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.

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