms (which use `PartialProblem` instead of
-//! `Problem`) can implement it uniformly. Every built-in algorithm in
-//! `heuropt` implements `AlgorithmInfo`; the explorer JSON export reads
-//! these methods to populate `algorithm` and `seed` fields in the
-//! exported run metadata.
+//! so multi-fidelity algorithms (which use `PartialProblem` instead
+//! of `Problem`) can implement it uniformly. Every built-in
+//! algorithm in `heuropt` implements `AlgorithmInfo`; the explorer
+//! JSON export reads these methods to populate the `algorithm` and
+//! `algorithm_full_name` fields in the exported run metadata.
 
-/// Algorithm metadata used by tooling such as the explorer JSON export.
+/// Algorithm metadata used by tooling such as the explorer JSON
+/// export.
 ///
-/// Implementors return a short canonical name like `"Nsga3"` or
-/// `"DifferentialEvolution"`, and the seed driving their current run
-/// when applicable.
+/// Implementors return:
+/// - **`name`** — the canonical short display name as it appears
+///   in the literature: `"NSGA-II"`, `"MOEA/D"`, `"ε-MOEA"`,
+///   `"CMA-ES"`. *Not* the Rust type name.
+/// - **`full_name`** — the academic long form, e.g.
+///   `"Non-dominated Sorting Genetic Algorithm II"`. Defaults to
+///   `name()` when not overridden, which is the right answer for
+///   algorithms whose short name *is* their full name (Random
+///   Search, Hill Climber, Tabu Search, …).
+/// - **`seed`** — the deterministic seed driving this run, when
+///   the algorithm uses one. Defaults to `None`.
 pub trait AlgorithmInfo {
-    /// Short, canonical algorithm name — e.g. `"Nsga3"`,
-    /// `"DifferentialEvolution"`, `"BayesianOpt"`.
+    /// Canonical short algorithm name — e.g. `"NSGA-II"`,
+    /// `"DE"`, `"CMA-ES"`. This is the form that should appear
+    /// in tables, plot legends, and exported JSON metadata.
     fn name(&self) -> &'static str;
 
-    /// The deterministic seed driving this run, if the algorithm uses
-    /// one. Default: `None`. Built-in algorithms return
+    /// Academic long name, expanded — e.g.
+    /// `"Non-dominated Sorting Genetic Algorithm II"`. Defaults
+    /// to `name()` for algorithms whose short and long forms
+    /// coincide (Random Search, Hill Climber, Tabu Search,
+    /// Hyperband, …).
+    fn full_name(&self) -> &'static str {
+        self.name()
+    }
+
+    /// The deterministic seed driving this run, if the algorithm
+    /// uses one. Default: `None`. Built-in algorithms return
     /// `Some(self.config.seed)`.
     fn seed(&self) -> Option<u64> {
         None